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Biology subjects

Paran, Y.

Publications and source records attributed to Paran, Y..

4 recordsLinked to original sources

Diverse patterns of intra-host genetic diversity in chronically infected SARS-CoV-2 patients

In rare individuals with a severely immunocompromised system, chronic infections of SARS-CoV-2 may develop, where the virus replicates in the body for months. Sequencing of some chronic infections has uncovered dramatic adaptive evolution and fixation of mutations reminiscent of lineage-defining mutations of variants of concern (VOCs). This has led to the prevailing hypothesis that VOCs emerged from chronic infections. To examine the mutation dynamics and intra-host genomic diversity of SARS-CoV-2 during chronic infections, we focused on a cohort of nine immunocompromised individuals with chronic infections and performed longitudinal sequencing of viral genomes. We show that sequencing errors may cause erroneous inference of high genetic diversity, and to overcome this we used duplicate sequencing across patients and time-points, allowing us to distinguish errors from low frequency mutations. We further find recurrent low frequency mutations that we flag as most likely sequencing errors. This stringent approach allowed us to reliably infer low frequency mutations and their dynamics across time. We inferred a synonymous divergence rate of the virus of [~]2x10-6 mutations/base/day, consistent with the SARS-CoV-2 mutation rate estimated in tissue culture. The rate of non-synonymous divergence varied widely among the different patients. We highlight two patients with opposing patterns: in one patient the rate of divergence was zero, yet this patient harbored multiple presumably defective viruses at low frequencies throughout the infection. Another patient exhibited dramatic adaptive evolution, including clonal competition. Overall, our results suggest that the emergence of highly divergent variants from chronic infections is likely a very rare event and this emphasizes the need to better understand the conditions that allow such emergence events.

evolutionary biology↗

A high-throughput zebrafish screen identifies novel candidate treatments for Kaposiform Lymphangiomatosis (KLA)

Kaposiform Lymphangiomatosis (KLA) is a rare, aggressive, and incurable disease caused by a somatic activating NRAS mutation (p.Q61R) in lymphatic endothelial cells (LECs). The development of new therapeutic avenues is hampered by the lack of animal models faithfully replicating the clinical manifestations of KLA. Here, we established a novel zebrafish model of KLA by driving conditional expression of the human NRAS mutation in venous and lymphatic ECs. We find that mutant embryos recapitulated clinical features of KLA, including pericardial edema and a dilated thoracic duct, and that the phenotypes were reverted by Trametinib, a MEK inhibitor used for KLA treatment. We further leverage this model in combination with an AI-based high-throughput drug screening platform to search for small compounds selectively reverting the mutant phenotypes and identify Cabozantinib, an FDA-approved tyrosine kinase inhibitor, and GSK690693, a competitive pan-Akt kinase inhibitor, as leading hits. Finally, we test these drugs in cultured cells derived from KLA patient and demonstrate their ability to normalize LEC sprouting and block NRAS downstream pathways, underscoring the potential of GSK690693 and Cabozantinib as potential KLA treatments. Overall, our novel zebrafish model provides a valuable tool for research into the etiology of KLA and for identifying new therapeutic avenues.

developmental biology↗

A versatile automated high-throughput drug screening platform for zebrafish embryos

Zebrafish provide a unique opportunity for drug screening in living animals, with the fast developing, transparent embryos allowing for relatively high throughput, microscopy-based screens. However, the limited availability of rapid, flexible imaging and analysis platforms has limited the use of zebrafish in drug screens. We have developed a easy-to-use, customisable automated screening procedure suitable for high-throughput phenotype-based screens of live zebrafish. We utilised the WiScan(R) Hermes High Content Imaging System to rapidly acquire brightfield and fluorescent images of embryos, and the WiSoft(R) Athena Zebrafish Application for analysis, which harnesses an Artificial Intelligence-driven algorithm to automatically detect fish in brightfield images, identify anatomical structures, partition the animal into regions, and exclusively select the desired side-oriented fish. Our initial validation combined structural analysis with fluorescence images to enumerate GFP-tagged haematopoietic stem and progenitor cells in the tails of embryos, which correlated with manual counts. We further validated this system to assess the effects of genetic mutations and x-ray irradiation in high content using a wide range of assays. Further, we performed simultaneous analysis of multiple cell types using dual fluorophores in high throughput. In summary, we demonstrate a broadly applicable and rapidly customisable platform for high content screening in zebrafish.

cell biology↗

Community Standards for Open Cell Migration Data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited due to the diversity of experimental protocols and non-standardised output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardised data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardisation efforts by the Cell Migration Standardisation Organization, CMSO, an open community-driven organisation to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools, and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

bioinformatics↗