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Identification of biomarkers for tuberculosis susceptibility via integrated analysis of gene expression and longitudinal clinical data.

Luo Q, Mehra S, Golden NA, Kaushal D, Lacey MR - Front Genet (2014)

Bottom Line: The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach.Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression.Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.

View Article: PubMed Central - PubMed

Affiliation: Mathematics Department, Tulane University New Orleans, LA, USA.

ABSTRACT
Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis (Mtb) that affects millions of people worldwide. The majority of individuals who are exposed to Mtb develop latent infections, in which an immunological response to Mtb antigens is present but there is no clinical evidence of disease. Because currently available tests cannot differentiate latent individuals who are at low risk from those who are highly susceptible to developing active disease, there is considerable interest in the identification of diagnostic biomarkers that can predict reactivation of latent TB. We present results from our analysis of a controlled longitudinal experiment in which a group of rhesus macaques were exposed to a low dose of Mtb to study their progression to latent infection or active disease. Subsets of the animals were then euthanized at scheduled time points, and granulomas taken from their lungs were assayed for gene expression using microarrays. The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach. Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression. Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.

No MeSH data available.


Related in: MedlinePlus

Hierarchical clustering of unique probe IDs based on fitted model coefficients. All coefficients were scaled prior to analysis to assign equal weight to each covariate. Red values denote coefficients that are positively associated with increases in the given covariates at the α = 0.05 significance level, while values in blue denote significant negatively associated coefficients.
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Figure 4: Hierarchical clustering of unique probe IDs based on fitted model coefficients. All coefficients were scaled prior to analysis to assign equal weight to each covariate. Red values denote coefficients that are positively associated with increases in the given covariates at the α = 0.05 significance level, while values in blue denote significant negatively associated coefficients.

Mentions: Hierarchical clustering was performed on the scaled set of estimated model parameters to determine subsets of probe IDs that had the most similar characteristics with respect to their fitted models. Following visual inspection, we determined that 6 clusters were sufficient to adequately classify the results, as shown in Figure 4.


Identification of biomarkers for tuberculosis susceptibility via integrated analysis of gene expression and longitudinal clinical data.

Luo Q, Mehra S, Golden NA, Kaushal D, Lacey MR - Front Genet (2014)

Hierarchical clustering of unique probe IDs based on fitted model coefficients. All coefficients were scaled prior to analysis to assign equal weight to each covariate. Red values denote coefficients that are positively associated with increases in the given covariates at the α = 0.05 significance level, while values in blue denote significant negatively associated coefficients.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109430&req=5

Figure 4: Hierarchical clustering of unique probe IDs based on fitted model coefficients. All coefficients were scaled prior to analysis to assign equal weight to each covariate. Red values denote coefficients that are positively associated with increases in the given covariates at the α = 0.05 significance level, while values in blue denote significant negatively associated coefficients.
Mentions: Hierarchical clustering was performed on the scaled set of estimated model parameters to determine subsets of probe IDs that had the most similar characteristics with respect to their fitted models. Following visual inspection, we determined that 6 clusters were sufficient to adequately classify the results, as shown in Figure 4.

Bottom Line: The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach.Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression.Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.

View Article: PubMed Central - PubMed

Affiliation: Mathematics Department, Tulane University New Orleans, LA, USA.

ABSTRACT
Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis (Mtb) that affects millions of people worldwide. The majority of individuals who are exposed to Mtb develop latent infections, in which an immunological response to Mtb antigens is present but there is no clinical evidence of disease. Because currently available tests cannot differentiate latent individuals who are at low risk from those who are highly susceptible to developing active disease, there is considerable interest in the identification of diagnostic biomarkers that can predict reactivation of latent TB. We present results from our analysis of a controlled longitudinal experiment in which a group of rhesus macaques were exposed to a low dose of Mtb to study their progression to latent infection or active disease. Subsets of the animals were then euthanized at scheduled time points, and granulomas taken from their lungs were assayed for gene expression using microarrays. The clinical profiles associated with the animals following Mtb exposure revealed considerable variability, and we developed models for the disease trajectory for each subject using a Bayesian hierarchical B-spline approach. Disease severity estimates were derived from these fitted curves and included as covariates in linear models to identify genes significantly associated with disease progression. Our results demonstrate that the incorporation of clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers for TB susceptibility.

No MeSH data available.


Related in: MedlinePlus