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An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation.

Aijö T, Edelman SM, Lönnberg T, Larjo A, Kallionpää H, Tuomela S, Engström E, Lahesmaa R, Lähdesmäki H - BMC Genomics (2012)

Bottom Line: All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource.It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes.Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.

ABSTRACT

Background: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior.

Results: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource.

Conclusions: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

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A two-dimensional PCA visualization of the differentially regulated genes among Th lineages. Each point corresponds to a differentially regulated gene. The color of the data point indicate the subset specificity as indicated in the figure. Four axes (black arrows) corresponding to different polarizing cell subsets are shown as a reference. The used probability cut-off for each class was 0.9.
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Figure 2: A two-dimensional PCA visualization of the differentially regulated genes among Th lineages. Each point corresponds to a differentially regulated gene. The color of the data point indicate the subset specificity as indicated in the figure. Four axes (black arrows) corresponding to different polarizing cell subsets are shown as a reference. The used probability cut-off for each class was 0.9.

Mentions: We analyzed the genome-wide gene expression time-course data from Th0, Th1 and Th2 lineages using LIGAP. For all genes, the method outputs the posterior probability values for each of the five hypotheses and also computes the scores for genes being differentially regulated in the Th subsets. An overview of the differentially regulated genes is shown in Figure 2, where the four-dimensional data points representing the condition specificities are projected into a plane using the principle component analysis (PCA). This demonstrates the convenience of the presented method as we are able to reduce highly complex data into a meaningful four-dimensional representation using a unified probabilistic framework. In Figure 2 individual points represent different genes and every gene is associated with four probabilities: P(“Differentially regulated in Th0”), P(“Differentially regulated in Th1”), P(“Differentially regulated in Th2”), and P(“Th0, Th1, and Th2 are all different from each other”). Note that IFNγ has the three probabilities P(“Differentially regulated in Th0”), P(“Differentially regulated in Th1”), and P(“Differentially regulated in Th2”) close to unity because the probability P(“Th0, Th1, and Th2 are all different from each other”) is close to unity. We set a criterion (P > 0.9) for the probabilities to call the differentially regulated probe sets; this threshold is in accordance with the Jeffrey’s interpretation of “strong evidence” for the Bayes factor [18]. In addition, we required a minimum of two-fold change between a lineage and all other lineages at some time point during the differentiation for a gene to be called as differentially regulated. The top 49 and 50 gene symbols for Th1 and Th2 lineages, respectively, are listed in Table 1, whereas, the Th0 list includes only 18 genes. In a Additional file 1: Figure S1 are depicted two additional examples illustrating the advantage of considering temporal correlation in gene expression and thus improving the sensitivity of detecting consistent yet subtle changes. In addition, we repeated the analysis using EDGE [12] and TANOVA [13] methods using the default parameter values. TANOVA identified almost twice as many genes (~1,300) to be differentially regulated as LIGAP or TANOVA (~700). A comparison of the obtained ranked lists revealed a higher correspondence between the lists produced by LIGAP and EDGE than with the list produced by TANOVA (data not shown).


An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation.

Aijö T, Edelman SM, Lönnberg T, Larjo A, Kallionpää H, Tuomela S, Engström E, Lahesmaa R, Lähdesmäki H - BMC Genomics (2012)

A two-dimensional PCA visualization of the differentially regulated genes among Th lineages. Each point corresponds to a differentially regulated gene. The color of the data point indicate the subset specificity as indicated in the figure. Four axes (black arrows) corresponding to different polarizing cell subsets are shown as a reference. The used probability cut-off for each class was 0.9.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3526425&req=5

Figure 2: A two-dimensional PCA visualization of the differentially regulated genes among Th lineages. Each point corresponds to a differentially regulated gene. The color of the data point indicate the subset specificity as indicated in the figure. Four axes (black arrows) corresponding to different polarizing cell subsets are shown as a reference. The used probability cut-off for each class was 0.9.
Mentions: We analyzed the genome-wide gene expression time-course data from Th0, Th1 and Th2 lineages using LIGAP. For all genes, the method outputs the posterior probability values for each of the five hypotheses and also computes the scores for genes being differentially regulated in the Th subsets. An overview of the differentially regulated genes is shown in Figure 2, where the four-dimensional data points representing the condition specificities are projected into a plane using the principle component analysis (PCA). This demonstrates the convenience of the presented method as we are able to reduce highly complex data into a meaningful four-dimensional representation using a unified probabilistic framework. In Figure 2 individual points represent different genes and every gene is associated with four probabilities: P(“Differentially regulated in Th0”), P(“Differentially regulated in Th1”), P(“Differentially regulated in Th2”), and P(“Th0, Th1, and Th2 are all different from each other”). Note that IFNγ has the three probabilities P(“Differentially regulated in Th0”), P(“Differentially regulated in Th1”), and P(“Differentially regulated in Th2”) close to unity because the probability P(“Th0, Th1, and Th2 are all different from each other”) is close to unity. We set a criterion (P > 0.9) for the probabilities to call the differentially regulated probe sets; this threshold is in accordance with the Jeffrey’s interpretation of “strong evidence” for the Bayes factor [18]. In addition, we required a minimum of two-fold change between a lineage and all other lineages at some time point during the differentiation for a gene to be called as differentially regulated. The top 49 and 50 gene symbols for Th1 and Th2 lineages, respectively, are listed in Table 1, whereas, the Th0 list includes only 18 genes. In a Additional file 1: Figure S1 are depicted two additional examples illustrating the advantage of considering temporal correlation in gene expression and thus improving the sensitivity of detecting consistent yet subtle changes. In addition, we repeated the analysis using EDGE [12] and TANOVA [13] methods using the default parameter values. TANOVA identified almost twice as many genes (~1,300) to be differentially regulated as LIGAP or TANOVA (~700). A comparison of the obtained ranked lists revealed a higher correspondence between the lists produced by LIGAP and EDGE than with the list produced by TANOVA (data not shown).

Bottom Line: All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource.It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes.Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.

ABSTRACT

Background: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior.

Results: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource.

Conclusions: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Show MeSH
Related in: MedlinePlus