Proteomic Signatures in Plasma during Early Acute Renal Allograft Rejection

Freue, Gabriela V. Cohen; Sasaki, Mayu; Meredith, Anna; Günther, Oliver P.; Bergman, Axel; Takhar, Mandeep; Mui, Alice; Balshaw, Robert; Ng, Raymond T.; Opushneva, Nina; Hollander, Zsuzsanna; Li, Guiyun; Borchers, Christoph H.; Wilson-McManus, J.; McManus, Bruce M.; Keown, Paul; McMaster, W. Robert

doi:10.1074/mcp.m110.000554

Cited by 85 publications

(77 citation statements)

References 61 publications

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“…However, our study was not designed to show differences in renal allograft transcriptome between different rejection grades so data should not be over interpreted. Decreased expression of KNG1 in CI compared to SCI and control is fully consistent with literature knowledge of decreased kininogen1-proteomic activity in acute cellular rejection [52].…”

Section: Discussionsupporting

confidence: 77%

Molecular Patterns of Subclinical and Clinical Rejection of Kidney Allograft: Quantity Matters

Wohlfahrtová¹,

Tycova²,

Honsová

et al. 2015

Kidney Blood Press Res

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Background/Aims: Subclinical rejection diagnosed from protocol biopsies is thought to be a risk factor of long- term allograft dysfunction. The reason why in some patients subclinical rejection does not represent risk for progression is not fully understood. Methods: The intragraft expression of 376 target genes involved in chemokine defense, apoptosis, inflammation, tolerance and TGF-β signalling pathways was measured using quantitative real-time RT-PCR (2^-∆∆^Ct) method in subclinical inflammation (SCI, n=10), clinical inflammation in acute T-cell mediated rejection (CI, n=10) and no rejection samples (n=9). Results: Clinical inflammation group showed a increased expression of genes for chemotaxis mediating cytokines (CCL1, CCL17, CCL24, CCL25, CCL26), cytokine receptors (CCR1, CCRL2, IL1RAPL2, CXCR5), proinflammatory cytokines (IL12A, LTA), inflammatory mediator (PTAFR), complement protein C3, executioner protein of apoptosis (CASP7), growth factor (TGFA), colony stimulating factor (CSF-2), proteins involved in dendritic cells differentiation and interaction (CD209, LAMP3), regulation of immune response (LILRB2, LILBRB4). The quantitative difference in transcripts signature between SCI and CI is consistent with stronger proinflammatory setting of CI. Prostaglandin E2 receptor gene expression was independently associated with lower risk of further graft function deterioration (OR 0.11, CI 0.01-0.78, p<0.0001). Conclusion: Subclinical acute kidney inflammation has transcriptional profile of immune injury of lower extend compared to clinical acute inflammation.

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Section: Discussionsupporting

confidence: 77%

Molecular Patterns of Subclinical and Clinical Rejection of Kidney Allograft: Quantity Matters

Wohlfahrtová¹,

Tycova²,

Honsová

et al. 2015

Kidney Blood Press Res

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“…Broad approaches can involve taking on a global examination of the entire proteome, or evaluation of a specific subproteome, such as all proteins with a particular PTM. Numerous renal biomarker studies are global; a concrete example of the latter is a renal transplantation study that compared plasma of patients with acute rejection to controls with stable graft function (9 ). The entire proteomes of individuals in each group were compared, and 18 proteins were found to be differentially expressed between the 2 groups.…”

Section: Proteomics: Promises and Limitationsmentioning

confidence: 99%

“…Plasma biomarkers of early AR were evaluated by iTRAQ (isobaric tags for relative and absolute quantification)-MALDI-TOF/TOF by comparison of meticulously selected patients with biopsy-proven AR and stable controls (9 ). Plasma was depleted of 14 highly abundant proteins, and 18 proteins differentiated the groups, although with a Ͻ2-fold change in each case.…”

Section: Transplantationmentioning

confidence: 99%

Searching for New Biomarkers of Renal Diseases through Proteomics

Konvalinka¹,

Scholey²,

Diamandis

2012

Clinical Chemistry

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BACKGROUND Technological advances have resulted in a renaissance of proteomic studies directed at finding markers of disease progression, diagnosis, or responsiveness to therapy. Renal diseases are ideally suited for such research, given that urine is an easily accessible biofluid and its protein content is derived mainly from the kidney. Current renal prognostic markers have limited value, and renal biopsy remains the sole method for establishing a diagnosis. Mass spectrometry instruments, which can detect thousands of proteins at nanomolar (or even femtomolar) concentrations, may be expected to allow the discovery of improved markers of progression, diagnosis, or treatment responsiveness. CONTENT In this review we describe the strengths and limitations of proteomic methods and the drawbacks of existing biomarkers, and provide an overview of opportunities in the field. We also highlight several proteomic studies of biomarkers of renal diseases selected from the plethora of studies performed. SUMMARY It is clear that the field of proteomics has not yet fulfilled its promise. However, ongoing efforts to standardize sample collection and preparation, improve study designs, perform multicenter validations, and create joint industry–regulatory bodies offer promise for the recognition of novel molecules that could change clinical nephrology forever.

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“…The PROOF Centre of Excellence performed a longitudinal study to identify diagnostic biomarkers in blood plasma to predict acute renal allograft rejection [14]. The iTraq kidney rejection dataset is a subsample thereof which includes 10 acute rejection patients (AR) and 20 non-rejection patients (NR).…”

Section: Methodsmentioning

confidence: 99%

“…Hence, a comprehensive analysis of the proteome under defined circumstances can reveal altered or new interactions. Moreover, the comparison of molecule kinetics over time in healthy and diseased individuals or in wildtypes and knockouts [13] will increase our understanding of disease progression, molecular functions and pathways [14].…”

Section: The Importance Of Time Course Experimentsmentioning

confidence: 99%

Development of statistical tools for integrating time course ‘omics’ data

Straube¹

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Biological systems adapt to environmental and genetic changes via dynamic molecular expression and regulation. During the past decade, the use of 'omics' technologies to take a snapshot of this molecular behaviour has become ubiquitous. Expression quantities of a single molecular level like the transcriptome, proteome or metabolome have been a focus of study, however this is not sufficient to understand the complex intermolecular level regulations. Instead, taking a series of snapshots of molecular levels over time allows the study of molecular expression dynamics. In addition, the integration of multiple time course 'omics' experiments may result in better understanding of how molecular interactions give rise to the functions and behaviours of that system. The analysis of time course 'omics' data has various objectives. One is to identify molecules that change expression over time or between groups to infer their contribution to biological processes.Another is to cluster molecules with similar expression profiles. These co-expressed molecules are believed to be co-regulated or have similar molecular functions and are hypothesised to be the result of networks of molecule interaction. Therefore, researchers use time course 'omics' data to identify co-expressed molecules and differentially expressed molecules to understand the dynamics of biological systems at the molecular level. These data can also be used to model molecular interaction networks with the objective to understand the relationships of molecules that drive a biological system.Time course 'omics' experiments quantify thousands of molecules for each biological sample, but those experiments often only include very few biological and technical replications. The data generated are difficult to analyse because of the high number of missing values, as well as potentially high within and between individual variability. Furthermore, expression changes of co-expressed molecules can occur delayed in time. Therefore, powerful statistical methods are critical for answering key questions about system response and function. They need to efficiently analyse highdimensional data that are noisy and complex. However, so far researchers have been limited in capitalising on the wealth of 'omics' data because of the lack of powerful statistical methods. This thesis outlines the development of novel and user-friendly statistical tools to analyse time course 'omics' data. It consists of two core projects: firstly, the development of a framework to analyse time course 'omics' data obtained from a single molecular level (e.g. proteome); secondly, the development of statistical methods and tools to integrate and analyse time course 'omics' data from several functional levels (e.g. transcriptome and metabolome).For my first project, I developed a framework consisting of three stages: quality assessment and filtering, profile modelling, and analysis. The quality control approach was developed to remove molecules for which expression was highly noisy. I then further deve...

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Proteomic Signatures in Plasma during Early Acute Renal Allograft Rejection

Cited by 85 publications

References 61 publications

Molecular Patterns of Subclinical and Clinical Rejection of Kidney Allograft: Quantity Matters

Molecular Patterns of Subclinical and Clinical Rejection of Kidney Allograft: Quantity Matters

Searching for New Biomarkers of Renal Diseases through Proteomics

Development of statistical tools for integrating time course ‘omics’ data

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