Biology subjects

Pastore, V. P.

Publications and source records attributed to Pastore, V. P..

5 recordsLinked to original sources

bioRxiv2026

Therapeutic vulnerabilities of the drug-resistant fungal meningitis pathogen Cryptococcus neoformans

Treatment options are limited for the priority fungal pathogen Cryptococcus neoformans. We describe CRISPR-insert, a scalable gene inactivation method for global interrogation of drug targets. Genome-wide screens using this methodology combined with machine learning predict 1,297 essential genes under liberal criteria. Unexpectedly absent from this list is FKS1, encoding the target of echinocandin drugs to which C. neoformans is resistant. Contrary to longstanding literature, targeted deletion shows that FKS1 is not essential. Solid-state NMR reveals that fks1{triangleup} mutants lack {beta}-1,3-glucan, arguing against compensation by another synthase. We experimentally test essentiality predictions for genes lacking human orthologs, identifying high-priority drug target candidates. To uncover targets for combination therapies, we establish genome-scale quantitative genetic interaction mapping. We use this method to uncover synthetic lethal dependencies of echinocandin resistance determinants, including the druggable calcineurin and Cdc50-Apt1 pathways. CRISPR-insert illuminates therapeutic vulnerabilities of an invasive fungal pathogen.

microbiology↗

bioRxiv2024

Phenotypic landscape of a fungal meningitis pathogen reveals its unique biology

Cryptococcus neoformans is the most common cause of fungal meningitis and the top-ranked W.H.O. priority fungal pathogen. Only distantly related to model fungi, C. neoformans is also a powerful experimental system for exploring conserved eukaryotic mechanisms lost from specialist model yeast lineages. To decipher its biology globally, we constructed 4328 gene deletions and measured--with exceptional precision--the fitness of each mutant under 141 diverse growth-limiting in vitro conditions and during murine infection. We defined functional modules by clustering genes based on their phenotypic signatures. In-depth studies leveraged these data in two ways. First, we defined and investigated new components of key signaling pathways, which revealed animal-like pathways/components not predicted from studies of model yeasts. Second, we identified environmental adaptation mechanisms repurposed to promote mammalian virulence by C. neoformans, which lacks a known animal reservoir. Our work provides an unprecedented resource for deciphering a deadly human pathogen.

microbiology↗

bioRxiv2022

Phenotype to genotype mapping using supervised and unsupervised learning

The relationship between the genotype, the genetic instructions encoded into a genome, and phenotype, the macroscopic realization of such instructions, remains mostly uncharted. In addition, tools able to uncover the connection between the phenotype with a specific set of responsible genes are still under definition. In this work, we focus on yeast organelles called vacuoles, which are cell membrane compartments that vary size and shape in response to various stimuli, and we develop a framework relating changes of cellular morphology to genetic modification. The method is a combination of convolutional neural network (CNN) and an unsupervised learning pipeline, which employs a deep-learning based segmentation, classification, and anomaly detection algorithm. From the live 3D fluorescence vacuole images, we observe that different genetic mutations generate distinct vacuole phenotypes and that the same mutation might correspond to more than one vacuole morphology. We trained a Unet architecture to segment our cellular images and obtain precise, quantitative information in 2D depth-encoded images. We then used an unsupervised learning approach to cluster the vacuole types and to establish a correlation between genotype and vacuole morphology. Using this procedure, we obtained 4 phenotypic groups. We extracted a set of 131 morphological features from the segmented vacuoles images, reduced to 50 after a tree-based feature selection. We obtained a purity of 85% adopting a Fuzzy K-Means based algorithm on a random subset of 880 images, containing all the detected phenotypic groups. Finally, we trained a CNN on the labels assigned during clustering. The CNN has been used for prediction of a large dataset (6942 images) with high accuracy (80%). Our approach can be applied extensively for live fluorescence image analysis and most importantly can unveil the basic principles relating genotype to vacuole phenotype in yeast cell, which can be thought as a first step for inferring cell designing principles to generate organelles with a specific, desired morphology.

bioinformatics↗

bioRxiv2019

Establishing the baseline for using plankton as biosensor

Plankton is at the bottom of the food chain. Microscopic phytoplankton account for about 50% of all photosynthesis on Earth, corresponding to 50 billion tons of carbon each year, or about 125 billion tonnes of sugar[1]. Plankton is also the food for most species of fish, and therefore it represents the backbone of the aquatic environment. Thus, monitoring plankton is paramount to infer potential dangerous changes to the ecosystem. In this work we use a collection of plankton species extracted from a large dataset of images from the Woods Hole Oceanographic Institution (WHOI), to establish a basic set of morphological features for supporting the use of plankton as a biosensor. Using a perturbation detection approach, we show that it is possible to detect deviation from the average space of features for each species of plankton microorganisms, that we propose could be related to environmental threat or perturbations. Such an approach can open the way for the development of an automatic Artificial Intelligence (AI) based system for using plankton as biosensor.

bioengineering↗

bioRxiv2019

Stereo In-Line Holographic Digital Microscope

Biologists use optical microscopes to study plankton in the lab, but their size, complexity and cost makes widespread deployment of microscopes in lakes and oceans challenging. Monitoring the morphology, behavior and distribution of plankton in situ is essential as they are excellent indicators of marine environment health and provide a majority of Earths oxygen and carbon sequestration. Direct in-line holographic microscopy (DIHM) eliminates many of these obstacles, but image reconstruction is computationally intensive and produces monochromatic images. By using one laser and one white LED, it is possible to obtain the 3D location plankton by triangulation, limiting holographic reconstruction to only the voxels occupied by the plankton, reducing computation by several orders of magnitude. The color information from the white LED assists in the classification of plankton, as phytoplankton contains green-colored chlorophyll. The reconstructed plankton images are rendered in a 3D interactive environment, viewable from a browser, providing the user the experience of observing plankton from inside a drop of water.

cell biology↗