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Sakellaridi, L.

Publications and source records attributed to Sakellaridi, L..

3 recordsLinked to original sources

Substantial Deceleration of Adaptation of HIV-1 Within 1,500 Generations in an Experimental Evolution: A Genomic Perspective

Numerous experimental evolution studies have suggested that adaptation rate of microbial populations evolving in stable environments decline over time. Despite the generality of this phenomenon across different domains of life, the timing and magnitude of decline in adaptation can vary greatly based on the idiosyncrasies of the biological system. To investigate the characteristics of adaptation deceleration in a fast-evolving virus, we propagated HIV-1 in two human T-cell lines (MT-2 and MT-4) for approximately 4.8 years and tracked its genome evolution through next-generation sequencing. The curated sequencing data covering the whole-genome can be accessed and explored via LTEEviz, an interactive web application. Time-resolved sequencing data uncovered that despite constant fixation rate of 0.085 (MT-2) and 0.042 (MT-4) mutations per generation, the fixation kinetics of adaptive mutations changed considerably over time. The rate of fixation of adaptive parallel mutations decreased by 44% per 300 generations, while their conferred fitness gain decreased by 27% (MT-2) and 18% (MT-4) per every added adaptive mutation in their genetic background. The early and substantial deceleration of adaptation in our HIV-1 populations can, at least in part, be explained by diminishing gains of adaptive mutations. Furthermore, we identified genomic patterns consistent with a hard selective sweep that occurred in one population later in the experiment. Together, our results confirm that HIV-1 genomic evolution is characterized by a swift and substantial deceleration of adaptation, while also revealing that episodes of positive selection can occur beyond the initial adaptive phase.

evolutionary biology↗

Identifying Modulators of Cellular Responses by Heterogeneity-sequencing

The response of individual cells to drug treatment, virus infections or other molecular stimuli is highly heterogeneous and depends on the cells initial state. Library preparation for single-cell transcriptomics is destructive, precluding a direct comparison between the initial state and the stimulus outcome. Consequently, current methods are restricted to identifying correlative associations rather than resolving causal drivers of heterogeneous outcomes. We developed Heterogeneity-seq, which combines single-cell RNA-seq with metabolic RNA labeling (scSLAM-seq) and double machine learning to overcome this limitation. By leveraging simultaneous measurements of unlabeled and labeled RNA in individual cells, Heterogeneity-seq uncovers the transition from pre-stimulated cell states to distinct stimulation outcomes across thousands of cells. These links enable the identification of factors that causally govern heterogeneous cellular responses. We used Heterogeneity-seq to identify both known and novel genes that drive responses to drug treatment, as well as pro- and antiviral host factors governing cytomegalovirus infection.

bioinformatics↗

grandR: a comprehensive package for nucleotide conversion sequencing data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. We here present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

bioinformatics↗