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Chapter 118 : Molecular Characterization of Rejection in Solid Organ Transplantation

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Molecular Characterization of Rejection in Solid Organ Transplantation, Page 1 of 2

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Abstract:

Improved knowledge of the alloimmune repertoire, development and clinical application of new therapies, advances in surgical techniques, and effective infection prophylaxis have helped to transition transplantation from a high-risk experimental therapy to a safe clinical remedy. Nevertheless, diagnostic and therapeutic challenges remain. Here we provide an outline of the immune cascade implicated in allograft rejection, emphasize molecular protocols for characterizing gene expression strengths and patterns and for high-throughput protein and peptide analyses, and summarize molecular correlates of rejection of human kidney, heart, lung, liver, and pancreas allografts.

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Figures

Image of FIGURE 1
FIGURE 1

CTOT-04 study cohorts and retrospective trajectories of the diagnostic signature. The average within-person retrospective trajectories of the diagnostic signature (i.e., trajectories as a function of the time before biopsy) in urine samples obtained at or before biopsy that passed quality control are shown for the group of 38 patients with first biopsy specimens showing acute cellular rejection (201 urine samples) (A) and the group of 113 patients with specimens showing no rejection (833 urine samples) (B). Only specimens obtained during the first 400 days after transplantation were included. (C) The diagnostic signature remained relatively flat and well below the −1.213 threshold that was diagnostic of acute cellular rejection during the 270 days before biopsy in the group of patients with findings showing no rejection. (D) There was a significant difference in the trajectories between the two groups, with a marked increase in the diagnostic signature during the 20-day period before the first specimen showing acute cellular rejection ( < 0.001). In all the panels, the black lines indicate the trajectory, the colored bands indicate the 95% confidence interval, and the red lines indicate the diagnostic threshold. (Reprinted from reference with permission of the publisher.)

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Image of FIGURE 2
FIGURE 2

Unsupervised hierarchical clustering and principal component analysis of miRNA expression profiles differentiate acute rejection biopsy specimens from normal allograft biopsies of human renal allografts. (A) miRNA expression patterns for seven human kidney allograft biopsy specimens (three with acute rejection [AR] and four with normal allograft biopsy [N]) were examined using TaqMan low-density arrays containing TaqMan probes and primer pairs for 365 human mature miRNAs. A total of 174 ± 7 miRNAs were expressed at a significant level (i.e., of ≥35) in all samples. The biopsy specimens were grouped by unsupervised hierarchical clustering on the basis of similarity in expression patterns, and the degrees of relatedness of the expression patterns of biopsy samples are represented by the dendrogram at the top of the panel. Branch lengths represent the degree of similarity between individual samples (top) or miRNA (left). Two major clusters (top) accurately divided AR biopsy specimens from normal allograft biopsies. Each column corresponds to the expression profile of a renal allograft biopsy, and each row corresponds to a miRNA. The color in each cell reflects the level of expression of the corresponding miRNA in the corresponding sample, relative to its mean level of expression in the entire set of biopsy samples. The increasing intensities of red mean that a specific miRNA has a higher expression in the given sample, and the increasing intensities of green mean that this miRNA has a lower expression. The scale (bottom right) reflects miRNA abundance ratio in a given sample relative to the mean level for all samples. (B) Principal component analysis of seven kidney allograft biopsy specimens based on the expression of 174 small RNAs significantly expressed (i.e., C ≥35) in all of the samples. Samples were accurately grouped by PC1, which explained 45.91% of the overall miRNA expression variability, whereas PC2 explained 21.48% of variability and did not classify the samples according to their diagnosis. (Reprinted from reference with permission from the publisher.)

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Image of FIGURE 3
FIGURE 3

miRNA profiles generated by small RNA sequencing. (A) Hierarchical clustering and heat map representation of kidney allograft IFTA biopsy samples (M1, M3, and M4), normal biopsy specimens (M5, M6, M7, and M8), human PBMCs, HDFa cells (primary skin fibroblasts), HEK293 cells (human embryonic kidney cells), and HK2 cells (kidney proximal tubule cells) according to merged miRNA profiles (average linkage, Manhattan distance). miRNAs represented by fewer than 100 reads per sample (average) were collapsed into a single entry (“other mirs”). Brighter shades represent more expression, according to the color scheme shown on the side, in which the numbers correspond to the log values of the normalized read frequencies (e.g., −4 on the scale corresponds to 2 = 6.25% of all miRNA reads). (B) Multidimensional scaling showing separation of IFTA from normal biopsy specimens by the third factor. (C) MA plot depicting differentially expressed miRNA sequence families, generated using DESeq. miRNA expressed more highly in IFTA samples appear above the horizontal midline. Colored data points represent values of <0.05 (red points signify false discovery rates of <0.1). (Reprinted from reference with permission of the publisher.)

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Image of FIGURE 4
FIGURE 4

Rise in number of published articles on proteomics observed over the last 2 decades. A search of PubMed with the keyword “proteomics” generated a list of research articles and clearly demonstrated a trend of a rise in use of this method for research.

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Image of FIGURE 5
FIGURE 5

MS for proteomic discovery and validation. (A) General schematic for MS identification of proteins and peptides. (B) Schematic of SRM by TripleQuad.

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Image of FIGURE 6
FIGURE 6

Proteomic strategy for biomarker discovery and validation. A strategy that utilizes data sets generated for different organ transplants can be useful for identifying and validating clinically useful protein biomarkers.

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
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Tables

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TABLE 1

Diagnostic accuracy of intragraft miRNA/mRNA levels during acute kidney rejection

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118
Generic image for table
TABLE 2

Comparison of proteomic methods

Citation: Dadhania D, Sigdel T, Muthukumar T, Hartono C, Sarwal M, Suthanthiran M. 2016. Molecular Characterization of Rejection in Solid Organ Transplantation, p 1132-1149. In Detrick B, Schmitz J, Hamilton R (ed), Manual of Molecular and Clinical Laboratory Immunology, Eighth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818722.ch118

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