Efficient identification of antimalarials with novel mechanism of action via multi-sample transcriptomic profiling

Efficient identification of antimalarials with novel mechanism of action via multi-sample transcriptomic profiling | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Efficient identification of antimalarials with novel mechanism of action via multi-sample transcriptomic profiling Ryuta Ishii , View ORCID Profile Takaya Sakura , Jing Hong , Kazuya Yasuo , Kazunari Hattori , Paul A Willis , Teruhisa Kato , View ORCID Profile Daniel Ken Inaoka doi: https://doi.org/10.1101/2025.02.27.640686 Ryuta Ishii 1 Laboratory for Drug Discovery and Disease Research , Shionogi & Co., Ltd. , Osaka 561-0825, Japan 2 Department of Cellular Architecture Studies, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Takaya Sakura 3 Department of Molecular Infection Dynamics, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan 4 School of Tropical Medicine and Global Health, Nagasaki University , Sakamoto, Nagasaki 852-8523, Japan 5 Department of Infection Biochemistry, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Takaya Sakura Jing Hong 3 Department of Molecular Infection Dynamics, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kazuya Yasuo 6 Laboratory for Medicinal Chemistry Research , Shionogi & Co., Ltd. , Osaka 561-0825, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kazunari Hattori 6 Laboratory for Medicinal Chemistry Research , Shionogi & Co., Ltd. , Osaka 561-0825, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul A Willis 7 MMV Medicines for Malaria Venture , 20, Route de Pré-Bois, 1215, Geneva 15, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Teruhisa Kato 1 Laboratory for Drug Discovery and Disease Research , Shionogi & Co., Ltd. , Osaka 561-0825, Japan 8 Exploratory Research for Drug Discovery, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Ken Inaoka 3 Department of Molecular Infection Dynamics, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan 4 School of Tropical Medicine and Global Health, Nagasaki University , Sakamoto, Nagasaki 852-8523, Japan 5 Department of Infection Biochemistry, Institute of Tropical Medicine (NEKKEN), Nagasaki University , Nagasaki 852-8523, Japan 9 Department of Biomedical Chemistry, Graduate School of Medicine, The University of Tokyo , Tokyo 113-0033, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Ken Inaoka For correspondence: danielken{at}nagasaki-u.ac.jp Abstract Full Text Info/History Metrics Preview PDF Abstract Malaria poses a significant health burden to endemic countries, necessitating the development of new antimalarials with novel mechanisms of action to combat emerging drug resistance. However, target identification methods for antimalarial compounds selected through phenotypic screening are currently limited, which has been a major obstacle in the field of antimalarial drug discovery. Here, we aim to address this need using perturbation transcriptomics to efficiently select antimalarial compounds targeting new pathways or molecules. The method involves comparing the transcriptomic changes by drug perturbation in the asexual blood stage of the malaria parasite Plasmodium falciparum. Using this method, we identified several compounds that exhibited distinct transcriptomic profiling from a diverse set of antimalarials with known modes of action, indicating their potential to target different pathways. In addition, we successfully identified a novel histone deacetylase inhibitor using this approach and chemogenetically validated its target through in vitro evolution. These results highlight the effectiveness of this platform in accelerating antimalarial drug discovery. Introduction Malaria is a devastating disease caused by Plasmodium parasites transmitted to humans through the bites of Anopheles mosquitoes. According to the World Health Organization, there were an estimated 263 million malaria cases and 597 thousand deaths in 2023 1 . Particularly in sub-Saharan Africa, where 94% of cases and 95% of deaths occur, malaria poses significant public health challenges, leading to economic and developmental issues despite the availability of several treatment options. The first-line treatment of uncomplicated malaria since 2006 has been artemisinin-based combination therapies (ACT), the combination of artemisinin derivatives and different class partner drugs. Over 200 million ACT treatment courses are used yearly 1 , and the overall treatment success rate is higher than 90% 2 , however, emerging artemisinin partial resistance is confirmed in many countries, including in sub-Saharan Africa 3 – 7 . In addition, there are emerging reports of potential resistance to the ACT partner drugs. One of the strategies to combat emerging resistance is developing a new class of antimalarials that acts by novel mechanisms of action (MoAs) and can replace artemisinin derivatives. A single oral dose regimen and rapid parasite clearance are essential to replace artemisinin derivatives and avoid the emergence of a new resistance 8 . Many groups have been working on fulfilling those criteria, however, no single-dose cure product has yet made it to market. Continuous drug discovery studies are required to reduce the burden of malaria toward elimination and eradication. Although the target-based approach is a dominant methodology in drug discovery, a comprehensive analysis suggested its inefficiency 9 . That is also true in antimalarial drug discovery; despite the dedication of many groups for more than a decade, none of the target-based antimalarial candidates have succeeded yet 10 , and most of the new class pipeline drugs in clinical trials are developed from phenotypic screening 11 – 17 . Phenotypic screening may allow researchers to easily select potent compounds, however, the uncertainty of the MoAs can preclude quick drug development, mainly due to issues in safety profiles and structure-activity relationships 18 . Furthermore, without identifying the actual MoAs, there is always a risk of developing new compounds with the same MoA as an existing drug, which cannot then be used as a next-generation antimalarial combination therapy. Therefore, several target identification techniques have been developed for identifying targets of antimalarials, such as click-chemistry pull-down, cellular thermal shift assay, and multi-omics approach 19 . These techniques sometimes provide precise results; for example, the targets of plasmepsin inhibitor WM382 were identified by cellular thermal shift assay 20 . However, given that the targets of antimalarials in the drug development pipeline are not specified in many cases 16 , 21 , 22 , there are limitations of those techniques, depending on the targets or compounds, and a new approach to antimalarial target identification is required. It would be particularly advantageous if any new methodology could be applied to tens of compounds, allowing it to be used to prioritize the most promising hits emerging from high throughput screens. Many of the existing methods are low throughput or required significant resources and time, making them unsuitable for screening a large number of compounds in a timely manner. RNA-seq analysis is a powerful technique that uses high-throughput sequencing to detect and quantify gene expression levels. It allows researchers to analyze a wide range of RNA populations, including mRNA and total RNA, providing valuable insights into biological processes. Transcriptome patterns of cells can be changed by any stimulation, including drug treatment, and can be a strong tool for drug discovery. It was reported that high-throughput RNA-seq analysis could distinguish MoAs of compounds by clustering based on transcriptomic changes of mammalian cell lines after drug perturbulation 23 . Identifying the exact MoA of a compound was also possible by using the same technology with comparing transcriptome patterns of known MoA compounds 24 , showing the usefulness of this technique in phenotypic drug discovery. RNA-seq analysis has also been used in research on the malaria parasite Plasmodium falciparum because its genome is one of the best annotated eukaryotic genomes 25 . It has been demonstrated that RNA-seq analysis can provide new insights into a variety of research, from in vitro single-cell analysis for identifying sexual differentiation drivers to the identification of transcriptional patterns in field isolates related to cerebral malaria 26 , 27 . However, multi-sample transcriptomics has never been applied to the asexual blood stage in the field of antimalarial drug discovery, Here, we propose a method to detect and compare transcriptomic changes of P. falciparum by drug perturbation, which could estimate the MoAs of antimalarial drugs and led to identification of a compound with a novel MoA. Our transcriptome clustering identified a novel P. falciparum histone deacetylase 1 ( Pf HDAC1) inhibitor from previously uncharacterized phenotypic antimalarial hits, demonstrating the effectiveness of this method as a powerful tool for target identification studies. Results RNA-seq analysis of drug perturbation of P. falciparum asexual blood stage To establish a method for efficiently detecting transcriptomic changes in P. falciparum after drug perturbation, we implemented a 96-well plate-based procedure. Since mRNA expression patterns significantly change throughout the different stages of the blood stage parasite 28 , we separately collected RNA from the tightly synchronized ring, trophozoite, and schizont stage parasites after treatment with each compound ( Fig. 1 ). To comprehensively understand how the MoA of a compound affects transcriptomic changes, we tested 63 compounds in triplicate for this study ( Table S1 ). Of these, 9 compounds were structurally new and identified from Shionogi & Co., Ltd. chemical library 29 , while others were compounds demonstrated to have antimalarial activity including a diverse set of compounds from Medicines for Malaria Venture (MMV) having either a known mode of action or compounds refractory to resistance where a mode of action could not be determined. RNA purification and reverse transcription were also performed in a 96-well plate, after which samples were collected for library preparation and Illumina sequencing. As a result, the number of read counts and detected genes varied according to the developmental stage, consistent with their RNA content 30 ( Fig. 2 ). Only four out of 576 samples were excluded during quality control based on a detected gene count 500 or below. We concluded that antimalarial compound-treated parasites had a sufficient amount of RNA and could be used for the RNA-seq analysis. Download figure Open in new tab Figure 1. Overview of the assay procedure. P. falciparum was tightly synchronized to the ring, trophozoite, or schizont stage by sorbitol synchronization at various time points and dispensed onto compound plates. After an 8-hour incubation, total RNA from each sample was purified using a 96-well spin column, followed by cDNA library preparation and sequencing using a next-generation sequencer. Download figure Open in new tab Figure 2. RNA-seq qualities of drug-treated parasites. Distribution of a read count and b number of genes detected by RNA-seq. Each parasite stage includes 192 samples from drug-treated asexual P. falciparum parasites. Transcriptomic changes can distinguish MoAs of antimalarials For further analysis, we utilized 1000 highly variable genes to maximize the differences in expression patterns among each sample. We performed dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) 31 , followed by clustering analysis. This analysis revealed several clusters of samples that exhibited distinct changes in mRNA expression ( Fig. 3 ). The clustering results for the ring stage suggested that this stage may not be suitable for observing MoA-based transcriptomic changes, as it only showed clusters with a wide range of different MoAs. However, the results for the trophozoite and schizont stages revealed more clusters, some of which included a small number of compounds that may have the same MoA ( Table 1 ). For example, Cluster No. 6 in the schizont stage includes cytochrome bc 1 inhibitors and the dihydroorotate dehydrogenase (DHODH) inhibitor DSM265, all of which should lead to the same phenotype according to the previous findings showing that these compounds lose their antimalarial activities when yeast DHODH is overexpressed in the blood stage of the parasite 32 , 33 . Furthermore, MMV021735 has been reported to have metabolic profiles similar to those of DSM265 and atovaquone 34 , indicating that our RNA-seq analysis has the potential to detect inhibition of the mitochondrial pathway by this compound as well. Overall, the schizont stage showed more clusters with distinct MoAs: Cluster No.3 contains hemozoin formation inhibitors (chloroquine 35 , ferroquine 36 ), Cluster No. 4 contains translation inhibitors (M5717 37 , MMV670325 38 , MMV674143 39 , and MMV009952 40 ), Cluster No. 5 contains dihydrofolate reductase–thymidylate synthase (DHFR-TS) inhibitors (pyrimethamine 41 and MMV027634 42 ). Our phenotypic hit with a novel chemotype was also included in Cluster No. 3, potentially indicating the finding of a new class of hemozoin formation inhibitor. In addition, the result of Cluster No. 10 in the trophozoite stage suggests that MMV011438 may function as a new inhibitor of acetyl-coenzyme A synthetase (AcAS), despite the presence of a resistance gene that is essential for activating the compound 43 . Further investigation into the MoAs of these novel compounds is required. Download figure Open in new tab Figure 3. Clustering analysis based on transcriptomic changes of parasites after drug treatment. The results of mapping and clustering of transcriptomes from a the ring stage, b the trophozoite stage, and c the schizont stage of P. falciparum . The Uniform Manifold Approximation and Projection (UMAP) of quality-filtered transcriptome (left panels). Cluster numbers for each stage are indicated by different colors. Heatmaps of normalized gene expression levels displayed according to the clusters (right panels). Clusters with distinctive MoAs are summarized in Table 1 . The number of samples for the ring, trophozoite, and schizont stages were 191, 191, and 190, respectively. View this table: View inline View popup Download powerpoint Table 1. List of compounds in distinctive clusters. Clusters of compounds with similar or small numbers of MoAs are listed. All the triplicate samples in the compounds listed above were in the same cluster. MoA: mechanism of action, HDAC: histone deacetylase, AcAS: acetyl-CoA synthetase, DHFR-TS: bifunctional dihydrofolate reductase-thymidylate synthase, ETC: electron transport chain, DHODH: dihydroorotate dehydrogenase, PfFNT: P. falciparum formate-nitrite transporter. RNA-seq analysis identified a novel Pf HDAC1 inhibitor The results of Cluster No. 4 in the trophozoite stage were inconclusive because it included compounds with two different MoAs: chlamydocin-like HDAC1 inhibitor 44 , 45 and threonine-tRNA synthetase (TRS) inhibitor Borrelidin 46 . To determine the MoA of the phenotypic hits, we conducted Novel A compound resistance selection using the Dd2 strain. After 23 days of single-step selection at a concentration equal to 5-fold EC 50 , viable parasites were observed, followed by cloning, which resulted in the obtaining of five resistant clones. All the clones exhibited similar fold changes to Novel A ( Fig. S1 ), and whole genome sequencing identified single nucleotide variants (SNVs) of three genes, HDAC1 (PfDd2_090030700), Cyclic nucleotide-binding domain containing protein (PfDd2_140022100) and conserved Plasmodium protein, unknown function (PfDd2_140047100) as common SNVs in all clones ( Fig. S2 ). According to the gene conservation among P. falciparum strains and essentiality, we focused on the L137F mutation in PfHDAC1. To further validate the effect of the L137F mutation in Novel A resistance, we generated the Dd2 strain with or without the PfHDAC1 L137F mutation (Dd2-L137F) using CRISPR-Cas9 mutagenesis ( Fig. 4a ). Drug selection, followed by cloning, allowed us to obtain three clones each with confirmed synthetic DNA insertions through diagnostic PCR ( Fig. S3 ). Clones with the L137F mutation demonstrated EC 50 fold changes similar to the Novel A resistant strains ( Fig. S4 ), confirming that the L137F mutation is responsible for the resistance in these strains. We further corroborated the effect of the L137F mutation on resistance to PfHDAC1 inhibitors using molecular docking with a Novel A derivative Compound 1, which also showed lower activity against the L137F mutant strains ( Figs. 4b , 4c ). In this analysis, the bulky moiety of the phenylalanine mutant obstructed the binding of Compound 1 into the active site of PfHDAC1 ( Fig. 4d ). Based on these findings, we concluded that the Novel A compound, and presumably the structurally similar Novel G compound, are a new-class of PfHDAC1 inhibitors, underscoring the utility of our RNA-seq approach in identifying the MoAs of novel antimalarials. Download figure Open in new tab Figure 4. A novel compound series targeting PfHDAC1 and their activity loss due to L137F mutation in PfHDAC1. a Schematic representation of donor DNA insertion into the HDAC1 locus using CRISPR-Cas9 (left panel). Five silent mutations are inserted into the sgRNA target sequence of both control and L137F mutation donor DNAs (right panel). b Dose-response curves of Novel A (left panel) and Novel A derivative Compound (right panel) for the parental Dd2 line, HDAC1:L137F mutant lines, and HDAC1:silent mutation control lines (four technical replicates and three independent experiments, mean ± SD). c The structure of Compound 1. d PfHDAC1/ Compound 1 complex model for the wildtype (left panel) and L137F mutant (right panel). Compound 1 is depicted in green, and the L137 or F137 amino acid is highlighted with the electron density of atoms. Yellow dot lines represent the hydrogen bonds between the compound and amino acids. The red dot line indicates the collision between F137 and Compound 1. yDHODH: yeast dihydroorotate dehydrogenase, sgRNA: single guide RNA, HR: homologous region, Amp: ampicillin-resistant gene, PAM: protospacer adjacent motif. Discussion In the present study, we utilized multi-sample RNA-seq to estimate the MoAs of a wide variety of antimalarials in P. falciparum for the first time. This method is time-efficient, only requiring an 8-hour drug treatment of the parasite in a 96-well plate. Furthermore, recent advancements in next-generation sequencers have made it cost-effective and capable of processing larger sample sizes. Although the RNA content of the parasite at different developmental stages affected read counts and the number of detected genes, antimalarial drug treatment was compatible with performing the RNA-seq analysis and subsequent clustering based on changes in transcriptomic patterns. We evaluated 63 compounds with at least 35 different MoAs, resulting in 4, 10, and 12 clusters in the ring, trophozoite, and schizont stages, respectively. These findings suggest limitations in our method, as not all antimalarial treatments induce transcriptomic changes in parasites, and it is unpredictable which compounds will form distinctive clusters until tested. In addition, we did not observe MoA-specific clusters in the ring stage, potentially indicating that our method is unsuitable for assessing MoAs of ring stage-specific antimalarials. However, ferroquine, which is expected to be effective specifically in the ring stage 34 , was detected as one of the hemozoin formation inhibitors in the trophozoite stage, suggesting that ring stage-specific inhibitors may also affect the transcriptome of parasites in different stages. Further investigation is required to fully understand the behavior of ring stage-specific antimalarials in our method. It is worth noting that compounds targeting different proteins but predicted to have the same phenotypic outcomes clustered together, such as translation inhibitors and electron transport chain inhibitors. This demonstrates that our method can detect the transcriptomic response of parasites to compounds and can be a powerful tool for identifying antimalarials with novel MoAs. In the trophozoite stage, Cluster No. 5 includes only three antimalarials with unknown MoAs, suggesting that they may share a novel target. Of these three compounds, MMV1542945, also known as ACT-451840, only exhibited resistance during the minimum inoculum for resistance (MIR) studies 47 . All resistant strains had mutations in P. falciparum multidrug resistance-1 (PfMDR1), but there is no clear evidence of a direct interaction between PfMDR1 and ACT-451840. Assessing the efficacy of MMV006455 and MMV665888 against PfMDR1 mutants would provide new insights into understanding the MoA of ACT-451840. Another example of compounds with unknown MoAs that exhibited distinguishable transcriptomic patterns is Cluster No. 12 in the schizont stage, comprising MMV007839 and MMV020750. It has been demonstrated that MMV007839 inhibits P. falciparum formate-nitrite transporter (PfFNT) 48 , 49 , while MMV020750 may be a weak aminopeptidase inhibitor or β-hematin inhibitor 50 , 51 . Given that the relations between reported activities of MMV020750 and PfFNT functions are unclear, MMV020750 may directly inhibit PfFNT. Conducting a PfFNT functional assay would be necessary to confirm this MoA. In vitro resistance selection and CRISPR-Cas9 mutagenesis results demonstrated that Cluster No. 4 in the trophozoite stage consists of HDAC inhibitors, while Borrelidin is reported to be a TRS inhibitor. Borrelidin has been found to have different functions in other systems, such as angiogenesis inhibition and apoptosis induction 52 , 53 , and it is also known as a cyclin-dependent kinase (CDK) inhibitor of Saccharomyces cerevisiae 54 . CDKs are conserved in P. falciparum and expressed in the asexual blood stage, where they are believed to have diverse functions 55 . Among these CDKs, P. falciparum CDK-related kinase 3 (Pfcrk-3) was found to be associated with protein complexes exhibiting HDAC activity and is essential for parasite proliferation 56 . This indicates that inhibition of Pfcrk-3 by Borrelidin may have resulted in the inhibition of HDAC and incorporation of Borrelidin into Cluster No. 4 in the trophozoite stage. The segregation of HDAC inhibitors into Clusters No. 9 and No. 10 in the schizont stage suggests they may have slightly different functions, possibly due to their specificities against other HDACs expressed in the asexual blood stage 57 . However, in vitro functional assays will be required to investigate further details. In conclusion, we have developed a method for conducting RNA-seq of the blood stage malaria parasite P. falciparum after antimalarial treatment using a 96-well plate. Clustering based on transcriptomic changes in the drug-treated parasite successfully predicted the MoAs of several antimalarials. This assay is believed to be advantageous in identifying antimalarials with novel MoAs, thereby accelerating the discovery of next generation antimalarial drugs. Methods Parasite cultures Plasmodium falciparum 3D7 and Dd2 parasites were maintained in RPMI1640 medium supplemented with 25 mg/L gentamicin, 50 mg/L hypoxanthine, 23.8 mM sodium bicarbonate, and 0.5% (w/v) Albumax II (Gibco). The culture was maintained at 2% hematocrit using O+ human erythrocytes obtained from The Japanese Red Cross Society. The parasite culture was maintained in the environment of mixed gas (5% O 2 , 5% CO 2 , 90% N 2 ) at 37 °C. Parasitemia was determined using Giemsa staining or an XN-30 automated hematology analyzer (Sysmex) 58 . RNA-seq of drug-treated parasites The P. falciparum 3D7 culture was sorbitol synchronized 59 72 h and 36 h before magnetic purification 60 as a pre-synchronization step. The purified parasite was then added to new erythrocytes and allowed to invade for 3 h, followed by sorbitol synchronization to obtain a tightly synchronized parasite culture. After 8 h, 24 h, or 36 h from sorbitol synchronization for the ring, trophozoite, or schizont samples, respectively, 200 µL/well of the parasite culture was dispensed into 96-well plates with pre-dispensed compounds (200 nL/well of 10 mM DMSO solution using Echo 555 (Beckman Coulter)). The plates were incubated at 37 °C for 8 h and the total RNA from each sample was purified using NucleoSpin 96 (Takara) following the manufacturer’s instructions and stored at −80 °C. The cDNA library preparation from the purified RNA was performed using QIAseq UPX 3’ Transcriptome Kits (QIAGEN) following the manufacturer’s protocol, and Novogene performed next-generation sequencing. In vitro selection of Novel A resistant strains The Dd2 strain was cultured with 1.15 µM (5-fold EC 50 ) Novel A, with daily medium changes, for 5 days. After parasite clearance, parasitemia count and medium changes were performed three times a week until parasite regrowth and parasitemia reached above 1%. The resistant strain was cloned through limiting dilution of the obtained parasite culture. The growth inhibition of Novel A against ring-stage synchronized parasites was measured using the diaphorase-nitro blue tetrazolium assay, following previously described methods 29 . Whole genome sequencing of HDAC1 resistance mutants Genomic DNA of Novel A resistant strains was extracted using Qiagen Blood and Tissue Kit (Qiagen). Illumina library preparation and sequencing were performed in Novogene. The fastq files of whole genome sequencing were created and aligned to reference genome of Dd2 (PlasmoDB-64_PfalciparumDd2_Genome, which was downloaded from https://plasmodb.org/common/downloads/release-64/PfalciparumDd2/fasta/data/ ) using BWA-MEM 61 (v 0.7.18). SAMtools2 (v 1.18) was used for generating format converted files. Duplicate reads were removed by Picard tools MarkDuplicates (v 3.1.0), following with calling of single nucleotide variants (SNVs) which was performed using GATK HaplotypeCaller (v 4.4.0). The variants were narrowed down by removing the common variants of parent Dd2 strain and the resistant strains as well as filtered with QUAL > 1000, QD > 1.5, MQ > 30, DP> 5 using BCFtools 62 (v 1.18). The common variants of five resistant strains were highlighted and annotated by SnpEff 63 (v 5.1f) with genome annotation reference (PlasmoDB-64_PfalciparumDd2_AnnotatedCDSs). The variants that were evaluated as either HIGH or MODERATE were output. Regions of interest were inspected with Integrated Genome Viewer (v 2.16.2) 64 . Generation of HDAC1 resistance mutants P. falciparum Dd2 strain and pUF-Cas9-pre-sgRNA 65 obtained from Addgene were used for CRISPR-Cas9 genome editing. The sgRNA (Fasmac) was inserted into the BtgZI site of the plasmid. The synthesized donor DNAs purchased from Genewiz were amplified with primers containing 19 bp flanking plasmid homology sequences and inserted into the NcoI site using SLiCE 66 . The plasmid constructs were confirmed by Sanger sequencing. The plasmid was transfected into the parasite using a Gene Pulser Xcell (Bio-Rad). Fifty µg of plasmid was mixed with 150 µL of packed erythrocytes and brought to a final volume of 400 µL with cytomix (120 mM KCl, 0.2 mM CaCl 2 , 2 mM EGTA, 10 mM MgCl 2 , 25 mM HEPES, 5 mM K 2 HPO 4 , 5 mM KH 2 PO 4 ; pH 7.6 adjusted with KOH) 67 . The transfected erythrocytes were mixed with magnetically purified Dd2 strain and cultured until the parasitemia reached 5%. The transgenic parasites were selected in a complete medium containing 1.5 µM DSM1 (Sigma-Aldrich) for 10 days and then maintained in a drug-free complete medium until parasite regrowth. Limiting dilution was performed to obtain transgenic clones, which were genotyped by diagnostic PCR and Sanger sequencing. The sgRNA, synthesized donor DNAs, and Sanger sequencing primers are shown in Supplementary Table S2 . Generation of PfHDAC1/Compound 1 complex model The homology model of PfHDAC1 was constructed following the method described in Mukherjee P., et al. 68 . Briefly, the structures of the templates, HDLP (PDB code: 1C3R) and HDAC8 (PDB code: 1T64), were obtained from the Protein Data Bank (PDB). The protein sequence of PfHDAC1 (Accession No. Q9XYC7) was retrieved from UniProt, and the amino acid sequences of 1C3R and 1T64 were aligned with the Q9XYC7 sequence. The alignment was performed using the Multiple Sequence Viewer application in Maestro from the Schrödinger suite 69 , with reference to the supplementary data in the paper. Subsequently, the homology model of PfHDAC1 was constructed using the Prime application. The molecular docking model was generated by docking to the homology model using Glide. Data analysis The inhibition curves and EC 50 values were determined using GraphPad Prism (v 8.4.3 GraphPad Software, Inc.). The raw data from the next-generation sequencer were processed, demultiplexed, and aligned to P. falciparum 3D7 annotated transcripts (PlasmoDB v. 64) using CLC Genomics Workbench (v 23.0.4, QIAGEN). Sample quality control, differential expression, and normalization were performed using Python (v 3.12.0). For data cleaning, genes with no expression in any sample were removed, and samples with detection of 500 or fewer genes were excluded. Read counts were log normalized, and 1000 highly variable genes were selected using the expression and dispersion for further analysis. The most significant principal components were employed for clustering, which was performed using Scanpy (v 1.10.0) 70 . Data availability All sequence data that support the findings of this study have been deposited in NCBI sequence read archive (SRA) with the accession code PRJNA1226290. Code availability The custom code scripts used to analyze data for this study are available at https://github.com/NEKKEN-MID/Transcriptome_MoA . Author contributions R.I., T.S., and J.H. performed the experiments. R.I., T.S., J.H., and K.Y. analyzed the data. P.W. selected compounds. R.I., T.S., J.H., and D.K.I. wrote the paper. D.K.I., T.K., and K.H. directed the work. All authors directly participated in this work, including preparation of the manuscript and approval of the final version. All authors have read and agreed to the published version of the manuscript. Conflict of interest R.I., K.Y., K.H., and T.K. are employees of Shionogi & Co., Ltd. Nagasaki University and Shionogi & Co., Ltd., launched the Shionogi Global Infectious Diseases Division (SHINE) at the Institute of Tropical Medicine (NEKKEN), Nagasaki University. Under the collaboration agreement, Shionogi & Co., Ltd. provided the compounds pre-dispensed into the 96-well plates for the assay and contributed to discussions with the authors. Shionogi & Co., Ltd. did not influence the experimental design, data collection, data analysis, or interpretation. Supplementary information Download figure Open in new tab Figure S1. Efficacy of Novel A against obtained resistant strains. Dose-response curves of Novel A compound for the parental Dd2 line and five Novel A resistance clones (three technical replicates, mean ± SD). Download figure Open in new tab Figure S2. Whole genome sequencing of Novel A resistant strains. Three SNVs were identified among 5 resistant clones and L137F mutations of HDAC1 were visualized by Integrative Genomics Viewer (IGV). Download figure Open in new tab Figure S3. The result of diagnostic PCR to confirm insertion of donor DNA. Schematic representation of diagnostic PCR (left panel) and the results of diagnostic PCR for the parental Dd2 line and clones with donor DNA insertion (right panel). Download figure Open in new tab Figure S4. Efficacy of Novel A against transgenic L137F mutant strains. Dose-response curves of Novel A compound for the parental Dd2 line and five L137F mutant clones (three technical replicates, mean ± SD). View this table: View inline View popup Download powerpoint Table S1. List of tested compounds. (provided as an Excel file as well). The compounds with an MMV number were provided by MMV. Origins of other compounds are listed in Supplementary method. Chlamydocin-like compound is identical to molecule 3 of Traoré, M. D. M. et al. 45 , and Plasmepsin 2 inhibitor-like compound is identical to compound 8 of Hurley, K. A. et al. 71 , which is quite similar to plasmepsin 2 inhibitor 72 . View this table: View inline View popup Download powerpoint Table S2. Single guide RNA, donor DNAs, and Sanger sequence primers for plasmid construction and verifying obtained transgenic parasites. Acknowledgements The authors thank the Japanese Red Cross Society for providing human red blood cells and Dr. Ken-ichi Matsumura, Dr. Kenji Takaya, Ms. Rina Kaki, and Mr. Riku Ogasawara (Laboratory for Medicinal Chemistry Research, Shionogi & Co., Ltd.) for selecting Novel A-I compounds and synthesizing ELQ-300, M5717, MMV533, and ZY-19489. This work was supported by Shionogi & Co., Ltd., Osaka, Japan. Figure 1 was created in BioRender. Sakura, T. (2025) https://BioRender.com/q87j322 References ↵ World Health Organization . World malaria report 2024: addressing inequity in the global malaria response . 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Share Efficient identification of antimalarials with novel mechanism of action via multi-sample transcriptomic profiling Ryuta Ishii , Takaya Sakura , Jing Hong , Kazuya Yasuo , Kazunari Hattori , Paul A Willis , Teruhisa Kato , Daniel Ken Inaoka bioRxiv 2025.02.27.640686; doi: https://doi.org/10.1101/2025.02.27.640686 Share This Article: Copy Citation Tools Efficient identification of antimalarials with novel mechanism of action via multi-sample transcriptomic profiling Ryuta Ishii , Takaya Sakura , Jing Hong , Kazuya Yasuo , Kazunari Hattori , Paul A Willis , Teruhisa Kato , Daniel Ken Inaoka bioRxiv 2025.02.27.640686; doi: https://doi.org/10.1101/2025.02.27.640686 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7636) Biochemistry (17705) Bioengineering (13899) Bioinformatics (41967) Biophysics (21460) Cancer Biology (18600) Cell Biology (25526) Clinical Trials (138) Developmental Biology (13384) Ecology (19909) Epidemiology (2067) Evolutionary Biology (24326) Genetics (15613) Genomics (22512) Immunology (17740) Microbiology (40423) Molecular Biology (17193) Neuroscience (88645) Paleontology (667) Pathology (2835) Pharmacology and Toxicology (4825) Physiology (7647) Plant Biology (15159) Scientific Communication and Education (2046) Synthetic Biology (4302) Systems Biology (9825) Zoology (2271)