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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 schematic illustration of the LIGAP method. LIGAP implements a statistical analysis of multiple lineage commitment, as shown here, or other time-course profiles. LIGAP considers all possible comparisons between cell subsets, quantifies a probabilistic model fit for each partition, and summarizes the individual probabilities into differential regulation scores. The case of three lineages, Th0, Th1 and Th2 is shown.
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Figure 1: A schematic illustration of the LIGAP method. LIGAP implements a statistical analysis of multiple lineage commitment, as shown here, or other time-course profiles. LIGAP considers all possible comparisons between cell subsets, quantifies a probabilistic model fit for each partition, and summarizes the individual probabilities into differential regulation scores. The case of three lineages, Th0, Th1 and Th2 is shown.

Mentions: The discovery of condition specific genes at the level of gene expression is an important first step in systems biology studies. To capture temporal aspects of biological processes, such as cell differentiation, gene expression is commonly measured over time. We developed a novel model-based method LIGAP for detecting and visualizing changes between multiple lineage commitment time-course profiles. Briefly, for each gene at a time, our method carries out all comparisons between different cell subsets. In the case of Th0, Th1 and Th2 lineages, we assess all 5 alternatives; (i) “Th0, Th1, Th2 time-course profiles are all similar” (hypothesis H1), (ii) “Th0 and Th1 are similar and Th2 is different” (hypothesis H2), (iii) “Th0 and Th2 are similar and Th1 is different” (hypothesis H3), (iv) “Th1 and Th2 are similar and Th0 is different” (hypothesis H4), and (v) “Th0, Th1, and Th2 are all different from each other” (hypothesis H5). LIGAP comparisons and quantifications are illustrated in Figure 1. The modeling is done using Gaussian processes, which provide a flexible and nonparametric approach for estimating smooth differentiation profiles. With the help of Bayesian statistics, we can quantify differences and similarities by assigning posterior probabilities for all the different profile comparisons between polarizing cell subsets. The problem can be seen as a model selection problem, where different comparisons are thought of as different model structures (H1,… H5) and, given experimental lineage commitment profile data D, the marginal likelihood P(D / Hj), j=1,…,5, is used to score different models. Using the Bayes’ theorem, the marginal likelihoods can be converted into posterior probabilities of different hypothesis. These Bayesian model scores can be used further to quantify genes, which are specific for a certain lineage. For example, the probability of a gene being differentially regulated in Th2 lineage, i.e., score for Th2 is P(“Gene is differentially regulated in Th2” / D) = P(“Th0 and Th1 are similar and Th2 is different” / D) + P(“Th0, Th1 and Th2 are all different” / D) = P(H2/ D) + P(H5/ D). Genes which are differentially regulated in each of the conditions can be found by quantifying the probabilities P(“Th0, Th1, and Th2 are all different from each other” / D) = P(H5/ D) or the three probabilities of differential regulation. Each score quantifies the amount of differential regulation, which refers to distinct temporal behavior from other lineages. The methodology generalizes to any number of lineages/conditions. Our method copes with non-uniform sampling, is able to model non-stationary biological processes (where e.g. changes are fast at the beginning of the experiment and slow at the end), can make comparisons for paired samples, and can carry out the analysis with different number of replicates and missing data. Importantly, the method affords comparison of more than two conditions of interest and is widely applicable to different experimental platforms.


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 schematic illustration of the LIGAP method. LIGAP implements a statistical analysis of multiple lineage commitment, as shown here, or other time-course profiles. LIGAP considers all possible comparisons between cell subsets, quantifies a probabilistic model fit for each partition, and summarizes the individual probabilities into differential regulation scores. The case of three lineages, Th0, Th1 and Th2 is shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: A schematic illustration of the LIGAP method. LIGAP implements a statistical analysis of multiple lineage commitment, as shown here, or other time-course profiles. LIGAP considers all possible comparisons between cell subsets, quantifies a probabilistic model fit for each partition, and summarizes the individual probabilities into differential regulation scores. The case of three lineages, Th0, Th1 and Th2 is shown.
Mentions: The discovery of condition specific genes at the level of gene expression is an important first step in systems biology studies. To capture temporal aspects of biological processes, such as cell differentiation, gene expression is commonly measured over time. We developed a novel model-based method LIGAP for detecting and visualizing changes between multiple lineage commitment time-course profiles. Briefly, for each gene at a time, our method carries out all comparisons between different cell subsets. In the case of Th0, Th1 and Th2 lineages, we assess all 5 alternatives; (i) “Th0, Th1, Th2 time-course profiles are all similar” (hypothesis H1), (ii) “Th0 and Th1 are similar and Th2 is different” (hypothesis H2), (iii) “Th0 and Th2 are similar and Th1 is different” (hypothesis H3), (iv) “Th1 and Th2 are similar and Th0 is different” (hypothesis H4), and (v) “Th0, Th1, and Th2 are all different from each other” (hypothesis H5). LIGAP comparisons and quantifications are illustrated in Figure 1. The modeling is done using Gaussian processes, which provide a flexible and nonparametric approach for estimating smooth differentiation profiles. With the help of Bayesian statistics, we can quantify differences and similarities by assigning posterior probabilities for all the different profile comparisons between polarizing cell subsets. The problem can be seen as a model selection problem, where different comparisons are thought of as different model structures (H1,… H5) and, given experimental lineage commitment profile data D, the marginal likelihood P(D / Hj), j=1,…,5, is used to score different models. Using the Bayes’ theorem, the marginal likelihoods can be converted into posterior probabilities of different hypothesis. These Bayesian model scores can be used further to quantify genes, which are specific for a certain lineage. For example, the probability of a gene being differentially regulated in Th2 lineage, i.e., score for Th2 is P(“Gene is differentially regulated in Th2” / D) = P(“Th0 and Th1 are similar and Th2 is different” / D) + P(“Th0, Th1 and Th2 are all different” / D) = P(H2/ D) + P(H5/ D). Genes which are differentially regulated in each of the conditions can be found by quantifying the probabilities P(“Th0, Th1, and Th2 are all different from each other” / D) = P(H5/ D) or the three probabilities of differential regulation. Each score quantifies the amount of differential regulation, which refers to distinct temporal behavior from other lineages. The methodology generalizes to any number of lineages/conditions. Our method copes with non-uniform sampling, is able to model non-stationary biological processes (where e.g. changes are fast at the beginning of the experiment and slow at the end), can make comparisons for paired samples, and can carry out the analysis with different number of replicates and missing data. Importantly, the method affords comparison of more than two conditions of interest and is widely applicable to different experimental platforms.

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