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Multidimensional Clusters of CD4+ T Cell Dysfunction Are Primarily Associated with the CD4/CD8 Ratio in Chronic HIV Infection.

Frederiksen J, Buggert M, Noyan K, Nowak P, Sönnerborg A, Lund O, Karlsson AC - PLoS ONE (2015)

Bottom Line: HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified.In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals.Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually.

View Article: PubMed Central - PubMed

Affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.

ABSTRACT
HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified. However, most studies to date have focused on single/double measurements of immune dysfunction, while the identification of pathological CD4+ T cell clusters that is highly associated to a specific biomarker for HIV disease remain less studied. Here, multi-parametric flow cytometry was used to investigate immune activation, exhaustion, and senescence of diverse maturation phenotypes of CD4+ T cells. The traditional method of manual data analysis was compared to a multidimensional clustering tool, FLOw Clustering with K (FLOCK) in two cohorts of 47 untreated HIV-infected individuals and 21 age and sex matched healthy controls. In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals. Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually. Multiple correlation analyses between laboratory parameters and pathological CD4+ clusters revealed that the CD4/CD8 ratio was the preeminent surrogate marker of CD4+ T cells dysfunction using all three methods. Increased frequencies of an early-differentiated CD4+ T cell cluster with high CD38, HLA-DR and PD-1 expression were best correlated (Rho = -0.80, P value = 1.96×10-11) with HIV disease progression as measured by the CD4/CD8 ratio. The novel approach described here can be used to identify cell clusters that distinguish healthy from HIV infected subjects and is biologically relevant for HIV disease progression. These results further emphasize that a simple measurement of the CD4/CD8 ratio is a useful biomarker for assessment of combined CD4+ T cell dysfunction in chronic HIV disease.

No MeSH data available.


Related in: MedlinePlus

Box plots of all manual and FLOCK gated populations between HIV-infected and -uninfected subjects.Box plot representation of summary results of the two groups generated by the three methods used to investigate the same HIV immunopathogenesis dataset, manual data analysis (left), FLOCK data analysis using the single HIV reference (middle) and FLOCK data analysis using the artificial reference (right). The data is presented in a box and whisker plot where the horizontal line in the box is the median population occupation, the edges of the boxes are the 25th and 75th percentiles of the population occupation and the ‘whiskers’ represent the 10th and 90th percentiles of the population occupation, and the dots indicate outliers. The purple and grey boxes represent the HIV+ and healthy control group, respectively, where the green dots indicate outliers that are AIDS patients. Results of the multiple Mann-Whitney tests followed by Bonferroni adjustments between the HIV and healthy control group for each gated population is shown using P value significance codes found directly above: 0 *** 0.001 ** 0.01 * 0.05.
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pone.0137635.g003: Box plots of all manual and FLOCK gated populations between HIV-infected and -uninfected subjects.Box plot representation of summary results of the two groups generated by the three methods used to investigate the same HIV immunopathogenesis dataset, manual data analysis (left), FLOCK data analysis using the single HIV reference (middle) and FLOCK data analysis using the artificial reference (right). The data is presented in a box and whisker plot where the horizontal line in the box is the median population occupation, the edges of the boxes are the 25th and 75th percentiles of the population occupation and the ‘whiskers’ represent the 10th and 90th percentiles of the population occupation, and the dots indicate outliers. The purple and grey boxes represent the HIV+ and healthy control group, respectively, where the green dots indicate outliers that are AIDS patients. Results of the multiple Mann-Whitney tests followed by Bonferroni adjustments between the HIV and healthy control group for each gated population is shown using P value significance codes found directly above: 0 *** 0.001 ** 0.01 * 0.05.

Mentions: The FLOCK and manually gated clusters were next correlated to numerous routine laboratory parameters that are used to assess the immune status of HIV-infected individuals. The initial step of the analysis involved the selection for the immunopathogenic clusters for each data set. This was done by performing multiple Mann-Whitney U tests to select for clusters that varied significantly, after Bonferroni correction for multiple testing. The pre-processing step resulted in six clusters (ten clusters before Bonferroni adjustments) for the manual dataset, seven clusters (21 clusters before Bonferroni adjustments) for the sFLOCK dataset and twelve clusters (25 clusters before Bonferroni adjustments) for the aFLOCK dataset. The phenotypes of the sFLOCK and aFLOCK results are shown in S1 Table. Notably, almost half of the original artificial reference dataset captured an immunopathogenic difference between the HIV+ and healthy control groups, whereas only a third of the sFLOCK dataset captured this immunopathogenesis. Box plot representations of these multiple Mann-Whitney U tests are illustrated in Fig 3.


Multidimensional Clusters of CD4+ T Cell Dysfunction Are Primarily Associated with the CD4/CD8 Ratio in Chronic HIV Infection.

Frederiksen J, Buggert M, Noyan K, Nowak P, Sönnerborg A, Lund O, Karlsson AC - PLoS ONE (2015)

Box plots of all manual and FLOCK gated populations between HIV-infected and -uninfected subjects.Box plot representation of summary results of the two groups generated by the three methods used to investigate the same HIV immunopathogenesis dataset, manual data analysis (left), FLOCK data analysis using the single HIV reference (middle) and FLOCK data analysis using the artificial reference (right). The data is presented in a box and whisker plot where the horizontal line in the box is the median population occupation, the edges of the boxes are the 25th and 75th percentiles of the population occupation and the ‘whiskers’ represent the 10th and 90th percentiles of the population occupation, and the dots indicate outliers. The purple and grey boxes represent the HIV+ and healthy control group, respectively, where the green dots indicate outliers that are AIDS patients. Results of the multiple Mann-Whitney tests followed by Bonferroni adjustments between the HIV and healthy control group for each gated population is shown using P value significance codes found directly above: 0 *** 0.001 ** 0.01 * 0.05.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4581870&req=5

pone.0137635.g003: Box plots of all manual and FLOCK gated populations between HIV-infected and -uninfected subjects.Box plot representation of summary results of the two groups generated by the three methods used to investigate the same HIV immunopathogenesis dataset, manual data analysis (left), FLOCK data analysis using the single HIV reference (middle) and FLOCK data analysis using the artificial reference (right). The data is presented in a box and whisker plot where the horizontal line in the box is the median population occupation, the edges of the boxes are the 25th and 75th percentiles of the population occupation and the ‘whiskers’ represent the 10th and 90th percentiles of the population occupation, and the dots indicate outliers. The purple and grey boxes represent the HIV+ and healthy control group, respectively, where the green dots indicate outliers that are AIDS patients. Results of the multiple Mann-Whitney tests followed by Bonferroni adjustments between the HIV and healthy control group for each gated population is shown using P value significance codes found directly above: 0 *** 0.001 ** 0.01 * 0.05.
Mentions: The FLOCK and manually gated clusters were next correlated to numerous routine laboratory parameters that are used to assess the immune status of HIV-infected individuals. The initial step of the analysis involved the selection for the immunopathogenic clusters for each data set. This was done by performing multiple Mann-Whitney U tests to select for clusters that varied significantly, after Bonferroni correction for multiple testing. The pre-processing step resulted in six clusters (ten clusters before Bonferroni adjustments) for the manual dataset, seven clusters (21 clusters before Bonferroni adjustments) for the sFLOCK dataset and twelve clusters (25 clusters before Bonferroni adjustments) for the aFLOCK dataset. The phenotypes of the sFLOCK and aFLOCK results are shown in S1 Table. Notably, almost half of the original artificial reference dataset captured an immunopathogenic difference between the HIV+ and healthy control groups, whereas only a third of the sFLOCK dataset captured this immunopathogenesis. Box plot representations of these multiple Mann-Whitney U tests are illustrated in Fig 3.

Bottom Line: HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified.In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals.Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually.

View Article: PubMed Central - PubMed

Affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.

ABSTRACT
HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified. However, most studies to date have focused on single/double measurements of immune dysfunction, while the identification of pathological CD4+ T cell clusters that is highly associated to a specific biomarker for HIV disease remain less studied. Here, multi-parametric flow cytometry was used to investigate immune activation, exhaustion, and senescence of diverse maturation phenotypes of CD4+ T cells. The traditional method of manual data analysis was compared to a multidimensional clustering tool, FLOw Clustering with K (FLOCK) in two cohorts of 47 untreated HIV-infected individuals and 21 age and sex matched healthy controls. In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals. Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually. Multiple correlation analyses between laboratory parameters and pathological CD4+ clusters revealed that the CD4/CD8 ratio was the preeminent surrogate marker of CD4+ T cells dysfunction using all three methods. Increased frequencies of an early-differentiated CD4+ T cell cluster with high CD38, HLA-DR and PD-1 expression were best correlated (Rho = -0.80, P value = 1.96×10-11) with HIV disease progression as measured by the CD4/CD8 ratio. The novel approach described here can be used to identify cell clusters that distinguish healthy from HIV infected subjects and is biologically relevant for HIV disease progression. These results further emphasize that a simple measurement of the CD4/CD8 ratio is a useful biomarker for assessment of combined CD4+ T cell dysfunction in chronic HIV disease.

No MeSH data available.


Related in: MedlinePlus