METHODS: A total of 46 csp genes were subjected to polymerase chain reaction amplification. The genes were obtained from P. knowlesi isolates collected from different divisions of Sabah, Malaysian Borneo, and Peninsular Malaysia. The targeted gene fragments were cloned into a commercial vector and sequenced, and a phylogenetic tree was constructed while incorporating 168 csp sequences retrieved from the GenBank database. The genetic diversity and natural evolution of the csp sequences were analysed using MEGA6 and DnaSP ver. 5.10.01. A genealogical network of the csp haplotypes was generated using NETWORK ver. 4.6.1.3.
RESULTS: The phylogenetic analysis revealed indistinguishable clusters of P. knowlesi isolates across different geographic regions, including Malaysian Borneo and Peninsular Malaysia. Nucleotide analysis showed that the csp non-repeat regions of zoonotic P. knowlesi isolates obtained in this study underwent purifying selection with population expansion, which was supported by extensive haplotype sharing observed between humans and macaques. Novel variations were observed in the C-terminal non-repeat region of csp.
CONCLUSIONS: The csp non-repeat regions are relatively conserved and there is no distinct cluster of P. knowlesi isolates from Malaysian Borneo and Peninsular Malaysia. Distinctive variation data obtained in the C-terminal non-repeat region of csp could be beneficial for the design and development of vaccines to treat P. knowlesi.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.