Biology subjects

Novak, D.

Publications and source records attributed to Novak, D..

4 recordsLinked to original sources

bioRxiv2024

A Laboratory-Adapted and a Clinical Isolate of Dengue Virus Serotype 4 Differently Impact Aedes aegypti Life-History Traits Relevant to Vectorial Capacity

1Dengue virus cases are on the rise globally, and strategies to reduce new infections by controlling its primary vector, the mosquito Aedes aegypti, represent a promising biotechnological approach. However, the interaction between virus serotypes and genotypes with Aedes aegypti is poorly characterized at the molecular level, as well as in terms of life-history traits related to vector capacity and mosquito disease tolerance. Here, we infected Aedes aegypti with two philogenetically distant strains of Dengue virus serotype 4 genotype II: a laboratory-adapted strain, DENV4 - TVP/360, and a recent clinical strain isolated from southern Brazil, DENV4 - LRV 13/422. These strains, which exhibit 26 amino acid differences in their sequences, have been shown in to produce distinct immune phenotypes in vertebrate cells. Here, we assessed various life-history traits of Aedes aegypti, including mortality, fecundity, fertility, and induced flight capacity, as well as vector competence-related parameters such as infection intensity and prevalence, following exposure to different viral concentrations. We found that each viral strain differentially affected mosquito traits. While neither strain significantly reduced mosquito lifespan, Aedes aegypti infection prevalence was influenced by the initial dose of DENV4 - TVP/360. This laboratory-adapted strain also enhanced mosquito induced-flight capacity at early (24 hours post-infection - DPI) and late (21 DPI) time points. On the other hand, the recent clinical isolate, DENV4 - LRV 13/422, specifically reduced Aedes aegypti fertility. A better understanding of how different arbovirus strains influence mosquito life-history traits connected to disease spread will be critical in public health efforts to mitigate arbovirus outbreaks that are focused on the mosquito vector. 2 Highlights- The DENV4 strains TVP/360 and LRV 13/422 have 26 amino acid differences in their sequences. - Aedes aegypti infection prevalence is influenced by the initial dose of DENV4 - TVP/360 strain but not DENV4 - LRV 13/422. - Fertility is specifically reduced by DENV4 - LRV 13/422. - Induced-flight activity is enhanced at 1 and 21 days post-infection with DENV4 - TVP/360 strain.

microbiology↗

bioRxiv2024

Tviblindi algorithm identifies branching developmental trajectories of human B cell development

Detailed knowledge of the human B-cell development is crucial for proper interpretation of inborn errors of immunity and for malignant diseases. It is of interest to understand the kinetics of protein expression changes during the B cell development, but also to properly interpret the major and possibly alternative developmental trajectories. We have investigated human bone marrow and peripheral blood samples from healthy individuals with the aim to describe all B-cell developmental trajectories across the two tissues. We validated a 30-parameter mass cytometry panel and demonstrated the utility of "vaevictis" visualization of B-cell developmental stages. We used our recently developed trajectory inference tool "tviblindi" to exhaustively describe all trajectories leading to all developmental ends discovered in the data. Focusing on Natural Effector B cells, we demonstrated the dynamics of expression of nuclear factors (PAX-5, TdT, Ki-67, Bcl-2), cytokine and chemokine receptors (CD127, CXCR4, CXCR5) in relation to the canonical B-cell developmental stage markers (CD34, CD10, sIgM, IgD, CD20, CD27). Lastly, we performed analysis of the expression changes related to developmental branching points (Natural Effector versus Switched Memory B cells, marked by up-regulation of CD73). In conclusion, we developed, validated and presented a comprehensive set of tools for investigation of B-cell development.

immunology↗

bioRxiv2023

A framework for quantifiable local and global structure preservation in single-cell dimensionality reduction

The ability to explore high-dimensional single-cell transcriptomics data efficiently is crucial in many biological studies. Dimensionality reduction techniques have therefore emerged as a basic building block of analytical workflows. They generate low-dimensional embeddings that capture important structures in the data, and are often used in discovery, quality control, and downstream analysis. However, the trustworthiness of current methods and the rigour of popular evaluation criteria are limited. We tackle this in an empirical study of structure-preserving data embeddings, delivering two tools. First, we introduce ViScore: a robust scoring framework that improves both unsupervised and supervised quality metrics, with emphasis on scalability and fairness. Second, we introduce ViVAE : a deep learning model that achieves better multi-scale structure preservation and is equipped with new tools for interpretability. We demonstrate the potential of these contributions to advance the trustworthiness of single-cell dimensionality reduction in a quantitative comparison and focused case studies.

bioinformatics↗

bioRxiv2023

Deconstructing Complexity: A Computational Topology Approach to Trajectory Inference in the Human Thymus with tviblindi

Understanding complex, organ-level single-cell datasets represents a formidable interdisciplinary challenge. This study aims to describe developmental trajectories of thymocytes and mature T cells. We developed tviblindi, a trajectory inference algorithm that integrates several autonomous modules - pseudotime inference, random walk simulations, real-time topological classification using persistent homology, and autoencoder-based 2D visualization using the vaevictis algorithm. This integration facilitates interactive exploration of developmental trajectories, revealing not only the canonical CD4 and CD8 development but also offering insights into checkpoints such as TCR{beta} selection and positive/negative selection. Furthermore, tviblindi allowed us to thoroughly characterize thymic regulatory T cells, tracing their development passed the negative selection stage to mature thymic regulatory T cells. At the very end of the developmental trajectory we discovered a previously undescribed subpopulation of thymic regulatory T cells. Experimentally, we confirmed its extensive proliferation history and an immunophenotype characteristic of activated and recirculating cells. tviblindi represents a new class of methods that is complementary to fully automated trajectory inference tools. It offers a semi-automated tool that leverages features derived from data in an unbiased and mathematically rigorous manner. These features include pseudotime, homology classes, and appropriate low-dimensional representations. These features can be integrated with expert knowledge to formulate hypotheses regarding the underlying dynamics, tailored to the specific trajectory or biological process under investigation.

immunology↗