Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma

Cancer cells show remarkable plasticity and can switch lineages in response to the tumor microenvironment. Cellular plasticity drives invasiveness and metastasis and helps cancer cells to evade therapy by developing resistance to radiation and cytotoxic chemotherapy. Increased understanding of cell fate determination through epigenetic reprogramming is critical to discover how cancer cells achieve transcriptomic and phenotypic plasticity. Glioblastoma is a perfect example of cancer evolution where cells retain an inherent level of plasticity through activation or maintenance of progenitor developmental programs. However, the principles governing epigenetic drivers of cellular plasticity in glioblastoma remain poorly understood. Here, using machine learning (ML) we employ cross-patient prediction of transcript expression using a combination of epigenetic features (ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq) of glioblastoma stem cells (GSCs). We investigate different ML and deep learning (DL) models for this task and build our final pipeline using XGBoost. The model trained on one patient generalizes to another one suggesting that the epigenetic signals governing gene transcription are consistent across patients even if GSCs can be very different. We demonstrate that H3K27Ac is the epigenetic feature providing the most significant contribution to cross-patient prediction of gene expression. In addition, using H3K27Ac signals from patients-derived GSCs, we can predict gene expression of human neural crest stem cells suggesting a shared developmental epigenetic trajectory between subpopulations of these malignant and benign stem cells. Our cross-patient ML/DL models determine weighted patterns of influence of epigenetic marks on gene expression across patients with glioblastoma and between GSCs and neural crest stem cells. We propose that broader application of this analysis could reshape our view of glioblastoma tumor evolution and inform the design of new epigenetic targeting therapies.

Epigenetic Regulation and Chromatin Remodeling in Malaria Parasites

Plasmodium falciparum, the human malaria parasite, infects two hosts and various cell types, inducing distinct morphological and physiological changes in the parasite in response to different environmental conditions. These variations required the parasite to adapt and develop elaborate molecular mechanisms to ensure its spread and transmission. Recent findings have significantly improved our understanding of the regulation of gene expression in P. falciparum. Here, we provide an up-to-date overview of technologies used to highlight the transcriptomic adjustments occurring in the parasite throughout its life cycle. We also emphasize the complementary and complex epigenetic mechanisms regulating gene expression in malaria parasites. This review concludes with an outlook on the chromatin architecture, the remodeling systems, and how this 3D genome organization is critical in various biological processes.

Novel insights into the role of long non-coding RNA in the human malaria parasite, Plasmodium falciparum

The complex life cycle of Plasmodium falciparum requires coordinated gene expression regulation to allow host cell invasion, transmission, and immune evasion. Increasing evidence now suggests a major role for epigenetic mechanisms in gene expression in the parasite. In eukaryotes, many lncRNAs have been identified to be pivotal regulators of genome structure and gene expression. To investigate the regulatory roles of lncRNAs in P. falciparum we explore the intergenic lncRNA distribution in nuclear and cytoplasmic subcellular locations. Using nascent RNA expression profiles, we identify a total of 1768 lncRNAs, of which 718 (~41%) are novels in P. falciparum. The subcellular localization and stage-specific expression of several putative lncRNAs are validated using RNA-FISH. Additionally, the genome-wide occupancy of several candidate nuclear lncRNAs is explored using ChIRP. The results reveal that lncRNA occupancy sites are focal and sequence-specific with a particular enrichment for several parasite-specific gene families, including those involved in pathogenesis and sexual differentiation. Genomic and phenotypic analysis of one specific lncRNA demonstrate its importance in sexual differentiation and reproduction. Our findings bring a new level of insight into the role of lncRNAs in pathogenicity, gene regulation and sexual differentiation, opening new avenues for targeted therapeutic strategies against the deadly malaria parasite.

The role of long noncoding RNAs in malaria parasites

The human malaria parasites, including Plasmodium falciparum, persist as a major cause of global morbidity and mortality. The recent stalling of progress toward malaria elimination substantiates a need for novel interventions. Controlled gene expression is central to the parasite's numerous life cycle transformations and adaptation. With few specific transcription factors (TFs) identified, crucial roles for chromatin states and epigenetics in parasite transcription have become evident. Although many chromatin-modifying enzymes are known, less is known about which factors mediate their impacts on transcriptional variation. Like those of higher eukaryotes, long noncoding RNAs (lncRNAs) have recently been shown to have integral roles in parasite gene regulation. This review aims to summarize recent developments and key findings on the role of lncRNAs in P. falciparum.

Inhibitors of ApiAP2 protein DNA binding exhibit multistage activity against Plasmodium parasites

Plasmodium parasites are reliant on the Apicomplexan AP2 (ApiAP2) transcription factor family to regulate gene expression programs. AP2 DNA binding domains have no homologs in the human or mosquito host genomes, making them potential antimalarial drug targets. Using an in-silico screen to dock thousands of small molecules into the crystal structure of the AP2-EXP (Pf3D7_1466400) AP2 domain (PDB:3IGM), we identified putative AP2-EXP interacting compounds. Four compounds were found to block DNA binding by AP2-EXP and at least one additional ApiAP2 protein. Our top ApiAP2 competitor compound perturbs the transcriptome of P. falciparum trophozoites and results in a decrease in abundance of log₂ fold change > 2 for 50% (46/93) of AP2-EXP target genes. Additionally, two ApiAP2 competitor compounds have multi-stage anti-Plasmodium activity against blood and mosquito stage parasites. In summary, we describe a novel set of antimalarial compounds that interact with AP2 DNA binding domains. These compounds may be used for future chemical genetic interrogation of ApiAP2 proteins or serve as starting points for a new class of antimalarial therapeutics.

Plasmodium parasites are the causative agent of malaria, which resulted in over 600,000 deaths in 2021. Due to resistance arising for every antimalarial therapeutic deployed to date, new drug targets and druggable pathways must be explored. To address this concern, we used a molecular docking screen to predict competitors of DNA binding by the parasite specific family of Apicomplexan AP2 (ApiAP2) transcription factor proteins for testing in vitro and in vivo. We find that ApiAP2 competing compounds have antimalarial activity consistent with the disruption of gene regulation. This work will further our understanding of both the biological role and targetability of parasite transcriptional regulation.

Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

Histone Modification Landscapes as a Roadmap for Malaria Parasite Development

Plasmodium falciparum remains the deadliest parasite species in the world, responsible for 229 million cases of human malaria in 2019. The ability of the P. falciparum parasite to progress through multiple life cycle stages and thrive in diverse host and vector species hinges on sophisticated mechanisms of epigenetic regulation of gene expression. Emerging evidence indicates such epigenetic control exists in concentric layers, revolving around core histone post-translational modification (PTM) landscapes. Here, we provide a necessary update of recent epigenome research in malaria parasites, focusing specifically on the ability of dynamic histone PTM landscapes to orchestrate the divergent development and differentiation pathways in P. falciparum parasites. In addition to individual histone PTMs, we discuss recent findings that imply functional importance for combinatorial PTMs in P. falciparum parasites, representing an operational histone code. Finally, this review highlights the remaining gaps and provides strategies to address these to obtain a more thorough understanding of the histone modification landscapes that are at the center of epigenetic regulation in human malaria parasites.

Deep Learning Methods in Predicting Gene Expression Levels for the Malaria Parasite

Malaria is a mosquito-borne disease caused by single-celled blood parasites of the genus Plasmodium. The most severe cases of this disease are caused by the Plasmodium species, Falciparum. Once infected, a human host experiences symptoms of recurrent and intermittent fevers occurring over a time-frame of 48 hours, attributed to the synchronized developmental cycle of the parasite during the blood stage. To understand the regulated periodicity of Plasmodium falciparum transcription, this paper forecast and predict the P. falciparum gene transcription during its blood stage life cycle implementing a well-tuned recurrent neural network with gated recurrent units. Additionally, we also employ a spiking neural network to predict the expression levels of the P. falciparum gene. We provide results of this prediction on multiple genes including potential genes that express possible drug target enzymes. Our results show a high level of accuracy in being able to predict and forecast the expression levels of the different genes.

Differential contribution to gene expression prediction of histone modifications at enhancers or promoters

The ChIP-seq signal of histone modifications at promoters is a good predictor of gene expression in different cellular contexts, but whether this is also true at enhancers is not clear. To address this issue, we develop quantitative models to characterize the relationship of gene expression with histone modifications at enhancers or promoters. We use embryonic stem cells (ESCs), which contain a full spectrum of active and repressed (poised) enhancers, to train predictive models. As many poised enhancers in ESCs switch towards an active state during differentiation, predictive models can also be trained on poised enhancers throughout differentiation and in development. Remarkably, we determine that histone modifications at enhancers, as well as promoters, are predictive of gene expression in ESCs and throughout differentiation and development. Importantly, we demonstrate that their contribution to the predictive models varies depending on their location in enhancers or promoters. Moreover, we use a local regression (LOESS) to normalize sequencing data from different sources, which allows us to apply predictive models trained in a specific cellular context to a different one. We conclude that the relationship between gene expression and histone modifications at enhancers is universal and different from promoters. Our study provides new insight into how histone modifications relate to gene expression based on their location in enhancers or promoters.

Gene expression can be properly predicted by the ChIP-seq signal of histone modifications at promoters, but whether this is also true at enhancers is unclear. In this study we develop predictive models of gene expression that demonstrate the predictive power of histone modifications at enhancers in the context of mouse embryonic stem cells, during differentiation, and in animal development. Moreover, by assessing the contribution of each histone modification, we found that enhancer predictive models and promoter predictive models have different histone modification requirement. Therefore, different histone modifications relate better to enhancer or promoter function(s). Finally, by applying predictive models trained in a specific cellular context to a different one, we concluded that the relationship between gene expression and histone modifications at enhancers is universal.

Peculiarities of Plasmodium falciparum Gene Regulation and Chromatin Structure

The highly complex life cycle of the human malaria parasite, Plasmodium falciparum, is based on an orchestrated and tightly regulated gene expression program. In general, eukaryotic transcription regulation is determined by a combination of sequence-specific transcription factors binding to regulatory DNA elements and the packaging of DNA into chromatin as an additional layer. The accessibility of regulatory DNA elements is controlled by the nucleosome occupancy and changes of their positions by an active process called nucleosome remodeling. These epigenetic mechanisms are poorly explored in P. falciparum. The parasite genome is characterized by an extraordinarily high AT-content and the distinct architecture of functional elements, and chromatin-related proteins also exhibit high sequence divergence compared to other eukaryotes. Together with the distinct biochemical properties of nucleosomes, these features suggest substantial differences in chromatin-dependent regulation. Here, we highlight the peculiarities of epigenetic mechanisms in P. falciparum, addressing chromatin structure and dynamics with respect to their impact on transcriptional control. We focus on the specialized chromatin remodeling enzymes and discuss their essential function in P. falciparum gene regulation.

Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes

Long regulatory elements (LREs), such as CpG islands, polydA:dT tracts or AU-rich elements, are thought to play key roles in gene regulation but, as opposed to conventional binding sites of transcription factors, few methods have been proposed to formally and automatically characterize them. We present here a computational approach named DExTER (Domain Exploration To Explain gene Regulation) dedicated to the identification of candidate LREs (cLREs) and apply it to the analysis of the genomes of P. falciparum and other eukaryotes. Our analyses show that all tested genomes contain several cLREs that are somewhat conserved along evolution, and that gene expression can be predicted with surprising accuracy on the basis of these long regions only. Regulation by cLREs exhibits very different behaviours depending on species and conditions. In P. falciparum and other Apicomplexan organisms as well as in Dictyostelium discoideum, the process appears highly dynamic, with different cLREs involved at different phases of the life cycle. For multicellular organisms, the same cLREs are involved in all tissues, but a dynamic behavior is observed along embryonic development stages. In P. falciparum, whose genome is known to be strongly depleted of transcription factors, cLREs are predictive of expression with an accuracy above 70%, and our analyses show that they are associated with both transcriptional and post-transcriptional regulation signals. Moreover, we assessed the biological relevance of one LRE discovered by DExTER in P. falciparum using an in vivo reporter assay. The source code (python) of DExTER is available at https://gite.lirmm.fr/menichelli/DExTER.

Disentangling the Contribution of Each Descriptive Characteristic of Every Single Mutation to Its Functional Effects

Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimental data. The next natural questions to ask are if it is possible to gain insights into which attribute of the mutation contributes how much to the mutational effects and if one can develop universal rules for mapping the descriptors to mutational effects. In this work, we mainly address the former aspect using a framework of interpretable AI. Relations between the physicochemical descriptors and their contributions to the mutational effects are extracted by analyzing the data on 29,832 variants from eight systematic deep mutational scan studies. An opposite trend in the dependence of fitness and solubility on the distance of the amino acid from the catalytic sites could be extracted and quantified. The dependence of the mutational effect contributions on the position-specific scoring matrix (PSSM) score for the amino acid after mutation or the BLOSUM score of the substitution showed universal trends. Our attempts in the present work to explain the quantitative differences in the dependence on conservation and SASA across proteins were not successful. The work nevertheless brings transparency into the predictions and development of rules, and will hopefully lead to empirically uncovering the universalities among these rules.

From Genes to Transcripts, a Tightly Regulated Journey in Plasmodium

Over the past decade, we have witnessed significant progresses in understanding gene regulation in Apicomplexa including the human malaria parasite, Plasmodium falciparum. This parasite possesses the ability to convert in multiple stages in various hosts, cell types, and environments. Recent findings indicate that P. falciparum is talented at using efficient and complementary molecular mechanisms to ensure a tight control of gene expression at each stage of its life cycle. Here, we review the current understanding on the contribution of the epigenome, atypical transcription factors, and chromatin organization to regulate stage conversion in P. falciparum. The adjustment of these regulatory mechanisms occurring during the progression of the life cycle will be extensively discussed.

Dynamic Chromatin Structure and Epigenetics Control the Fate of Malaria Parasites

Multiple hosts and various life cycle stages prompt the human malaria parasite, Plasmodium falciparum, to acquire sophisticated molecular mechanisms to ensure its survival, spread, and transmission to its next host. To face these environmental challenges, increasing evidence suggests that the parasite has developed complex and complementary layers of regulatory mechanisms controlling gene expression. Here, we discuss the recent developments in the discovery of molecular components that contribute to cell replication and differentiation and highlight the major contributions of epigenetics, transcription factors, and nuclear architecture in controlling gene regulation and life cycle progression in Plasmodium spp.