Biology subjects

Oteo, J. A.

Publications and source records attributed to Oteo, J. A..

4 recordsLinked to original sources

bioRxiv2024

Quantifying defective and wild-type viruses from high-throughput RNA sequencing

Defective viral genomes (DVGs) are variants of the wild-type (wt) virus that lack the ability to complete an infectious cycle independently. However, in the presence of their parental (helper) wt virus, DVGs can interfere with the replication, encapsidation, and spread of functional genomes, acting as a significant selective force in viral evolution. DVGs also affect the hosts immune responses and are linked to chronic infections and milder symptoms. Thus, identifying and characterizing DVGs is crucial for understanding infection prognosis. Quantifying DVGs is challenging due to their inability to sustain themselves, which makes it difficult to distinguish them from the helper virus, especially using high-throughput RNA sequencing (RNA-seq). Accurate quantification is essential for understanding their interactions with their helper virus. We present a method to simultaneously estimate the abundances of DVGs and wt genomes within a sample by identifying genomic regions with significant deviations from the expected sequencing depth. Our approach involves reconstructing the depth profile through a linear system of equations, which provides an estimate of the number of wt and DVG genomes of each type. Until now, in silico methods have only estimated the DVG-to-wt ratio for localized genomic regions. This is the first method that simultaneously estimates the proportions of wt and DVGs across RNA sequencing of the whole genome. Availability and implementationThe MO_SCPLOWATLABC_SCPLOW code and the synthetic datasets are freely available at https://github.com/jmusan/wtDVGquantific.

bioinformatics↗

bioRxiv2024

System and transcript dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2)

Statistical laws arise in many complex systems and can be explored to gain insights into their structure and behavior. Here, we investigate the dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2) at the system and individual gene levels; and demonstrate that the statistical frameworks used here are robust in spite of the technical noise associated with single-cell RNA sequencing (scRNA-seq) data. A biphasic fit to Taylors power law was observed, and it is likely associated with the larger sampling noise inherent to the measure of less expressed genes. The type of the distribution of the system, as assessed by Taylors parameters, varies along the course of infection in a cell type-dependent manner, but also sampling noise had a significant influence on Taylors parameters. At the individual gene level, we found that genes that displayed signals of punctual rank stability and/or long-range dependence behavior, as measured by Hurst exponents, were associated with translation, cellular respiration, apoptosis, protein-folding, virus processes, and immune response. Author summaryViruses replicate within susceptible cells by exploiting the cellular machinery. Consequently, cells initiate defenses against the virus and signal other cells, notably immune cells. This ongoing battle prompts significant alterations in the cells gene expression patterns throughout the infection process. In this study, we apply statistical principles from complex systems theory to analyze gene expression data from individual cells infected with SARS-CoV-2. Our research aims to elucidate how viral infection impacts cells at both systemic and individual gene levels. Our primary findings are twofold: (i) the virus influences the distribution of gene transcripts over the course of infection, varying depending on cell type. (ii) As the infection progresses, numerous genes associated with critical cellular functions and immunity exhibit signs of punctual instability and/or autocorrelation, indicating their response to viral infection at various stages of the process.

systems biology↗

bioRxiv2023

The geometry of admixture in population genetics: the blessing of dimensionality

We present a geometry-based interpretation of the f-statistics framework, commonly used to determine phylogenetic relationships from genetic data. The focus is on the determination of the mixing coefficients in population admixture events subject to post-admixture drift. The interpretation takes advantage of the high dimension of the dataset and analyzes the problem as a dimensional reduction issue. We show that it is possible to think of the f-statistics technique as an implicit transformation of the genetic data from a phase space into a subspace where the mapped data structure is more similar to the ancestral admixture configuration. The positive effect of the map can be explicitly assessed. The overarching geometric framework provides slightly more general formulas than the f-formalism by using a different rationale as a starting point. Explicitly addressed are two- and three-way admixtures. The mixture proportions are provided by suitable linear fits in two or three dimensions that can be easily visualized. The developments and findings are illustrated with numerical simulations from real world datasets.

evolutionary biology↗

bioRxiv2022

Impact of HIV infection and integrase strand transfer inhibitors-based treatment on gut virome

Viruses are the most abundant components of the microbiome in human beings with a significant impact on health and disease. However, the impact of human immunodeficiency virus (HIV) infection on gut virome has been scarcely analyzed. On the other hand, several studies suggested that not all antiretrovirals for treating HIV infection exert similar effects on the gut bacteriome, being the integrase strand transfers inhibitors (INSTIs) --first-choice treatment of naive HIV-infected patients nowadays-- those associated with a healthier gut. Thus, the aim of this study was to evaluate the effects of HIV infection and INSTIs in first line of treatment on gut virome composition. To accomplish this objective, 26 non-HIV-infected volunteers, 15 naive HIV-infected patients and 15 INSTIs-treated HIV-infected patients were recruited and gut virome composition was analysed using shotgun sequencing. The results showed that bacteriophages are the most abundant and diverse viruses in the gut independent from the HIV-status and the use of treatment. HIV infection was accompanied by a decrease in phage richness which was reverted after INSTIs-based treatment (p<0.01 naive vs. control Richness index and p<0.05 naive vs. control Fishers alpha index). {beta}-diversity of phages revealed that samples from HIV-infected samples clustered separately from those belonging to the control group (padj<0.01 naive vs. control and padj<0.05 INSTIs vs. control). However, it is worth mentioning that samples coming from INSTIs-treated patients were more grouped than those from naive patients. Differential abundant analysis of phages showed an increase of Caudoviricetes class in the naive group compared to control the group (padj<0.05) and a decrease of Malgrandaviricetes class in the INSTIs-treated group compared to the control group (padj<0.001). Besides, it was observed that INSTIs-based treatment was not able to reverse the increase of lysogenic phages associated with HIV infection (p<0.05 vs. control) or to modify the decrease observed on the relative abundance of Proteobacteria-infecting phages (p<0.05 vs. control). To sum up, our study describes for the first time the impact of HIV and INSTIs on gut virome and demonstrates that INSTIs-based treatments are able to partially restore gut dysbiosis not only at bacterial but also at viral level, which opens several opportunities for new studies focused on microbiota-based therapies. Author summaryThe impact of human immunodeficiency virus (HIV) infection and the effects of integrase strand transfer inhibitors (INSTIs)-based treatments --first-choice treatment of naive HIV-infected patients nowadays-- on gut virome are unknown. In this study, we have confirmed that phages are the most abundant viral component of the human gut virome. Besides, we have described for the first time that INSTIs-based treatments are able to partially restore gut dysbiosis induced by HIV infection not only at bacteria but also at viral level. This fact opens new opportunities for future studies and approaches focused on microbiota-based therapies in the context of HIV infection and treatment.

microbiology↗