Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.

Investigating the role that host erythrocyte proteins play in malaria infection is hampered by the genetic intractability of this anucleate cell. Here we report that reticulocytes derived through in vitro differentiation of an enucleation-competent immortalized erythroblast cell line (BEL-A) support both successful invasion and intracellular development of the malaria parasite Plasmodium falciparum . Using CRISPR-mediated gene knockout and subsequent complementation, we validate an essential role for the erythrocyte receptor basigin in P. falciparum invasion and demonstrate rescue of invasive susceptibility by receptor re-expression. Successful invasion of reticulocytes complemented with a truncated mutant excludes a functional role for the basigin cytoplasmic domain during invasion. Contrastingly, knockout of cyclophilin B, reported to participate in invasion and interact with basigin, did not impact invasive susceptibility of reticulocytes. These data establish the use of reticulocytes derived from immortalized erythroblasts as a powerful model system to explore hypotheses regarding host receptor requirements for P. falciparum invasion.

Mosquitoes infected with malaria parasites have demonstrated altered behaviour that may increase the probability of parasite transmission. Here, we examine the responses of the olfactory system in Plasmodium falciparum infected Anopheles gambiae , Plasmodium berghei infected Anopheles stephensi , and P . berghei infected An . gambiae . Infected and uninfected mosquitoes showed differential responses to compounds in human odour using electroantennography coupled with gas chromatography (GC-EAG), with 16 peaks triggering responses only in malaria-infected mosquitoes (at oocyst, sporozoite or both stages). A selection of key compounds were examined with EAG, and responses showed differences in the detection thresholds of infected and uninfected mosquitoes to compounds including lactic acid, tetradecanoic acid and benzothiazole, suggesting that the changes in sensitivity may be the reason for differential attraction and biting at the oocyst and sporozoite stages. Importantly, the different cross-species comparisons showed varying sensitivities to compounds, with P . falciparum infected An . gambiae differing from P . berghei infected An . stephensi , and P . berghei infected An . gambiae more similar to the P . berghei infected An . stephensi . These differences in sensitivity may reflect long-standing evolutionary relationships between specific Plasmodium and Anopheles species combinations. This highlights the importance of examining different species interactions in depth to fully understand the impact of malaria infection on mosquito olfactory behaviour.

Artemisinin-based combination therapies have been crucial in driving down the global burden of malaria, the world’s largest parasitic killer. However, their efficacy is now threatened by the emergence of resistance in Southeast Asia and sub-Saharan Africa.

Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. Such methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human-and machine-labelled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semisupervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.One Sentence Summary: A machine learning approach to classifying normal and aberrant cell morphology from plate-based imaging of mixed malaria parasite cultures, facilitating clustering of drugs by mechanism of action. Ashdown et alPage 3Cell-based screens have significantly advanced our ability to find new drugs (1). However, most screens are unable to predict the mechanism of action (MoA) of identified hits, necessitating years of follow up post-discovery. In addition, even the most complex screens frequently discover hits against cellular processes that are already targeted (2). Limitations in finding new targets is becoming especially important in the face of rising antimicrobial resistance across bacterial and parasitic infections. This rise in resistance is driving increasing demand for screens that can intuitively find new antimicrobials with novel MoAs. Demand for innovation in drug discovery is exemplified in efforts targeting Plasmodium falciparum, the parasite that causes malaria. Malaria disease continues to be a leading cause of childhood mortality, killing nearly half a million children each year (3). Drug resistance has emerged to every major antimalarial to date including rapidly emerging resistance to frontline artemisinin-based combination therapies (4). Whilst there is a healthy pipeline of developmental antimalarials, many target common processes (5) and may therefore fail quickly because of prevalent crossresistance. Thus, solutions are urgently sought for the rapid identification of new drugs that have a novel MoA at the time of discovery. Parasite cell morphology within the human red blood cell (RBC) contains inherent MoA predictive capacity. Intracellular parasite morphology can distinguish broad stages along the developmental continuum of the asexual parasite (responsible for all disease pathology). This developmental continuum includes e...