A genome-scale vector resource enables high-throughput reverse genetic screening in a malaria parasite

Abstract

The genome-wide identification of gene functions in malaria parasites is hampered by a lack of reverse genetic screening methods. We present a large-scale resource of barcoded vectors with long homology arms for effective modification of the Plasmodium berghei genome. Cotransfecting dozens of vectors into the haploid blood stages creates complex pools of barcoded mutants, whose competitive fitness can be measured during infection of a single mouse using barcode sequencing (barseq). To validate the utility of this resource, we rescreen the P. berghei kinome, using published kinome screens for comparison. We find that several protein kinases function redundantly in asexual blood stages and confirm the targetability of kinases cdpk1, gsk3, tkl3, and PBANKA_082960 by genotyping cloned mutants. Thus, parallel phenotyping of barcoded mutants unlocks the power of reverse genetic screening for a malaria parasite and will enable the systematic identification of genes essential for in vivo parasite growth and transmission.

Graphical abstract

Figure 1

PlasmoGEM: A Genome Scale Free Resource of Genetic Modification Vectors for P. berghei Reverse Genetics (A) A diagram of the modular vector production process showing the efficiency at each step (red), as well as resources (gray boxes) and data (dashed lines) submitted to the database. (B) Genome coverage achieved to date. (C) Schematic showing knockout vector designs and locations of the gene-specific molecular barcode included in each vector. (D) Default C-terminal epitope-tagging vector and a panel of alternative fusion tags.

Figure 2

Schematic Representation of a Typical Barcode Sequencing Experiment For each experiment three inbred mice are infected from a separate transfection of the same vector pool.

Figure 3

Reproducibility of Independent Barcode Counting Experiment with Respect to the Abundance and Relative Replication Rates of All Barcodes (A) Each experiment involved three replicate transfections of a different schizont culture performed on a different day and using independently prepared vector pools. Error bars show standard errors (n = 3 per experiment). Green lines, four sexual stage genes (p25, p28, p230p 3xHA tag, and soap). Orange lines, three attenuated mutants (plasmepsin IV, PBANKA_110420, PBANKA_140160). Twenty-two mutants are shown in total. See Figure S1 for genotyping data. (B) Linear regression analysis of mean abundance values for the two experiments shown in (B). All barcodes present until day 8 posttransfection were included. Error bars show standard errors of the mean (n = 3). (C) Regression analysis of average mean fitness for each barcode between days 5–8 posttransfection for the two biological replicates in (B). Fitness is calculated from the replication rate of the gene-specific barcode relative to the mean of the four sexual stage reference genes. Error bars show standard errors (n = 3). See Table S1 for fitness measurements for individual vectors, and Table S4, illustrating data analysis.

Figure 4

Fitness Measurements Obtained with PlasmoGEM Vectors Targeting Protein Kinases (A) Distribution plot generated from a ranked list of day 7 fitness values measured in triplicate for each of 42 genes in experiment 1 (left axis). The relative abundance of a targeting vector in the electroporation cuvette at the moment of transfection (gray crosses, right axis) did not predict whether a mutant could be obtained. See Figure S2 for relative abundance data of a representative replicate experiment. (B) Fitness of reference mutants averages 1 by definition. Error bars show standard errors (n = 6). (C) Fitness of selected mutants. Error bars as in (B). Asterisk, different from reference mutants as determined by a two-sided t test corrected for multiple testing (p < 0.01; n = 6).

Figure 5

Barcode Sequencing Is Validated by a Comparison with Published Data and Genotyping of Mutants (A) Barseq screen of 46 PlasmoGEM vectors targeting protein kinases compared to the conventional kinome screen by Tewari et al. (2010). (B) Read coverage from whole-genome sequencing of highly enriched mutant populations showing deletion of rio1 in a haploid genome (upper panel), and insertion of a rio2 knockout vector associated with stabilization of a 29.7 kb duplication including rio2. See Figure S5 for additional genotyping data. (C) Updated tree showing targetable and essential P. berghei protein kinases. Targetability of cdpk1 was independently shown by Jebiwott et al. (2013). A role for PK4 in blood stage growth was demonstrated by Zhang et al. (2012). See Figures S3 and S4 for genotype confirmation of cloned mutants for the knockouts.

Figure 6

Absence of Evidence for Multiple Integration Events (A) Vector pools were transfected into marker-free lines with pre-existing barcodes in cdpk1, cdpk3, or cdpk4. New barcodes account for approximately half of the total, as would be expected if each parasite genome carried exactly one new barcode. The slight overrepresentation of background barcodes on day 4 probably comes from parasites that failed to integrate a vector and which were not yet completely eliminated after only 3 days of drug selection. All data points are supported by three experiments, and error bars show standard deviations. See Figure S3 for genotyping of marker-free lines. (B) PCR genotyping was performed on parasite gDNA from six infected mice, each transfected with one of three final targeting vectors in the presence of a 20-fold excess of intermediate vectors (10 μg total DNA per transfection), which have the same homology arms but a zeocin resistance cassette that cannot be selected in P. berghei. Presence of intermediate vectors in the input cocktail but absence in the resistant parasite populations suggests that multiple integration events are rare or absent, since hitchhiking of marker free insertions would otherwise be observed.