Establishment of quantitative RNAi-based forward genetics inEntamoeba histolyticaand identification of genes required for growth
Authors: Bettadapur Akhila , Hunter Samuel S. , Suleiman Rene L. , Ruyechan Maura C. , Huang Wesley , Barbieri Charles G. , Miller Hannah W. , Tam Tammie S.Y. , Settles Matthew L. , Ralston Katherine S. 
Published: May 2020

WhileEntamoeba histolyticaremains a globally important pathogen, it is dramatically understudied. The tractability ofE. histolyticahas historically been limited, which is largely due to challenging features of its genome. To enable forward genetics, we constructed and validated the first genome-wideE. histolyticaRNAi 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 inE. histolyticaand 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 unusualE. histolyticaRNAi pathway. These studies dramatically improve the tractability ofE. histolyticaand open up the possibility of applying genetics to improve understanding of this important pathogen.

Author Summary

Entamoeba histolyticais a globally important pathogen that is dramatically understudied. One of the major limitations of this organism is its challenging genome. RNAi is the state-of-the-art tool for genetic manipulation inE. histolytica,though the RNAi pathway has several noncanonical features. Here, we harnessed the RNAi pathway to enable RNAi-based forward genetics for the first time in this organism. We validated the RNAi library by performing the firstE. histolyticaRNAi screen and identified slow growth mutants. We showed that independently-generated mutants also exhibited slow growth phenotypes, and we characterized protein localization and domains for some of the identified slow growth genes. The RNAi library that we constructed enables modern, quantitative Illumina deep sequencing analysis to identify mutants that are enriched or depleted after selection. We developed a novel analysis pipeline to precisely define and quantify full-length gene fragments inferred from read mapping. Our approach differs from previous approaches for analysis of RNAi screens, and it better represents the actual DNA fragments and their quantities. This study dramatically improves the tractability of this important pathogen. Moreover, the strategies behind this RNAi library, and its analysis, are novel, and can be applied to other organisms.

Author Contributions

A.B. designed and performed the experiments, except fragmentation of gDNA, immunofluorescence, and growth curves with expression of full-length or truncated proteins. R.L.S. performed SG1 and SG2 immunofluorescence experiments. M.C.R. performed growth curves with expression of full-length or truncated proteins. W.H. generated transfectants expressing full-length SG2. C.G.B. fragmented gDNA and processed the fragments to enable subsequent plasmid library construction. T.S.Y.T. and H.W.M. performed Gal/GalNAc lectin immunofluorescence experiments. S.S.H. designed the analysis strategy for Illumina sequencing data, and analyzed all Illumina sequencing data. M.L.S. oversaw the Illumina analysis approach. K.S.R. performed growth curves, conceived of the overall approach and oversaw the design and analysis of the experiments. A.B., S.S.H., and K.S.R. wrote the manuscript.