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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

Heatmap of expression profiles for 106 genes significantly associated with post-exposure time T and/or final severity score S and included in a set of 393 transcripts that were previously reported to effectively discriminate between cases of active and latent TB in blood samples (Berry et al., 2010). Hierarchical clustering of the samples on the basis of this subset separates 5 of the 6 least severe cases in our sample from the remaining 9 cases.
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Figure 6: Heatmap of expression profiles for 106 genes significantly associated with post-exposure time T and/or final severity score S and included in a set of 393 transcripts that were previously reported to effectively discriminate between cases of active and latent TB in blood samples (Berry et al., 2010). Hierarchical clustering of the samples on the basis of this subset separates 5 of the 6 least severe cases in our sample from the remaining 9 cases.

Mentions: To put our results in a larger context, we compared our transcription profiles with those identified in recent studies seeking to identify TB expression biomarkers. In 2010, researchers published a list of 393 transcripts that were found to effectively discriminate between cases of active and latent TB in blood samples (Berry et al., 2010). We downloaded this list, which included 376 distinct gene IDs, and matched it to our set of 1950 genes that were significantly associated with T and/or S. We found an overlap of 106 genes and applied hierarchical clustering to the expression profiles associated with this subset. The results, shown in Figure 6, clearly separated five of our subjects from the others. Interestingly, these correspond to five of the six lowest scoring cases using our fitted severity models, all of which had very low CXR scores, 0 CRP values, and low or undetectable levels of bacteria upon autopsy. A very low-scoring subject on our severity scale that was not included in this group was CA75, an animal which demonstrated considerable weight loss despite an absence of other clinical symptoms and whose expression profile more closely resembled those of some of the more symptomatic subjects. Overall, we found that there was a significant difference in both time post-exposure and severity score between the subjects in the two clusters, although the temporal association may be due to the fact that the three animals that were studied for over 24 weeks were also coincidentally among the least symptomatic cases. We also performed a hierarchical clustering analysis on the set of profiles for 168 temporal- and/or severity associated genes included in a list of 409 gene IDs identified in a recent aggregate analysis of TB expression biomarkers identified in 7 prior studies (Joosten et al., 2013) and obtained identical clusters, suggesting that our results are fairly robust.


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)

Heatmap of expression profiles for 106 genes significantly associated with post-exposure time T and/or final severity score S and included in a set of 393 transcripts that were previously reported to effectively discriminate between cases of active and latent TB in blood samples (Berry et al., 2010). Hierarchical clustering of the samples on the basis of this subset separates 5 of the 6 least severe cases in our sample from the remaining 9 cases.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Heatmap of expression profiles for 106 genes significantly associated with post-exposure time T and/or final severity score S and included in a set of 393 transcripts that were previously reported to effectively discriminate between cases of active and latent TB in blood samples (Berry et al., 2010). Hierarchical clustering of the samples on the basis of this subset separates 5 of the 6 least severe cases in our sample from the remaining 9 cases.
Mentions: To put our results in a larger context, we compared our transcription profiles with those identified in recent studies seeking to identify TB expression biomarkers. In 2010, researchers published a list of 393 transcripts that were found to effectively discriminate between cases of active and latent TB in blood samples (Berry et al., 2010). We downloaded this list, which included 376 distinct gene IDs, and matched it to our set of 1950 genes that were significantly associated with T and/or S. We found an overlap of 106 genes and applied hierarchical clustering to the expression profiles associated with this subset. The results, shown in Figure 6, clearly separated five of our subjects from the others. Interestingly, these correspond to five of the six lowest scoring cases using our fitted severity models, all of which had very low CXR scores, 0 CRP values, and low or undetectable levels of bacteria upon autopsy. A very low-scoring subject on our severity scale that was not included in this group was CA75, an animal which demonstrated considerable weight loss despite an absence of other clinical symptoms and whose expression profile more closely resembled those of some of the more symptomatic subjects. Overall, we found that there was a significant difference in both time post-exposure and severity score between the subjects in the two clusters, although the temporal association may be due to the fact that the three animals that were studied for over 24 weeks were also coincidentally among the least symptomatic cases. We also performed a hierarchical clustering analysis on the set of profiles for 168 temporal- and/or severity associated genes included in a list of 409 gene IDs identified in a recent aggregate analysis of TB expression biomarkers identified in 7 prior studies (Joosten et al., 2013) and obtained identical clusters, suggesting that our results are fairly robust.

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