A genome-wide CRISPR-Cas9 screen identifies CENPJ as a host regulator of altered microtubule organization during Plasmodium liver infection

Prior to initiating symptomatic malaria, a single Plasmodium sporozoite infects a hepatocyte and develops into thousands of merozoites, in part by scavenging host resources, likely delivered by vesicles. Here, we demonstrate that host microtubules (MTs) dynamically reorganize around the developing liver stage (LS) parasite to facilitate vesicular transport to the parasite. Using a genome-wide CRISPR-Cas9 screen, we identified host regulators of cytoskeleton organization, vesicle trafficking, and ER/Golgi stress that regulate LS development. Foci of γ-tubulin localized to the parasite periphery; depletion of centromere protein J (CENPJ), a novel regulator identified in the screen, exacerbated this re-localization and increased infection. We demonstrate that the Golgi acts as a non-centrosomal MT organizing center (ncMTOC) by positioning γ-tubulin and stimulating MT nucleation at parasite periphery. Together, these data support a model where the Plasmodium LS recruits host Golgi to form MT-mediated conduits along which host organelles are recruited to PVM and support parasite development.


Introduction
Malaria is transmitted to humans by the injection of Plasmodium sporozoites into the skin during the blood meal of an infectious female Anopheles mosquito. Sporozoites exit the skin by traversing blood vessels to enter the circulation and are then carried to the liver. Sporozoites leave the circulation by traversing the sinusoidal cell layer and infecting hepatocytes and forming liver stages (LS) (Mota et al., 2001;Shortt and Garnham, 1948;Vanderberg, 1981). LS parasites reside in a membrane-bound compartment in the hepatocyte termed the parasitophorous vacuole (PV), where they differentiate into exoerythrocytic merozoites. The PV membrane subsequently breaks down, and merozoites reenter the blood and infect erythrocytes (Sturm et al., 2006).
Hepatocyte infection is obligate for parasite life cycle progression, and thus is an important target for antimalarial intervention. Elimination of the parasite during this stage would block both disease symptoms and transmission. During LS development, host hepatocytes undergo remarkable morphological changes. The cytoskeleton is a key regulator of deformability, and Plasmodium has been demonstrated to alter host cell actin cytoskeleton during LS development (Gomes-Santos et al., 2012), egress (Burda et al., 2017), and blood stage development (Hale et al., 2017;Warncke and Beck, 2019). We and others previously have demonstrated that the Plasmodium liver stage PV membrane interacts with late endosomes (Petersen et al., 2017), lysosomes (Lopes da Silva et al., 2012;Niklaus et al., 2019;Prado et al., 2015;Risco-Castillo et al., 2015;Vijayan et al., 2019), retrograde vesicles (Raphemot et al., 2019) and autophagic vesicles (Prado et al., 2015;Real et al., 2018;Wacker et al., 2017). As many intracellular pathogens target the host microtubule network to subvert host vesicle trafficking events for their own benefit (Alix et al., 2011;Asrat et al., 2014), we hypothesized that Plasmodium LS parasites actively alters the host cytoskeleton to traffic the host vesicles to PVM.
Multiple focused forward genetic screens have informed our understanding of host regulatory factors for LS malaria Raphemot et al., 2019;Rodrigues et al., 2008). These screens have provided valuable insights into parasite-host interactions, but the scope of these investigations have been limited, suggesting that a complete complement of factors required for Plasmodium entry and development remains to be discovered. Plasmodium LS infection actively remodels the host hepatocyte by rewiring a portion of host cell signaling and disrupting canonical signaling cascades . We therefore sought to use an unbiased genome-wide approach to identify the parasite driven host factors that contribute to host cytoskeleton remodeling.

CRISPR-Cas9 screen to identify host regulators of infection
Whole genome CRISPR-Cas9 screens facilitate an unbiased approach to identify regulators of a key process. To identify host genes critical for MT remodeling during Plasmodium LS development, we prioritized breadth in our approach, as canonical regulators of infection are sometimes rewired during infection . We used a pooled library of GeCKOv2 sgRNAs to generate a whole-genome knockout library in HepG2-CD81 cells Shalem et al., 2014). HepG2-CD81 cells were transduced with lentivirus containing the pooled GeCKOv2 sgRNA library of 123,642 sgRNAs targeting 19,031 protein-coding genes (~6 sgRNAs/gene), 1,864 microRNAs (4 sgRNA/microRNA) and 1,000 negative controls (2 sgRNA/control) and selected in puromycin for 5-7 days. To evaluate sgRNA diversity in the HepG2-CD81-GeCKOv2 library, we PCR-amplified the integrated sgRNA cassettes from genomic DNA extracted from transduced cells and subjected the amplified library to Illumina sequencing. At the gene level, 16,629 out of 19,031 (87.38%) genes targeted by 3 or more sgRNAs guides were significantly enriched. We observed an absence of sgRNAs targeting 2402 genes out of 19031 (12.62%); this may be due to gene essentiality or the failure of certain sgRNA to incorporate successfully into the genome. We infected forty million puromycin-resistant cells with green fluorescent protein (GFP) expressing-Plasmodium yoelii at a multiplicity of infection (MOI) of 0.3. After 24 h of infection, cells were sorted into infected and bystander cell populations by GFP signal intensity with fluorescence-activated cell sorting (FACS) ( Fig. 2A). Separately, a parallel culture of uninfected cells was also maintained to normalize the sgRNA frequency distributions. We obtained four independent biological replicates with library generation and sequencing occurring in parallel. Genes with significantly enriched sgRNAs were identified for both the bystander and infected populations compared to uninfected cells.
Cells that harbor genetic alterations restricting P. yoelii development (i.e., sgRNAs that target host genes important for infection) were expected to be enriched in the uninfected bin; we termed this group 'putative positive regulators of infection'. We categorized sgRNAs enriched in the infected cells as 'putative negative regulators of infection'. In this initial screen, we identified 242 genes that were statistically enriched in infected or bystander groups after accounting for multiple hypotheses. There were 67 genes significantly enriched in the infected cells compared to uninfected cells and 175 genes were significantly enriched in the bystander bin relative to uninfected bin.
To further down-select the high confidence genes, we reasoned that biological pathways with multiple putative regulators were more likely to be bona fide regulators of infection. Hence, we performed gene ontology (GO) pathway analysis to identify significantly enriched biological processes (Fig. 2B) using 242 hits from our initial screen.
We shortlisted the significant gene regulators present in statistically enriched biological processes for further validation. After this stringent down-selection step, we were left with eight putative negative regulators of infection and seven putative positive regulators of infection for further study (Fig,2C and D).

Integrating multiple forward genetic screens provides additional testable hypotheses
We report the first global screen for host factors that regulate Plasmodium LS infection.
Yet, like previous focused screens, our screen includes false negatives as sgRNAs are lost during the generation of the library and/or not all sgRNAs result in the disruption of the functional protein. To generate a more comprehensive picture, we systematically compared our screen, which interrogated host regulators of P. yoelii infection, with earlier forward genetic screens Raphemot et al., 2019;Rodrigues et al., 2008) that identified regulators of the closely related parasite, P. berghei (Fig. S1A, Supplementary file 1). For the purpose of analysis, we pooled results from the screens by Rodrigues et al., 2008 andPrudencio et al., 2008, as these two screens used the same methodology but had no overlapping factors. To compare our findings to previous screens, we developed a new methodology, meta-analysis by information content (MAIC), to combine data from diverse sources, in the form of ranked gene lists. Briefly, a meta ranking of the 3 screens was performed by sorting each screen separately by z score, calculating each gene's rank percentile location after sorting, and then averaging the gene rank percentile locations across the 3 screens, with no penalty for a gene being missing in a screen. This meta ranking was then sorted by average rank percentile location and augmented with the average z score from all screens where the function of the gene was evaluated (Supplementary file 1). Positively and negatively represented genes were sorted separately, then combined afterwards for pathway analysis. At gene level, using the z score cutoff of 2 and 1.5, our screen shared only few hits each of the other screens ( Fig. S1B and S1C) without any common hits across all screens. We reasoned this could be due to many factors including host cell type, parasite species and the methodology employed. When we loosened the stringency of the cutoff to a Z-score of 1, there were several genes overlapping between the three screens, although false positive rates could be higher at this cut-off.
Despite little specific gene overlap, we asked if overlapping pathways and biological processes were present in all the screens. We employed ClueGO to determine gene ontology (GO) (Bindea et al., 2009) and observed significant enrichment in biological processes from the genes represented in at least two of three screens at zscore of 1.5 and 1. Specifically, we identified 19 high confident biological processes that are significantly enriched using a z-score cutoff of 1.5. This includes biological processes that have been previously described, such as scavenger receptor activity and cholesterol biosynthesis reported (Itoe et al., 2014;Labaied et al., 2011;Petersen et al., 2017;Rodrigues et al., 2008) (Fig. S1D and S1E). Taken together, this combined resource provides a wealth of hypotheses for further investigation.

Identifying host factors that regulate Plasmodium LS invasion and development from CRISPR-Cas9 screen
To evaluate the false positive rate of our screen, we individually disrupted each of the 15 putative regulators with three sgRNAs per gene using CRISPR-Cas9 gene editing of HepG2-CD81 cells. In this system, a fluorescent reporter, GFP, is expressed only upon guide integration and puromycin resistance, enabling us to exclude any cells that did not take up and integrate the sgRNA (Fig. S2A). GFP positive cells were FACS sorted, cultured with puromycin and the knockout efficiency of CENPJ and COL4A3BP was further confirmed using western blot ( Fig. S2B and S2C). To identify genes that alter Plasmodium LS invasion, we infected each knockout line with P. yoelii sporozoites for 90 min and assessed hepatocyte entry by flow cytometry. Among the selected 15 hits, only low-density lipoprotein receptor-related protein 4 (LRP4) exhibited significantly reduced entry of sporozoites 90 min after infection (Fig. 3A). This is consistent with the previous finding that CSP interacts with LRP and HSPG to facilitates host cell invasion of Plasmodium (Shakibaei and Frevert, 1996).
As an orthogonal approach, we modulated HepG2-CD81 cells with small molecule inhibitors targeting positive regulators identified in the screen (Fig. 3B) (Supplementary Table 1). IC50 values for each small molecule inhibitor were obtained in uninfected HepG2-CD81 cells using Live/ Dead staining (Fig. S2D). We included eltanexer, an inhibitor of exportin-1 (XPO1), a putative negative regulator of infection (Than et al., 2020). To test the role of LRP4 in sporozoite entry of hepatocytes, we pretreated HepG2-CD81 cells with HPA-12, a ceramide transport inhibitor (Berkes et al., 2016) and an LDL-R blocking peptide, which blocks LRP4, significantly inhibited sporozoite entry. Thus, both genetic and peptide-mediated intervention of LRP4 inhibits sporozoite entry of hepatocytes ( Fig. 3A and B).
We next performed an imaging-based secondary screen with the selected 15 putative regulators to assess the role of these hits on the longer-term LS development.
This was intended to more closely mirror the experiment performed in the initial screen, although we used a later time point in order to characterize the full impact on LS development. Specifically, individual CRISPR-Cas9 knockout lines were infected with P.
yoelii sporozoites and observed 48 hours post infection (hpi). Several of the knockout lines exhibited substantially altered LS burden (Fig. 3C). The number of LS parasites was significantly increased in CENPJ (centromere protein J) and KDELC1 (Lys-Asp-Glu-Leu containing 1) disrupted lines, illustrating that each of these factors is indeed a negative regulator of infection. In contrast, knockout of CISD1 (CDGSH iron sulfur domain 1), COL4A3BP (collagen type IV alpha-3-binding protein), IREB (iron-responsive element-binding protein) and LRP4 significantly reduced the number of LS parasites 48 hpi (Fig.   3C). We next tested whether LS infection could be perturbed by targeting these factors with pharmacological inhibitors. Consistent with the genetic experiments, small molecule inhibitors (Supplementary table 1) that target CISD1, COL4A3BP, IREB, and LRP4, significantly reduced the number of LS parasites observed after 48 h of infection ( Fig.   3D), further supporting the notion that these factors are positive regulators of LS infection.
We next asked if any of the screen hits altered the growth of LS parasites.
Interestingly, knock out of VPS51 (Ang2) did not significantly alter parasite load but instead, the size of the parasite was significantly smaller (Fig. 3E). HepG2-CD81 cells expressing sgRNAs directed against COL4A3BP and LRP4, which both reduced the number of LS parasites (Fig. 3C), and the size of LS parasites (Fig. 3E). In contrast, depletion of CENPJ increased both the size and the number of LS parasites. Knockout of other putative regulators did not result in altered parasite size (Fig. 3E). Interestingly, while our screen was only set up to identify factors that altered infection rate, not LS growth, it is possible that some slow-growing parasites may have not reached the threshold of GFP levels to be included in the "infected" gate. Taken together, these studies identified several host factors influencing parasite entry, growth and development CENPJ is a conserved, ubiquitously expressed centrosomal protein with a key role in centriole organization and biogenesis (Cho et al., 2006;Ganem et al., 2009;Kohlmaier et al., 2009). The centrosome is a major microtubule organizing center (MTOC) (Hung et al., 2000). CENPJ depletion impairs centriole assembly, resulting in fragmented MTOCs and non-radial MT cytoskeleton organization (Cho et al., 2006;Ganem et al., 2009;Kohlmaier et al., 2009). To characterize the role of CENPJ in parasite development, we assessed the localization of γ-tubulin with γ-TuRC (γ-tubulin ring complex), a core functional unit of the MTOC (Wiese and Zheng, 2000). We infected HepG2-CD81 cells with P. yoelii sporozoites and allowed infection to proceed for 48 h. Cells were stained with anti-UIS4 (upregulated in infectious sporozoites gene 4) and γ-tubulin. In uninfected cells, γ-tubulin foci were localized primarily near nuclear periphery (88%) (Fig. 4A).
Next, we evaluated the functional role of CENPJ in regulating the LS parasite. In uninfected CENPJ depleted cells, we observed increased cytoplasmic localization (~80%) (  (Fig 4A and B); the absence of CENPJ further exacerbated the non-centrosome MTOC organization close to PVM that supports LS development (Fig 4A and B).

Golgi serves as a non-centrosomal MTOC (ncMTOC) in P. yoelii infected cells
Canonically, microtubule (MT) arrays nucleate from MTOCs and radiates towards cell periphery (Wiese and Zheng, 2000). To understand whether the γ-tubulin sequestration resulted in dynamic reorganization of MTs around the parasite, we infected rfp-α-tubulin transfected HepG2-CD81 cells with P. yoelii sporozoites, and allowed the infection to procced for 48 h. After 46 h, nocodazole was added. After an additional 2h (48h post-infection), cells were washed, and incubated with nocodazole-free media for 45 sec to allow the nucleation of MT. Cells were stained with γ-tubulin and UIS4. In uninfected cells, microtubules nucleated from γ-tubulin foci at the host nucleus ( Fig. 5A).
In the infected cells, MT nucleated from γ-tubulin foci localized adjacent to the PVM (Fig.   5A). This suggest that, during infection, MTOCs reorganizes the host microtubule network around the developing LS parasite.
Several studies have demonstrated that, in the absence of centrosome organizing proteins, Golgi outposts act as a non-centrosomal MTOCs (ncMTOCs) that function as MT nucleation sites by recruiting γ-tubulin foci ( (Grimaldi et al., 2013) reviewed in (Zhu and Kaverina, 2013)). To test whether the cytoplasm localized γ-tubulin foci are regulated by Golgi outposts, we infected rfp-α-tubulin expressing HepG2-CD81 cells with P. yoelii sporozoites. We allowed the infection to procced for 48 h. As above, nocodazole was added to the cells after 46 hours. Cells were washed and incubated for an additional 45 sec to allow MT nucleation. Cells were fixed then stained with antibodies against the Golgi peripheral cytoplasmic membrane protein, Golgi membrane protein of 130 kDa; golgin subfamily A member 2 (GM130), γ-tubulin and UIS4. In uninfected cells, we observed nucleating MTs originating at γ-tubulin foci associated with the host nucleus. In P. yoelii infected cells, we primarily observed MTs nucleating from γ-tubulin foci in proximity to GM130 staining ( Fig. 5B and C).
We next asked if an intact Golgi was required for PVM-associated MTOC formation. To do this, we utilized the small molecule brefeldin A, which reversibly disrupts and fragments the Golgi, blocking assembly and transport of secretory vesicles (Sciaky et al., 1997). We infected rfp-α-tubulin expressing HepG2-CD81 cells with P. yoelii sporozoites; after 24h cells were treated with brefeldin-A. Twenty two hours later (46h post-infection), nocodazole was added to the cells. After an additional 2h (48h postinfection), cells were washed, then incubated with media alone for 45 sec to allow MT nucleation. Cells were stained with antibodies against GM130, γ-tubulin and UIS4. In contrast to infected cells with intact golgi, following brefeldin-A treatment, we observed few γ-tubulin foci at the PVM and instead MT nucleation originating from at the nuclear periphery (Fig. 5B). These results are consistent with the hypothesis that the parasite utilizes Golgi-associated ncMTOC formation to initiate MT reorganization around PVM.

Host Golgi and intracellular vesicles interact with Plasmodium liver stage.
To better understand the role the Golgi plays in regulating ncMTOC formation, we studied Golgi-PVM interaction during infection. HepG2-CD81 cells were infected with P.
yoelii sporozoites and stained cells with antibodies against the Golgi peripheral cytoplasmic membrane protein, GM130 (Golgi membrane protein of 130 kDa; golgin subfamily A member 2). We observed UIS4-positive membrane co-localized with Golgi stacks in nearly three quarters of the infected cells at 48 hpi (Fig. 6A). Consistent with other reports (De Niz et al., 2020;Raphemot et al., 2019), we also observed Golgi localized to the PVM (Fig. 6A). Co-localization between the PVM and Golgi was reduced following brefeldin-A treatment ( Fig. 6A and C). These results are consistent with a model where an association between PVM-Golgi induces ncMTOC formation and reorganizes the MT network.
We hypothesized that the reorganization of MT network by parasite serves to redirect vesicle traffic to the PVM and facilitate LS survival. This hypothesis is based on two observations: (1) that the Golgi reorients the MTOC to the parasite periphery and (2) that GO terms associated with vesicular trafficking and Golgi and ER stress were significantly enriched as putative regulators of infection in screen (Fig. 2B). We infected HepG2-CD81 cells with P. yoelii sporozoites and allowed infection to proceed for 24 or 36 h. Cells were stained with anti-UIS4 and anti-VAMP7 (to visualize intracellular vesicles) antibodies to visualize the PVM and host intracellular vesicles, respectively. We observed colocalization between intracellular vesicles and the PVM (

Discussion
For decades, dogma suggested that the elimination of anything short of 100% of LS parasites would result in little to no benefit in the effort towards malaria eradication. New evidence suggests this is not the case. Mathematical models suggest elimination of even a portion of hypnozoites could dramatically reduce P. vivax prevalence (White et al., 2018). Recently, it was demonstrated that targeting host aquaporin 3 leads to the elimination of P. vivax hypnozoites from field isolates (Posfai et al., 2020), suggesting that host targeted interventions may provide an opportunity to tackle even the Achilles heel of malaria control efforts. Another host factor known to be critical for LS infection, the tumor suppressor p53 (Douglass et al., 2015;Kain et al., 2020;Kaushansky et al., 2013), has been associated with lower severity of infection in Malian children (Tran et al., 2019).
Additionally, host targeted interventions can induce at least partial immunity to subsequent challenge (Ebert et al., 2020). This supports the investigation into the use of host targeted drugs in casual prophylaxis strategies, provided the drugs have suitable toxicity profiles (reviewed in (Glennon et al., 2018)). Because not all host targets are suitable drug targets, a broad and comprehensive picture of factors that regulate LS malaria infection is needed.

Previous forward-genetic screens have identified host factors involved in
Plasmodium infection Raphemot et al., 2019;Rodrigues et al., 2008).These screens have exhibited very little overlap in identified factors (Fig. S1A, B & C), presumably in part because each screen prioritized identifying a small, but bona fide list of "hits," and suffered a high false negative rate as a result. This has led to many key discoveries into interactions between the malaria parasite and its host hepatocyte but has fallen short of providing a systematic view of the fundamental biological properties that regulate the development and survival of the LS parasite. Like the earlier screens, the CRISPR-Cas9 screen we report here does not exhibit substantial overlap with previous screens when individual gene hits are evaluated, suggesting that additional analysis is still needed to comprehensively assess factors that regulate infection. Yet, when we evaluate whether hits from our screen are present in similar pathways to those observed in other screens, the overlap is substantial (Fig. S1D & E). Thus, while we may have, as a field, identified many central regulatory biological functions that control LS development, we have yet to saturate our understanding of the molecular players that mediate these biological necessities. Together, the CRISPR-Cas9 screen we present here, along with the previously reported siRNA screens, represent a key resource for the field moving forward, and we anticipate that merging findings from these experiments (Supplemental File 1) will provide many additional hypotheses to probe. One limitation of this work is that, since it is likely that at least a subset of canonical signaling pathways are rewired in the course of infection , pathway analysis, which is based primarily on canonical signaling networks, is unlikely to comprehensively describe the an entirely accurate topology of the signaling relationships that mediate the complex host-parasite interface. Developing tools to reconstruct signaling relationships, within the context of malaria infection, is a critical area for future investigation.

Significance
New strategies to combat malaria in the field are desperately needed. The causative agent of malaria, the obligate intracellular parasite Plasmodium, relies heavily on its mammalian host to survive and develop. The identification host regulators of infection provide an opportunity combat parasites, particularly during their initial liver stage of infection. Here, we perform a genome wide forward genetic screen to identify host factors that regulate Plasmodium liver stage infection. We demonstrate a mechanism by which the parasite remodels the host cytoskeleton to redirect host vesicular traffic to the parasite and facilitate its development. Our work implicates diverse host processes in liver stage Plasmodium development, which may be leveraged to develop pharmacological agents to fight malaria.

Mosquito rearing and sporozoite production
For P. yoelii sporozoite production, female 6-8-week-old Swiss Webster mice (Harlan, Indianapolis, IN) were injected with blood stage P. yoelii (17XNL) parasites to begin the growth cycle. Animal handling was conducted according to the Institutional Animal Care and Use Committee-approved protocols. Briefly, Anopheles stephensi mosquitoes were allowed to feed on infected mice after gametocyte exflagellation was observed. Salivary gland sporozoites were isolated using a standard protocol at day 14 or 15 post-blood meal. The sporozoites were activated with 20% v/v FBS and pelleted by centrifugation at 1,000 × g to salivary gland detritus. Sporozoites were further enriched by a second centrifugation at 15,000 × g for 4 min at 4 °C, before resuspension in a desired volume of complete medium.
On day 12-14 post-transduction, 40 million puromycin-resistant cells were infected with GFP tagged-P. yoelii at a MOI of 0.3. After 24 h of infection, cells were sorted as infected and uninfected by FACS into different bins based on GFP signal. A non-treated, noninfected control was also collected for each experiment to assess library representation.
The experiment was performed four independent times. Genomic DNA from each sample was isolated using QIAamp DNA mini kit (Qiagen, Hilden, Germany).

Next-generation sequencing
Libraries were generated using a 2-step PCR according to previously published protocol . Briefly, an initial PCR was performed using AccuPrime Pfx Supermix (Invitrogen, Waltham, MA, USA) with lentiCRISPRv2 adaptor primers to amplify the sgRNA region and add priming sites for Illumina indexing. Amplicons were purified using FlashGels (Lonza, Allendale, NJ, USA) and purified PCR products were used as templates for subsequent PCR amplification. Sufficient PCR reactions were performed to maintain library coverage. Next, a second PCR was performed in order to add Illumina P5 and P7 index sequences, as well as barcodes for multiplexing, and samples were re- Undetected guides (RPM below 0.1) were excluded from further calculations. 16,629 out of 19,031 (87.38%) genes targeted by 3 or more guides/ sgRNA were detected in least 3 experiments. Fold change with respect to 'before infection' was calculated by dividing RPM in 'infected' or 'bystanders' conditions by RPM in 'before infection' condition. The differential abundance of a guide is represented as the log2 ratio of fold change in 'infected' condition divided by the fold change in 'bystanders' condition. If less than two screens call a guide detected (RPM >= 0.2), a log2FC of 0 and p-value of 1 are reported for this guide. Otherwise, the final log2FC of the guide is the arithmetic mean of the log2 ratios from each detected screen, and the final p-value of the guide is calculated by one sample t-test that the log2 ratios of the detected guides was not zero. The GeCKO library contains 6 independent guides for each protein-coding gene. The log2FC and p-value at the gene level is calculated from log2FC and p-value of its 6 guides. The log2FC of a gene is equal to the log2FC of the guide with the lowest (best) p-value. The corrected p-value of a guide is set to 1 if the sign of its log2FC is opposite to the log2FC of the gene. Then

Immunofluorescence
For imaging experiments, HepG2-CD81 wild type or knockout cells were plated in 8 well chamber slides (Labtek) and infected with P. yoelii sporozoites. Cells were fixed with 3.7% The Netherlands). Images for processed with IMARIS Bitplane, image analysis software to quantify LS, perform colocalization analysis and remove outlier cells. For the high throughput secondary screen, cells were plated onto 96 well plate, infected and stained as explained above. Images were acquired using Keyence BZ-X800 automated microscope and infection rate were quantified using Imaris 9.5, image analysis software.

Meta-analysis of screens
Z-scores of positively and negatively represented genes in each screen were calculated separately. Meta ranking was performed by function metaRank() from the R package DuffyTools, (using arguments: mode="percentile", rank.average.FUN=mean, naDropPercent = 0.75). Positively represented genes with z-scores greater than the cutoff (1.0, 1.5, 2.0) and negatively represented genes with z-scores smaller than the cutoff (-1.0, -1.5, -2.0) were selected as hits for each screen. Hits of positively and negatively represented genes were combined for further pathway enrichment analysis. To compare datasets of uneven sizes, gene rank percentiles were assigned to positively and negatively represented genes in each screen separately. Genes were ranked by the average percentiles across all datasets where they were screened.

Gene Ontology analysis on integrated forward genetic screens
Identified hit genes from all the four screens were uploaded in ious combinations in the to generate gene ontology (GO) and pathway enrichment networks. Enriched functionally annotated groups were obtained with the following setting parameters: organism was set to Homo sapiens; the total gene set used in each of the screen were used as reference; the gene ontology terms were accessed from the following ontologies/pathways: Biological Process and Reactome Pathway database evidence code was restricted to 'All_without_IEA'. The GO fusion option was also selected. The significance of each term was calculated with a two-sided hypergeometric test corrected with Benjamini-Hochberg correction for multiple testing. The kappa score was set to 0.5 and the GO tree levels were restricted at 6-16 (medium-detailed specificity). For GO term selection, a minimum of 3 genes and 3% coverage of the gene population was set. GO terms were grouped with an initial group size of 2 and 50% for group merge. The remaining parameters were set to defaults.        Evaluation of cytotoxicity profile of small molecules used in the study. Data are presented as the mean cytotoxicity value ± standard deviation from one representative experiment of three independent experiments.