Biology subjects

Smircich, P.

Publications and source records attributed to Smircich, P..

8 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

Beyond the transcript: chromatin implications in trans-splicing in Trypanosomatids

Trypanosoma cruzi, Trypanosoma brucei and Leishmania major, usually known as TriTryps, are the causal agents of animal and human sickness, and are characterized by having complex life cycles, alternating between a mammalian host and an insect vector. Their genes are organized in long transcriptional units that give rise to polycistronic transcripts which maturate into mRNA by a process known as trans-splicing. Among those genes, an important subset is composed of multicopy genes, which play crucial roles in host invasion and immune evasion. Here, we predicted the most likely trans-splicing acceptor sites (TASs) for TriTryps and found that the average chromatin organization is very similar among them with a mild nucleosome depletion at the TASs, and the same layout is observed in most of the genome. A detailed examination of the nucleosome landscapes resulting from different levels of chromatin digestion in T. brucei shows that an MNase-sensitive complex is protecting the TASs, and it is at least partly composed of histones. Additionally, comparative analysis for single and multi-copy genes in T. cruzi revealed a differential chromatin structure at the TASs suggesting a novel mechanism to guarantee the fidelity of trans-splicing in trypanosomatids.

genomics↗

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↗

bioRxiv2024

Expanding kinetoplastid genome annotation through protein structure comparison

Kinetoplastids belong to the supergroup Discobids, an early divergent eukaryotic clade. Although the amount of genomic information on these parasites has grown substantially, assigning gene functions through traditional sequence-based homology methods remains challenging. Recently, significant advancements have been made in in silico protein structure prediction and algorithms for rapid and precise large-scale protein structure comparisons. In this work, we developed a protein structure-based homology search pipeline (ASC, Annotation by Structural Comparisons) and applied it to annotate all kinetoplastid proteins available in TriTrypDB. Our pipeline assigned functional annotation to 23,000 hypothetical proteins across all 35 kinetoplastid species in the database. Among these, we identified ubiquitous eukaryotic proteins that had not been previously detected in kinetoplastid genomes. The resulting annotations (KASC, Kinetoplastid Annotation by Structural Comparison) are openly available to the community (kasc.fcien.edu.uy). Author SummaryKinetoplastids are a group of parasites that cause severe diseases in the poorest regions of the world. Despite the increasing amount of genomic information available on these parasites, predicting the function of many of their genes using traditional methods has been difficult. Recently, there have been significant advancements in predicting protein structures and comparing them on a large scale. In this study, we created a new method called ASC (Annotation by Structural Comparisons) to find functions for all the kinetoplastid genes listed in the TriTrypDB database. Our strategy successfully assigned functions to 23,000 proteins in kinetoplastids. Among these, we discovered important proteins found in all eukaryotes that had not been previously identified in kinetoplastids. This information (KASC, Kinetoplastid Annotation by Structural Comparison) is freely available at kasc.fcien.edu.uy.

genomics↗

bioRxiv2023

Impact of N-terminal mutated TcUBP1 knockdown on the transcriptome profiling of epimastigote cells: An RNA-Seq study in Trypanosoma cruzi

During its life cycle, the human pathogen Trypanosoma cruzi must quickly adapt to different environments, in which the variation in the gene expression of the regulatory U-rich RNA-binding protein 1 (TcUBP1) plays a crucial role. We have previously demonstrated that the overexpression of TcUBP1 in insect-dwelling epimastigotes orchestrates an RNA regulon to promote differentiation to infective forms. In an attempt to generate TcUBP1 knockout parasites by using CRISPR-Cas9 technology, in the present study, we obtained a variant transcript that encodes a protein with 95% overall identity and a modified N-terminal sequence. The expression of this mutant protein, named TcUBP1mut, was notably reduced compared to that of the endogenous form found in normal cells. TcUBP1mut-knockdown epimastigotes exhibited normal growth and differentiation into infective metacyclic trypomastigotes and were capable of infecting mammalian cells. We analyzed the RNA-Seq expression profiles of these parasites and identified 276 up- and 426 downregulated genes with respect to the wildtype control sample. RNA-Seq comparison across distinct developmental stages revealed that the transcriptomic profile of these TcUBP1mut-knockdown epimastigotes significantly differs not only from that of epimastigotes in the stationary phase but also from the gene expression landscape characteristic of infective forms. This is both contrary to and consistent with the results of our recent study involving TcUBP1-overexpressing cells. Together, our findings demonstrate that the genes exhibiting opposite changes under overexpression and knockdown conditions unveil key mRNA targets regulated by TcUBP1. These mostly encompass transcripts that encode for trypomastigote-specific surface glycoproteins and ribosomal proteins, supporting a role for TcUBP1 in determining the molecular characteristics of the infective stage.

genomics↗

bioRxiv2023

Translational control by Trypanosoma brucei DRBD18 contributes to the maintenance of the procyclic state

Trypanosoma brucei occupies distinct niches throughout its life cycle, within both the mammalian and tsetse fly hosts. The immunological and biochemical complexity and variability of each of these environments require a reshaping of the protein landscape of the parasite both to evade surveillance and face changing metabolic demands. Whereas most well-studied organisms rely on transcriptional control as the main regulator of gene expression, post-transcriptional control mechanisms are particularly important in T. brucei, and these are often mediated by RNA-binding proteins. DRBD18 is a T. brucei RNA-binding protein that interacts with ribosomal proteins and translation factors. Here, we tested a role for DRBD18 in translational control. We show that DRBD18 depletion by RNA interference leads to altered polysomal profiles with a specific depletion of heavy polysomes. Ribosome profiling analysis reveals that 101 transcripts change in translational efficiency (TE) upon DRBD18 depletion: 41 exhibit decreased TE and 60 exhibit increased TE. A further 66 transcripts are buffered, i.e. changes in transcript abundance are compensated by changes in TE such that the total translational output is expected not to change. Proteomic analysis validates these data. In DRBD18-depleted cells, a cohort of transcripts that codes for procyclic form-specific proteins is translationally repressed while, conversely, transcripts that code for bloodstream form- and metacyclic form-specific proteins are translationally enhanced. These data suggest that DRBD18 contributes to the maintenance of the procyclic state through both positive and negative translational control of specific mRNAs.

microbiology↗

bioRxiv2022

Nanopore quality score resolution can be reduced with little effect on downstream analysis

We investigate the effect of quality score information loss on downstream analysis from nanopore sequencing FASTQ files. We polished denovo assemblies for a mock microbial community and a human genome, and we called variants on a human genome. We repeated these experiments using various pipelines, under various coverage level scenarios, and various quality score quantizers. In all cases we found that the quantization of quality scores cause little difference on (or even improves) the results obtained with the original (non-quantized) data. This suggests that the precision that is currently used for nanopore quality scores is unnecessarily high, and motivates the use of lossy compression algorithms for this kind of data. Moreover, we show that even a non-specialized compressor, like gzip, yields large storage space savings after quantization of quality scores.

bioinformatics↗

bioRxiv2021

RENANO: a REference-based compressor for NANOpore FASTQ files

Nanopore sequencing technologies are rapidly gaining popularity, in part, due to the massive amounts of genomic data they produce in short periods of time (up to 8.5 TB of data in less than 72 hours). In order to reduce the costs of transmission and storage, efficient compression methods for this type of data are needed. Unlike short-read technologies, nanopore sequencing generates long noisy reads of variable length. In this note we introduce RENANO, a reference-based lossless FASTQ data compressor, specifically tailored to compress FASTQ files generated with nanopore sequencing technologies. RENANO builds on the recent compressor ENANO, which is the current state of the art. RENANO focuses on improving the compression of the base call sequence portion of the FASTQ file, leaving the other parts of ENANO intact. Two novel reference-based compression algorithms are introduced, contemplating different scenarios: in the first scenario, a reference genome is available without cost to both the compressor and the decompressor; in the second, the reference genome is available only on the compressor side, and a compacted version of the reference is transmitted to the decompressor as part of the compressed file. To evaluate the proposed algorithms, we compare RENANO against ENANO on several publicly available nanopore datasets. In the first scenario considered, RENANO improves the base call sequences compression of ENANO by 39.8%, on average, over all the datasets. As for total compression (including the other parts of the FASTQ file), the average improvement is 12.7%. In the second scenario considered, the base call compression improvements of RENANO over ENANO range from 15.2% to 49.0%, depending on the coverage of the compressed dataset, while in terms of total size, the improvements range from 5.1% to 16.5%. Implementations of the proposed algorithms are freely available for download at https://github.com/guilledufort/RENANO.

bioinformatics↗