Biology subjects

Trapotsi, M.-A.

Publications and source records attributed to Trapotsi, M.-A..

6 recordsLinked to original sources

bioRxiv2024

FlaHMM: unistrand flamenco-like piRNA cluster prediction in Drosophila species using hidden Markov models

PIWI-interacting RNAs are a class of small non-coding RNAs that are essential for transposon control in animal gonads. In Drosophila ovarian somatic cells, piRNAs are transcribed from large genomic regions called piRNA clusters, which are enriched for transposon fragments and acts as a memory of past invasions. Despite being widely present across Drosophila species, somatic piRNA clusters are notoriously difficult to identify and study due to their lack of sequence conservation and limited synteny. Current identification methods rely either on extensive manual curation or availability of high-throughput small RNA-seq data, limiting large-scale comparative studies. We now present FlaHMM, a hidden Markov model developed to automate genomic annotation of flamenco-like unistrand piRNA clusters in Drosophila species without the need of experimental data beyond a genome assembly. FlaHMM uses transposable element content across 5 or 10 kb bins calculated from genome sequence alone and is thus able to detect candidate piRNA clusters without the need to obtain flies and experimentally perform small RNA sequencing. We show that FlaHMM performs on par with piRNA-guided or manual methods, and thus provides a scalable and efficient approach to piRNA cluster annotation in new genome assemblies. FlaHMM is freely available at https://github.com/Hannon-lab/FlaHMM under an MIT licence.

bioinformatics↗

bioRxiv2024

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Paintings ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

cell biology↗

bioRxiv2023

Unistrand piRNA clusters are an evolutionary conserved mechanism to suppress endogenous retroviruses across the Drosophila genus

The PIWI-interacting RNA (piRNA) pathway prevents endogenous genomic parasites, transposable elements, from damaging the genetic material of animal gonadal cells. Specific regions in the genome, called piRNA clusters, are thought to define each species piRNA repertoire and therefore its capacity to recognize and silence specific transposon families. The unistrand cluster flamenco (flam) is essential in the somatic compartment of the Drosophila ovary to restrict Gypsy-family transposons from infecting the neighbouring germ cells. Disruption of flam results in transposon derepression and sterility, yet it remains unknown whether this silencing mechanism is present more widely. Here, we systematically characterised 119 Drosophila species and identify five additional flam-like clusters separated by up to 45 million years. Small RNA-sequencing validated these as bona-fide unistrand piRNA clusters expressed in somatic cells of the ovary, where they selectively target transposons of the Gypsy family. Together, our study provides compelling evidence of a widely conserved transposon silencing mechanism that co-evolved with virus-like Gypsy-family transposons.

molecular biology↗

bioRxiv2022

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models by Leveraging Similarity to Training Data

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY O_FLOATNOFigure:C_FLOATNO For TOC Only C_FIG_DISPLAY

bioinformatics↗

bioRxiv2022

Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature

PROTACs (PROteolysis TArgeting Chimeras) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances in targeted protein degradation technology have been remarkable with several molecules moving into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step towards delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design. HighlightsO_LIMorphological profiling detects various PROTACs phenotypic signatures C_LIO_LIPhenotypic signatures can be attributed to diverse biological responses C_LIO_LIChemical clustering from phenotypic signatures separates on drug selection C_LIO_LITrained in-silico machine learning models to predict PROTACs mitochondrial toxicity C_LI

pharmacology and toxicology↗

bioRxiv2022

Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/475326v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@f29341org.highwire.dtl.DTLVardef@1a1970borg.highwire.dtl.DTLVardef@1c3581dorg.highwire.dtl.DTLVardef@1ce00d4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

bioinformatics↗