Biology subjects

da Silva, C.

Publications and source records attributed to da Silva, C..

3 recordsLinked to original sources

bioRxiv2022

Trim69 is a microtubule regulator that acts as a pantropic viral inhibitor

To identify novel cellular modulators of HIV-1 infection in IFN-stimulated myeloid cells, we have carried out a screen that combines functional and evolutionary analyses in THP-1-PMA cells that led us to the Tripartite Motif Protein 69 (Trim69), a poorly studied member of the Trim family of innate immunity regulators. Trim69 inhibits HIV-1, primate lentiviruses and the negative and positive-strand RNA viruses VSV and SARS-CoV2, overall indicating it is a broad-spectrum antiviral factor. Trim69 binds directly to microtubules and its antiviral activity is intimately linked to its ability to promote the accumulation of stable MTs, a specialized subset of microtubules. By analyzing the behavior of primary blood cells, we provide evidence that a program of MT stabilization is commonly observed in response to IFN-I in cells of the myeloid lineage and Trim69 is the key factor behind this program. Overall, our study identifies Trim69 as the first antiviral innate defense factor that regulates the properties of microtubules to limit viral spread, highlighting the possibility that the cytoskeleton may be a novel unappreciated fighting ground in the host-pathogen interactions that underlie viral infections.

microbiology↗

bioRxiv2020

RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction

Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied 7 machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, ANN and DL) through 15 model organisms from different evolutionary branches. Then, we created a stand-alone and web server tool (RNAmining) to distinguish coding and noncoding sequences, selecting the algorithm with the best performance (XGBoost). Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their tri-nucleotides counts analysed and we performed a normalization by the sequence length. Thus, in total we built 180 models. All the machine learning algorithms tests were performed using 10-folds cross-validation and we selected the algorithm with the best results (XGBoost) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and Transdecoder) and our results outperformed them, opening opportunities for the development of RNAmining, which is freely available at https://rnamining.integrativebioinformatics.me/.

bioinformatics↗

bioRxiv2017

Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment.

Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

cell biology↗