While Entamoeba histolytica remains a globally important pathogen, it is dramatically understudied. The tractability of E. histolytica has historically been limited, which is largely due to challenging features of its genome. To enable forward genetics, we constructed and validated the first genome-wide E. histolytica RNAi knockdown mutant library. This library allows for Illumina deep sequencing analysis for quantitative identification of mutants that are enriched or depleted after selection. We developed a novel analysis pipeline to precisely define and quantify gene fragments. We used the library to perform the first RNAi screen in E. histolytica and identified slow growth (SG) mutants. Among genes targeted in SG mutants, many had annotated functions consistent with roles in cellular growth or metabolic pathways. Some targeted genes were annotated as hypothetical or lacked annotated domains, supporting the power of forward genetics in uncovering functional information that cannot be gleaned from databases. While the localization of neither of the proteins targeted in SG1 nor SG2 mutants could be predicted by sequence analysis, we showed experimentally that SG1 localized to the cytoplasm and cell surface, while SG2 localized to the cytoplasm. Overexpression of SG1 led to increased growth, while expression of a truncation mutant did not lead to increased growth, and thus aided in defining functional domains in this protein. Finally, in addition to establishing forward genetics, we uncovered new details of the unusual E. histolytica RNAi pathway. These studies dramatically improve the tractability of E. histolytica and open up the possibility of applying genetics to improve understanding of this important pathogen.
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http://dx.doi.org/10.1371/journal.ppat.1010088 | DOI Listing |
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View Article and Find Full Text PDFMach Learn Appl
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McGill University Department of Biostatistics, 805 rue Sherbrooke O, Montréal, H3A 0B9, Quebec, Canada.
In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures.
View Article and Find Full Text PDFMicrob Cell Fact
January 2025
Department of Genetics and Biotechnology, Ivan Franko National University of Lviv, Hrushevskoho st. 4, Rm. 102, Lviv, 79005, Ukraine.
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View Article and Find Full Text PDFInt J Mol Sci
January 2025
Department of Experimental Medicine, Tor Vergata University of Rome, 00133 Rome, Italy.
MicroRNA (miR)-126 is frequently downregulated in malignancies, including breast cancer (BC). Despite its tumor-suppressive role, the mechanisms underlying miR-126 deregulation in BC remain elusive. Through silencing experiments, we identified Early B Cell Factor 1 (EBF1), ETS Proto-Oncogene 2 (ETS2), and Krüppel-Like Factor 2 (KLF2) as pivotal regulators of miR-126 expression.
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