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Plasmodium falciparum gene expression measured directly from tissue during human infection.

Van Tyne D, Tan Y, Daily JP, Kamiza S, Seydel K, Taylor T, Mesirov JP, Wirth DF, Milner DA - Genome Med (2014)

Bottom Line: Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge.Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections.Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues.

View Article: PubMed Central - PubMed

Affiliation: Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA USA.

ABSTRACT

Background: During the latter half of the natural 48-h intraerythrocytic life cycle of human Plasmodium falciparum infection, parasites sequester deep in endothelium of tissues, away from the spleen and inaccessible to peripheral blood. These late-stage parasites may cause tissue damage and likely contribute to clinical disease, and a more complete understanding of their biology is needed. Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge.

Methods: We developed a custom nCounter® gene expression platform and used this platform to measure malaria parasite gene expression profiles in vitro and in vivo. We also used imputation to generate global transcriptional profiles and assessed differential gene expression between parasites growing in vitro and those recovered from malaria-infected patient tissues collected at autopsy.

Results: We demonstrate, for the first time, global transcriptional expression profiles from in vivo malaria parasites sequestered in human tissues. We found that parasite physiology can be correlated with in vitro data from an existing life cycle data set, and that parasites in sequestered tissues show an expected schizont-like transcriptional profile, which is conserved across tissues from the same patient. Imputation based on 60 landmark genes generated global transcriptional profiles that were highly correlated with genome-wide expression patterns from the same samples measured by microarray. Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections.

Conclusions: Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues.

No MeSH data available.


Related in: MedlinePlus

Imputation of global expression profiles based on landmarkP. falciparumgenes. (A) Model fitting. Spearman rank correlations between imputed and observed gene expression for 3,696 genes, based on imputation from varying numbers of probes. (B) Model testing. Spearman rank correlations between imputed and observed gene expression in 52 peripheral blood RNA samples, measured with both Affymetrix microarrays and nCounter®, before and after imputation. IAvA: imputed Affy vs. Affy (n = 3,969 genes); NvA: nCounter® vs. Affy (n = 328 genes); INvA: imputed nCounter® vs. Affy (n = 3,696 genes). (C) Cumulative distribution of median differences in rank abundance for 3,696 genes between gene expression imputed from nCounter® versus Affy, averaged over 52 peripheral blood RNA samples. (D) Cumulative distribution of Pearson correlations between imputed and measured gene expression, averaged over 52 peripheral blood RNA samples. (E) Correlation between imputed and observed gene expression scales with expression level. Pearson correlation versus quantile-normalized and log2-transformed gene expression for 3,696 genes averaged over 52 peripheral blood RNA samples.
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Fig3: Imputation of global expression profiles based on landmarkP. falciparumgenes. (A) Model fitting. Spearman rank correlations between imputed and observed gene expression for 3,696 genes, based on imputation from varying numbers of probes. (B) Model testing. Spearman rank correlations between imputed and observed gene expression in 52 peripheral blood RNA samples, measured with both Affymetrix microarrays and nCounter®, before and after imputation. IAvA: imputed Affy vs. Affy (n = 3,969 genes); NvA: nCounter® vs. Affy (n = 328 genes); INvA: imputed nCounter® vs. Affy (n = 3,696 genes). (C) Cumulative distribution of median differences in rank abundance for 3,696 genes between gene expression imputed from nCounter® versus Affy, averaged over 52 peripheral blood RNA samples. (D) Cumulative distribution of Pearson correlations between imputed and measured gene expression, averaged over 52 peripheral blood RNA samples. (E) Correlation between imputed and observed gene expression scales with expression level. Pearson correlation versus quantile-normalized and log2-transformed gene expression for 3,696 genes averaged over 52 peripheral blood RNA samples.

Mentions: We used imputation to calculate expression values for unknown genes using a small set of pre-defined genes (Methods). The imputation method was validated against parallel Affymetrix microarray data [9], and revealed that imputation based on 60 landmark genes could accurately approximate global transcriptional patterns, with minimal improvement in accuracy for imputation from more than 60 genes (Figure 3A). Imputed gene expression from 52 peripheral blood samples measured with the nCounter® platform showed good correlation with genome-wide Affymetrix microarray data gathered from the same samples (Figure 3B). To examine the accuracy of imputation on a gene-by-gene basis, we examined median differences in rank abundance (Figure 3C), and average Pearson correlation (Figure 3D), between imputed (from nCounter®) and observed (Affymetrix) expression of each gene among the 52 peripheral blood samples. Approximately 65% of imputed genes had a median difference in rank abundance lower than 500 (Figure 3C), and roughly 70% of genes had an average Pearson correlation above 0.3 (Figure 3D). Finally, the strength of correlation between imputed and observed values scaled with gene expression (Figure 3E), with more highly expressed genes having higher correlations and more lowly expressed genes having lower correlations.Figure 3


Plasmodium falciparum gene expression measured directly from tissue during human infection.

Van Tyne D, Tan Y, Daily JP, Kamiza S, Seydel K, Taylor T, Mesirov JP, Wirth DF, Milner DA - Genome Med (2014)

Imputation of global expression profiles based on landmarkP. falciparumgenes. (A) Model fitting. Spearman rank correlations between imputed and observed gene expression for 3,696 genes, based on imputation from varying numbers of probes. (B) Model testing. Spearman rank correlations between imputed and observed gene expression in 52 peripheral blood RNA samples, measured with both Affymetrix microarrays and nCounter®, before and after imputation. IAvA: imputed Affy vs. Affy (n = 3,969 genes); NvA: nCounter® vs. Affy (n = 328 genes); INvA: imputed nCounter® vs. Affy (n = 3,696 genes). (C) Cumulative distribution of median differences in rank abundance for 3,696 genes between gene expression imputed from nCounter® versus Affy, averaged over 52 peripheral blood RNA samples. (D) Cumulative distribution of Pearson correlations between imputed and measured gene expression, averaged over 52 peripheral blood RNA samples. (E) Correlation between imputed and observed gene expression scales with expression level. Pearson correlation versus quantile-normalized and log2-transformed gene expression for 3,696 genes averaged over 52 peripheral blood RNA samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4269068&req=5

Fig3: Imputation of global expression profiles based on landmarkP. falciparumgenes. (A) Model fitting. Spearman rank correlations between imputed and observed gene expression for 3,696 genes, based on imputation from varying numbers of probes. (B) Model testing. Spearman rank correlations between imputed and observed gene expression in 52 peripheral blood RNA samples, measured with both Affymetrix microarrays and nCounter®, before and after imputation. IAvA: imputed Affy vs. Affy (n = 3,969 genes); NvA: nCounter® vs. Affy (n = 328 genes); INvA: imputed nCounter® vs. Affy (n = 3,696 genes). (C) Cumulative distribution of median differences in rank abundance for 3,696 genes between gene expression imputed from nCounter® versus Affy, averaged over 52 peripheral blood RNA samples. (D) Cumulative distribution of Pearson correlations between imputed and measured gene expression, averaged over 52 peripheral blood RNA samples. (E) Correlation between imputed and observed gene expression scales with expression level. Pearson correlation versus quantile-normalized and log2-transformed gene expression for 3,696 genes averaged over 52 peripheral blood RNA samples.
Mentions: We used imputation to calculate expression values for unknown genes using a small set of pre-defined genes (Methods). The imputation method was validated against parallel Affymetrix microarray data [9], and revealed that imputation based on 60 landmark genes could accurately approximate global transcriptional patterns, with minimal improvement in accuracy for imputation from more than 60 genes (Figure 3A). Imputed gene expression from 52 peripheral blood samples measured with the nCounter® platform showed good correlation with genome-wide Affymetrix microarray data gathered from the same samples (Figure 3B). To examine the accuracy of imputation on a gene-by-gene basis, we examined median differences in rank abundance (Figure 3C), and average Pearson correlation (Figure 3D), between imputed (from nCounter®) and observed (Affymetrix) expression of each gene among the 52 peripheral blood samples. Approximately 65% of imputed genes had a median difference in rank abundance lower than 500 (Figure 3C), and roughly 70% of genes had an average Pearson correlation above 0.3 (Figure 3D). Finally, the strength of correlation between imputed and observed values scaled with gene expression (Figure 3E), with more highly expressed genes having higher correlations and more lowly expressed genes having lower correlations.Figure 3

Bottom Line: Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge.Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections.Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues.

View Article: PubMed Central - PubMed

Affiliation: Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA USA.

ABSTRACT

Background: During the latter half of the natural 48-h intraerythrocytic life cycle of human Plasmodium falciparum infection, parasites sequester deep in endothelium of tissues, away from the spleen and inaccessible to peripheral blood. These late-stage parasites may cause tissue damage and likely contribute to clinical disease, and a more complete understanding of their biology is needed. Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge.

Methods: We developed a custom nCounter® gene expression platform and used this platform to measure malaria parasite gene expression profiles in vitro and in vivo. We also used imputation to generate global transcriptional profiles and assessed differential gene expression between parasites growing in vitro and those recovered from malaria-infected patient tissues collected at autopsy.

Results: We demonstrate, for the first time, global transcriptional expression profiles from in vivo malaria parasites sequestered in human tissues. We found that parasite physiology can be correlated with in vitro data from an existing life cycle data set, and that parasites in sequestered tissues show an expected schizont-like transcriptional profile, which is conserved across tissues from the same patient. Imputation based on 60 landmark genes generated global transcriptional profiles that were highly correlated with genome-wide expression patterns from the same samples measured by microarray. Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections.

Conclusions: Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues.

No MeSH data available.


Related in: MedlinePlus