Biology subjects

Warchal, S.

Publications and source records attributed to Warchal, S..

4 recordsLinked to original sources

bioRxiv2024

The essential host genome for Cryptosporidium intracellular survival exposes metabolic dependencies that can be leveraged for treatment

Mapping how pathogens interact with their host cells can reveal unexpected pathogen and host cell biology, paving the way for new treatments. Cryptosporidium is an intracellular parasite of intestinal epithelial cells, and a leading cause of diarrheal death and disease in infants worldwide. Despite this, very little is known about the cell biology of infection of this eukaryotic pathogen. Here, we designed and implemented a unique microscopy-based arrayed CRISPR-Cas9 screen to interrogate the effects of the loss of every protein-coding human gene on a Cryptosporidium infection. As the experimental readout is image-based, we extracted multiple phenotypic features of infection, including parasite growth, progression of the parasite to its sexual life stage, and recruitment of host actin to pedestals beneath the parasite vacuole. Using this dataset, we discovered a tipping point in the host cholesterol biosynthesis pathway that controls Cryptosporidium infection. Parasite growth can either be inhibited or promoted by the intermediary metabolite squalene. A build-up of squalene in epithelial cells creates a reducing environment, with more reduced host glutathione available for uptake by the parasite. Because Cryptosporidium has lost the ability to synthesise glutathione, this uptake from the host cell is required for growth and progression through its life cycle. We demonstrate that this dependency can be leveraged for treatment with the abandoned drug lapaquistat, an inhibitor of host squalene synthase that has efficacy against Cryptosporidium in vitro and in vivo.

cell biology↗

bioRxiv2024

Spherical Phenotype Clustering

Phenotypic screening experiments produce many microscope images of cells under diverse perturbations, with biologically significant responses often subtle or difficult to identify visually. A central challenge is to extract image representations that distinguish activity from controls and group phenotypically similar perturbations. In this work we propose new adaptations of contrastive loss functions that incorporate experimental metadata as learned class vectors, and a geometrically inspired variant, called SPC, where class vectors are confined to the unit sphere and updated only by attractive terms (allowing more overlap of phenotypically similar classes). The approach is tested on two popular benchmarking datasets, BBBC021 and RxRx3-core; and we also evaluate performance on uncurated screens of HaCaT cells to gauge effectiveness in a realistic use-case scenario. We find we outperform prior methods across the three datasets and on a wide array of metrics measuring phenotype grouping, biological recall, drug-target interaction and mechanism-of-action inference. We also show we maintain this improved performance compared to models over 10x larger in parameter count, and that SPC can be used as an effective fine-tuning technique. The method is easy to implement and is well suited to settings with limited data or compute resources.

bioinformatics↗

bioRxiv2021

Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains

We examined the immunogenicity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant B.1.1.7 that arose in the United Kingdom and spread globally. Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa B.1.351 variant, than of the infecting variant. The drop in cross-reactivity was more pronounced following B.1.1.7 than parental strain infection, indicating asymmetric heterotypic immunity induced by SARS-CoV-2 variants.

immunology↗

bioRxiv2020

Visualising uncertainty in brightfield to fluorescent image inference with BFNet

Predicting fluorescently labelled cellular structures from brightfield images is a recent application of convolutional neural networks and has already proven a valuable tool in biological imaging studies. These methods reduce the need for time-consuming manual annotations for supervised datasets, potentially reduce the costs in high-throughput imaging screens, and open up a number of potentially novel analyses. However as with any prediction there can be sources of error and uncertainty. Here we present BFNet, a method to visualise the uncertainty in predicted images by applying Monte Carlo dropout during inference and calculating per-pixel variance as an uncertainty map. Our method demonstrates the ability to highlight regions of an image where prediction is difficult or impossible due to imaging artefacts such as occlusions or out-of-focus images, as well as more general uncertainty when a trained model is applied to new data from different imaging of experimental settings. We have provided a python implementation of the method which is available at github.com/swarchal/bfnet.

bioinformatics↗