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Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites.

Liu X, Huang Y, Liang J, Zhang S, Li Y, Wang J, Shen Y, Xu Z, Zhao Y - BMC Bioinformatics (2014)

Bottom Line: We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used.We integrated gene expression data to improve prediction accuracy and to reduce false positives.The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathogenic Biology, The Fourth Military Medical University, Xi'an, 710032, P. R. China. liu_xuewu@hotmail.com.

ABSTRACT

Background: The invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.

Results: In this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.

Conclusions: We predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.

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Identification of malaria parasite genes highly expressed at schizont stage. (a) PfAMA1 expression signal was converted from a time domain into a frequency domain by FFT. (b) Periodic genes identified using FFT were organized in increasing order of peak expression time. (c) Venn diagram for number of proteins found at malaria parasite schizont stage and that of proteins predicted to be located in the parasite membrane.
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Fig3: Identification of malaria parasite genes highly expressed at schizont stage. (a) PfAMA1 expression signal was converted from a time domain into a frequency domain by FFT. (b) Periodic genes identified using FFT were organized in increasing order of peak expression time. (c) Venn diagram for number of proteins found at malaria parasite schizont stage and that of proteins predicted to be located in the parasite membrane.

Mentions: Considering that the IDC gene expression of P. falciparum was highly dynamic, we performed fast Fourier transform (FFT) analysis to extract the periodic genes that were highly expressed in the schizont stage. FFT is an approach used to compute a discrete Fourier transform of a finite length signal; this approach has also been applied for periodic genes whose expressions oscillate at one or more frequency. FFT converts an expression signal in a time domain into a frequency domain. Significant frequencies could be obtained by using this method. For example, the apical merozoite antigen (PfAMA1) plays a critical role in parasite invasion [29]. This gene was highly expressed in the schizont stage (Figure 3a, left panel). By conducting an FFT analysis, we obtained the amplitude of the AMA1 expression signal at each frequency (Figure 3a, right panel). Using this method, the expression profiles that are inherently noisy or lack differential expression can be filtered out to obtain the periodic genes.Figure 3


Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites.

Liu X, Huang Y, Liang J, Zhang S, Li Y, Wang J, Shen Y, Xu Z, Zhao Y - BMC Bioinformatics (2014)

Identification of malaria parasite genes highly expressed at schizont stage. (a) PfAMA1 expression signal was converted from a time domain into a frequency domain by FFT. (b) Periodic genes identified using FFT were organized in increasing order of peak expression time. (c) Venn diagram for number of proteins found at malaria parasite schizont stage and that of proteins predicted to be located in the parasite membrane.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4265449&req=5

Fig3: Identification of malaria parasite genes highly expressed at schizont stage. (a) PfAMA1 expression signal was converted from a time domain into a frequency domain by FFT. (b) Periodic genes identified using FFT were organized in increasing order of peak expression time. (c) Venn diagram for number of proteins found at malaria parasite schizont stage and that of proteins predicted to be located in the parasite membrane.
Mentions: Considering that the IDC gene expression of P. falciparum was highly dynamic, we performed fast Fourier transform (FFT) analysis to extract the periodic genes that were highly expressed in the schizont stage. FFT is an approach used to compute a discrete Fourier transform of a finite length signal; this approach has also been applied for periodic genes whose expressions oscillate at one or more frequency. FFT converts an expression signal in a time domain into a frequency domain. Significant frequencies could be obtained by using this method. For example, the apical merozoite antigen (PfAMA1) plays a critical role in parasite invasion [29]. This gene was highly expressed in the schizont stage (Figure 3a, left panel). By conducting an FFT analysis, we obtained the amplitude of the AMA1 expression signal at each frequency (Figure 3a, right panel). Using this method, the expression profiles that are inherently noisy or lack differential expression can be filtered out to obtain the periodic genes.Figure 3

Bottom Line: We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used.We integrated gene expression data to improve prediction accuracy and to reduce false positives.The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathogenic Biology, The Fourth Military Medical University, Xi'an, 710032, P. R. China. liu_xuewu@hotmail.com.

ABSTRACT

Background: The invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.

Results: In this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.

Conclusions: We predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.

Show MeSH
Related in: MedlinePlus