Biology subjects

Caetano, T.

Publications and source records attributed to Caetano, T..

5 recordsLinked to original sources

bioRxiv2026

Direct effects on gene transcription revealed by global and CRISPR-targeted oxidative DNA damage

Oxidative stress induces rapid expression changes of specific genes. However, whether these changes are due to direct and local DNA oxidation and whether they depend on the repair machinery is still debated. Here, we show by total RNA-Seq that upon oxidative stress, many genes are either rapidly repressed or activated. The early transcriptional response also includes the activation of many read-through RNAs. Importantly, degradation of the endogenous OGG1 DNA glycosylase showed that the early repression of these genes partially depends on OGG1, therefore demonstrating that these genome expression changes are due to the processing of oxidized DNA. We further demonstrate by ChIPseq against total or S5-phosphorylated RNA polymerase II that transcriptional regulation by oxidative stress involves changes in the recruitment of RNA polymerase II to transcription start sites. Finally, through the use of a chemoptogenetic system allowing the targeting of ROS to chosen genomic loci, we were able to reproduce repression of some genes through the local induction of oxidative DNA damage at their promoters, demonstrating that local DNA oxidation directly affects transcription of genes. Altogether, our results show that local oxidative DNA damage leads to alterations in the activity of specific promoters by a mechanism dependent on repair processes.

genomics↗

bioRxiv2026

A novel class of tiny box C/D-like RNAs reveals hidden complexity in the mouse box C/D RNA repertoire

In recent years, the catalog of mammalian box C/D small nucleolar RNAs (SNORDs) has expanded considerably, with several hundred members now annotated in humans. Despite being a well-characterized family of antisense small noncoding RNAs, the full diversity of mammalian box C/D SNORDs remains incompletely understood. To address this, we combined deep sequencing of Fibrillarin- and Snu13-associated RNAs with systematic mining of a large collection of publicly available small RNA-seq datasets spanning diverse mouse tissues and cell types. This integrative approach uncovered an unexpectedly large repertoire of box C/D-like RNAs, comprising more than 20 000 distinct species. These RNAs display the defining hallmarks of intronic SNORDs but are expressed at low levels and exhibit poor evolutionary conservation. Strikingly, approximately 70% are only 20-50 nucleotides long, making them substantially shorter than canonical SNORDs. We designate this previously unrecognized class of box C/D-like RNAs as tiny SNORDs (tiSNORDs). Functional characterization of a representative 36-nucleotide tiSNORD demonstrates that, despite its reduced size, it retains nucleolar 2'-O-ribose methylation guide activity. The discovery of this previously unrecognized class of box C/D-like RNAs substantially expands the known repertoire of SNORDs and raises fundamental questions regarding their biogenesis, functional potential and evolutionary significance.

genetics↗

bioRxiv2025

Sni445 recruits box C/D snoRNPs snR4 and snR45 to guide ribosomal RNA acetylation by Kre33

Eukaryotic ribosome synthesis is a highly complex, multistep process that is best characterized in the yeast Saccharomyces cerevisiae. It is orchestrated by over 200 ribosome assembly factors and 75 small nucleolar ribonucleoproteins (snoRNPs), which guide site-specific chemical modifications of precursor ribosomal RNA (pre-rRNA). While canonical box C/D snoRNPs direct 2-O-methylation, the atypical box C/D snoRNPs snR4 and snR45 mediate acetylation of 18S rRNA residues C1280 and C1773, respectively, catalyzed by the acetyltransferase Kre33. Here, we identify and characterize Ynl050c/Sni445 as a novel ribosome assembly factor and previously unrecognized auxiliary component of the snR4 and snR45 box C/D snoRNPs. Sni445 associates with snR4 and snR45 in their free form and is required for their stable incorporation into 90S pre-ribosomes. Genetic interactions link Sni445 and the snR4 and snR45 snoRNAs to ribosomal proteins Rps20 (uS10) and Rps14 (uS11), which are positioned near the respective acetylation sites in the 40S subunit. Moreover, Sni445 physically interacts with Kre33 within the 90S pre-ribosome, and its absence abolishes acetylation of C1280 and C1773. Our findings suggest that Sni445 facilitates the recruitment of snR4 and snR45 snoRNPs to 90S particles and might promote their interaction with Kre33, thereby enabling the site-specific acetylation of 18S rRNA by Kre33.

molecular biology↗

bioRxiv2025

rnaends: an R package targeted to study the exact RNA ends at the nucleotide resolution

AO_SCPLOWBSTRACTC_SCPLOW5 and 3 RNA-end sequencing protocols have unlocked new opportunities to study aspects of RNA metabolism such as synthesis, maturation and degradation, by enabling the quantification of exact ends of RNA molecules in vivo. From RNA-Seq data that have been generated with one of the specialized protocols, it is possible to identify transcription start sites (TSS) and/or endoribonucleolytic cleavage sites, and even, in some cases, co-translational 5 to 3 degradation dynamics. Furthermore, post-transcriptional addition of ribonucleotides at the 3 end of RNA can be studied at the nucleotide resolution. While different RNA-end sequencing library protocols exist that have been adapted to a specific organism (prokaryote or eukaryote) or specific biological question, the generated RNA-Seq data are very similar and share common processing steps. Most importantly, the major aspect of RNA-end sequencing is that only the 5 or 3 end mapped location is of interest, contrary to conventional RNA sequencing that considers genomic ranges for gene expression analysis. This translates to a simple representation of the quantitative data as a count matrix of RNA-end location on the reference sequences. This representation seems under-exploited and is, to our knowledge, not available in a generic package focused on the analyses on the exact transcriptome ends. Here, we present the rnaends R package which is dedicated to RNA-end sequencing analysis. It offers functions for raw read pre-processing, RNA-end mapping and quantification, RNA-end count matrix post-processing, and further downstream count matrix analyses such as TSS identification, fast Fourier transform for signal periodic pattern analysis, or differential proportion of RNA-end analysis. The use of rnaends is illustrated here with applications in RNA metabolism studies through selected rnaends workflows on published RNA-end datasets: (i) TSS identification, (ii) ribosome translation speed and co-translational degradation, (iii) post-transcriptional modification analysis and differential proportion analysis.

bioinformatics↗

bioRxiv2018

FALCON: a method to infer metagenomic composition of ancient DNA

The general approaches to detect and quantify metagenomic sample composition are based on the alignment of the reads, according to an existing database containing reference microbial sequences. However, without proper parameterization, these methods are not suitable for ancient DNA. Quantifying somewhat dissimilar sequences by alignment methods is problematic, due to the need of fine-tuned thresholds, considering relaxed edit distances and the consequent increase of computational cost. Additionally, the choice of the thresholds poses the problem of how to quantify similarity without producing overestimated measures. We propose FALCON-meta, a compression-based method to infer metagenomic composition of next-generation sequencing samples. This unsupervised alignment-free method runs efficiently on FASTQ samples. FALCON-meta quickly learns how to give importance to the models that cooperate to predict similarity, incorporating parallelism and flexibility for multiple hardware characteristics. It shows substantial identification capabilities in ancient DNA without overestimation. In one of the examples, we found and authenticated an ancient Pseudomonas bacteria in a Mammoth mitogenome.\n\nFALCON-meta can be accessed at https://github.com/pratas/falcon.

bioinformatics↗