Jelili Oyelade

Computational identification of signalling pathways in Plasmodium falciparum

Malaria is one of the worlds most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins.

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

Cluster analysis of Plasmodium RNA-seq time-course data identifies stage-specific co-regulated biological processes and regulatory elements

In this study, we interpreted RNA-seq time-course data of three developmental stages of Plasmodium species by clustering genes based on similarities in their expression profile without prior knowledge of the gene function. Functional enrichment of clusters of upregulated genes at specific time-points reveals potential targetable biological processes with information on their timings. We identified common consensus sequences that these clusters shared as potential points of coordinated transcriptional control. Five cluster groups showed upregulated profile patterns of biological interest. This included two clusters from the Intraerythrocytic Developmental Cycle (cluster 4 = 16 genes, and cluster 9 = 32 genes), one from the sexual development stage (cluster 2 = 851 genes), and two from the gamete-fertilization stage in the mosquito host (cluster 4 = 153 genes, and cluster 9 = 258 genes). The IDC expressed the least numbers of genes with only 1448 genes showing any significant activity of the 5020 genes (~29%) in the experiment. Gene ontology (GO) enrichment analysis of these clusters revealed a total of 671 uncharacterized genes implicated in 14 biological processes and components associated with these stages, some of which are currently being investigated as drug targets in on-going research. Five putative transcription regulatory binding motifs shared by members of each cluster were also identified, one of which was also identified in a previous study by separate researchers. Our study shows stage-specific genes and biological processes that may be important in antimalarial drug research efforts. In addition, timed-coordinated control of separate processes may explain the paucity of factors in parasites.

Modeling of the Glycolysis Pathway in Plasmodium falciparum using Petri Nets

Malaria is one of the deadly diseases, which affects a large number of the worlds population. The Plasmodium falciparum parasite during erythrocyte stages produces its energy mainly through anaerobic glycolysis, with pyruvate being converted into lactate. The glycolysis metabolism in P. falciparum is one of the important metabolic pathways of the parasite because the parasite is entirely dependent on it for energy. Also, several glycolytic enzymes have been proposed as drug targets. Petri nets (PNs) have been recognized as one of the important models for representing biological pathways. In this work, we built a qualitative PN model for the glycolysis pathway in P. falciparum and analyzed the model for its structural and quantitative properties using PN theory. From PlasmoCyc files, a total of 11 reactions were extracted; 6 of these were reversible and 5 were irreversible. These reactions were catalyzed by a total number of 13 enzymes. We extracted some of the essential reactions in the pathway using PN model, which are the possible drug targets without which the pathway cannot function. This model also helps to improve the understanding of the biological processes within this pathway.

In Silico Gene Regulatory Network of the Maurer’s Cleft Pathway in Plasmodium falciparum

The Maurers clefts (MCs) are very important for the survival of Plasmodium falciparum within an infected cell as they are induced by the parasite itself in the erythrocyte for protein trafficking. The MCs form an interesting part of the parasites biology as they shed more light on how the parasite remodels the erythrocyte leading to host pathogenesis and death. Here, we predicted and analyzed the genetic regulatory network of genes identified to belong to the MCs using regularized graphical Gaussian model. Our network shows four major activators, their corresponding target genes, and predicted binding sites. One of these master activators is the serine repeat antigen 5 (SERA5), predominantly expressed among the SERA multigene family of P. falciparum, which is one of the blood-stage malaria vaccine candidates. Our results provide more details about functional interactions and the regulation of the genes in the MCs’ pathway of P. falciparum.

Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed

Background Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases. Objective Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose. Methods The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed. Result We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information. Conclusion This review presents useful knowledge and future directions on the subject matter.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

Functional enrichment of human protein complexes in malaria parasites

This study extracted differentially expressed genes (DEG) from a RNA-Seq gene expression experiment of human red blood cells for both case and control. A protein interaction network (PIN) for the DEG at the red blood stage was extracted from protein interaction database. From the protein interaction network built, we identified 64 protein complexes using the molecular complex detection (MCODE) algorithm in Cytoscape. The functional enrichment of the identified protein complexes revealed functions related to rRNA processing, Ribosome biogenesis, RNA metabolic process, cellular process, Nucleic and metabolic process and much more which are active in the RBCs that could be open to invasion by Plasmodium falciparum.