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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

Left: An illustration of the simulated clinical observations for WT, TMP, CRP, and CXR associated with trajectories representing rapid disease progression (subject 1), minimal infection (subject 2), infection followed by recovery (subject 3), and gradually progressing infection (subject 4). Right: Estimated disease state trajectories for 100 simulated datasets (green curves) and the associated scaled true trajectory (black curve) for each of the four cases.
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Figure 1: Left: An illustration of the simulated clinical observations for WT, TMP, CRP, and CXR associated with trajectories representing rapid disease progression (subject 1), minimal infection (subject 2), infection followed by recovery (subject 3), and gradually progressing infection (subject 4). Right: Estimated disease state trajectories for 100 simulated datasets (green curves) and the associated scaled true trajectory (black curve) for each of the four cases.

Mentions: The results from our simulation study are shown in Figure 1. The 100 estimated trajectories for each of the four cases were compared to the true disease trajectory. As mentioned previously, the disease state is not identifiable and is unique only up to a constant. Therefore, the comparison was done in relative terms to see whether our modeling approach can recover the “shape” of the disease state trajectories. Specifically, suppose zi(t) is the true trajectory and ẑi(t) is any one of the 100 estimated trajectories for the ith case where i = 1, 2, 3, 4. We expect that there exists a scaling constant γ such that γ zi(t) ≈ ẑi(t) for all i, and the right panel of Figure 1 displays a plot of the 100 estimated trajectories ẑi(t) against the scaled true trajectory γ zi(t). In each case, our simulations faithfully recovered the underlying trajectory, thereby demonstrating the ability of our approach to accurately estimate model parameters in practice.


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)

Left: An illustration of the simulated clinical observations for WT, TMP, CRP, and CXR associated with trajectories representing rapid disease progression (subject 1), minimal infection (subject 2), infection followed by recovery (subject 3), and gradually progressing infection (subject 4). Right: Estimated disease state trajectories for 100 simulated datasets (green curves) and the associated scaled true trajectory (black curve) for each of the four cases.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Left: An illustration of the simulated clinical observations for WT, TMP, CRP, and CXR associated with trajectories representing rapid disease progression (subject 1), minimal infection (subject 2), infection followed by recovery (subject 3), and gradually progressing infection (subject 4). Right: Estimated disease state trajectories for 100 simulated datasets (green curves) and the associated scaled true trajectory (black curve) for each of the four cases.
Mentions: The results from our simulation study are shown in Figure 1. The 100 estimated trajectories for each of the four cases were compared to the true disease trajectory. As mentioned previously, the disease state is not identifiable and is unique only up to a constant. Therefore, the comparison was done in relative terms to see whether our modeling approach can recover the “shape” of the disease state trajectories. Specifically, suppose zi(t) is the true trajectory and ẑi(t) is any one of the 100 estimated trajectories for the ith case where i = 1, 2, 3, 4. We expect that there exists a scaling constant γ such that γ zi(t) ≈ ẑi(t) for all i, and the right panel of Figure 1 displays a plot of the 100 estimated trajectories ẑi(t) against the scaled true trajectory γ zi(t). In each case, our simulations faithfully recovered the underlying trajectory, thereby demonstrating the ability of our approach to accurately estimate model parameters in practice.

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