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Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection.

Shin H, G√ľnther O, Hollander Z, Wilson-McManus JE, Ng RT, Balshaw R, Keown PA, McMaster R, McManus BM, Isbel NM, Knoll G, Tebbutt SJ - Bioinform Biol Insights (2014)

Bottom Line: We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors.Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ.This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

View Article: PubMed Central - PubMed

Affiliation: NCE CECR PROOF Centre of Excellence, Vancouver, BC. ; University of British Columbia (UBC) Department of Medicine, Vancouver, BC. ; Institute for HEART + LUNG Health, Vancouver, BC.

ABSTRACT
In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

No MeSH data available.


Related in: MedlinePlus

Comparison of 24 ARs at rejection and 24 matched NRs. A. PCA plot of 24 AR patient samples at the time of rejection and their 24 matched NR patient samples. AR and NR samples do not separate clearly. B. The same PCA plot of 24 AR and 24 NR patient samples as in A. Samples are highlighted by the time (days) of rejection (ie, time of sample collection since transplant). Sample separation based on the time of collection can be seen indicating the presence of a time-dependent gene expression signature. C. PCA plot of samples from days 2 to 10 posttransplant generated using the time-dependent gene expression signature. Overall, a strong separation of samples from days 2 to 4 and days 6 to 10 posttransplant is observed (the number shown next to each sample point is the number of days posttransplant for that sample). D. PCA plot of 8 late ARs (week 3 and beyond, posttransplant) and the matched NRs using all the filtered probe set data. These late ARs separate from NRs more clearly compared to the early ARs and NRs.
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f1-bbi-8-2014-017: Comparison of 24 ARs at rejection and 24 matched NRs. A. PCA plot of 24 AR patient samples at the time of rejection and their 24 matched NR patient samples. AR and NR samples do not separate clearly. B. The same PCA plot of 24 AR and 24 NR patient samples as in A. Samples are highlighted by the time (days) of rejection (ie, time of sample collection since transplant). Sample separation based on the time of collection can be seen indicating the presence of a time-dependent gene expression signature. C. PCA plot of samples from days 2 to 10 posttransplant generated using the time-dependent gene expression signature. Overall, a strong separation of samples from days 2 to 4 and days 6 to 10 posttransplant is observed (the number shown next to each sample point is the number of days posttransplant for that sample). D. PCA plot of 8 late ARs (week 3 and beyond, posttransplant) and the matched NRs using all the filtered probe set data. These late ARs separate from NRs more clearly compared to the early ARs and NRs.

Mentions: In an attempt to uncover differential gene expression signatures that correlated with acute allograft kidney rejection, we compared transcriptome data from 24 AR samples at the time of rejection with their matched 24 NR samples. To do this, we first simplified the analysis by performing data reduction using PCA (principal component analysis),24 a method that creates a visualization of different clusters in two- or three-dimensional space, allowing the highest variations to be determined. PCA analysis revealed no significant variance (in the 2 principal components) that separated AR samples from NR samples (Fig. 1A). However, we found that the most significant of the criteria that separated the sample data was time posttransplantation, regardless of rejection status. Specifically, samples from early time points posttransplant separated from samples taken in weeks 3 and beyond (Fig. 1B). This result highlighted the importance of precise sample matching between the AR and NR patients groups with respect to sample collection time posttransplantation. A closer examination of the initial sample matching between AR and NR patients revealed an imbalance with respect to collection time in the first week post transplant. For example, 7 AR samples collected at day 3 or 4 were originally matched with 7 NR samples from day 6 or 7. Given the potential for the presence of dynamic and extensive transcriptional changes post surgery, the imprecise time matching of the samples may have introduced a confounding factor to the analysis, potentially masking rejection-associated expression changes.


Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection.

Shin H, G√ľnther O, Hollander Z, Wilson-McManus JE, Ng RT, Balshaw R, Keown PA, McMaster R, McManus BM, Isbel NM, Knoll G, Tebbutt SJ - Bioinform Biol Insights (2014)

Comparison of 24 ARs at rejection and 24 matched NRs. A. PCA plot of 24 AR patient samples at the time of rejection and their 24 matched NR patient samples. AR and NR samples do not separate clearly. B. The same PCA plot of 24 AR and 24 NR patient samples as in A. Samples are highlighted by the time (days) of rejection (ie, time of sample collection since transplant). Sample separation based on the time of collection can be seen indicating the presence of a time-dependent gene expression signature. C. PCA plot of samples from days 2 to 10 posttransplant generated using the time-dependent gene expression signature. Overall, a strong separation of samples from days 2 to 4 and days 6 to 10 posttransplant is observed (the number shown next to each sample point is the number of days posttransplant for that sample). D. PCA plot of 8 late ARs (week 3 and beyond, posttransplant) and the matched NRs using all the filtered probe set data. These late ARs separate from NRs more clearly compared to the early ARs and NRs.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3921155&req=5

f1-bbi-8-2014-017: Comparison of 24 ARs at rejection and 24 matched NRs. A. PCA plot of 24 AR patient samples at the time of rejection and their 24 matched NR patient samples. AR and NR samples do not separate clearly. B. The same PCA plot of 24 AR and 24 NR patient samples as in A. Samples are highlighted by the time (days) of rejection (ie, time of sample collection since transplant). Sample separation based on the time of collection can be seen indicating the presence of a time-dependent gene expression signature. C. PCA plot of samples from days 2 to 10 posttransplant generated using the time-dependent gene expression signature. Overall, a strong separation of samples from days 2 to 4 and days 6 to 10 posttransplant is observed (the number shown next to each sample point is the number of days posttransplant for that sample). D. PCA plot of 8 late ARs (week 3 and beyond, posttransplant) and the matched NRs using all the filtered probe set data. These late ARs separate from NRs more clearly compared to the early ARs and NRs.
Mentions: In an attempt to uncover differential gene expression signatures that correlated with acute allograft kidney rejection, we compared transcriptome data from 24 AR samples at the time of rejection with their matched 24 NR samples. To do this, we first simplified the analysis by performing data reduction using PCA (principal component analysis),24 a method that creates a visualization of different clusters in two- or three-dimensional space, allowing the highest variations to be determined. PCA analysis revealed no significant variance (in the 2 principal components) that separated AR samples from NR samples (Fig. 1A). However, we found that the most significant of the criteria that separated the sample data was time posttransplantation, regardless of rejection status. Specifically, samples from early time points posttransplant separated from samples taken in weeks 3 and beyond (Fig. 1B). This result highlighted the importance of precise sample matching between the AR and NR patients groups with respect to sample collection time posttransplantation. A closer examination of the initial sample matching between AR and NR patients revealed an imbalance with respect to collection time in the first week post transplant. For example, 7 AR samples collected at day 3 or 4 were originally matched with 7 NR samples from day 6 or 7. Given the potential for the presence of dynamic and extensive transcriptional changes post surgery, the imprecise time matching of the samples may have introduced a confounding factor to the analysis, potentially masking rejection-associated expression changes.

Bottom Line: We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors.Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ.This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

View Article: PubMed Central - PubMed

Affiliation: NCE CECR PROOF Centre of Excellence, Vancouver, BC. ; University of British Columbia (UBC) Department of Medicine, Vancouver, BC. ; Institute for HEART + LUNG Health, Vancouver, BC.

ABSTRACT
In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

No MeSH data available.


Related in: MedlinePlus