- Research article
- Open Access
- Open Peer Review
Alteration of human blood cell transcriptome in uremia
© Scherer et al.; licensee BioMed Central Ltd. 2013
- Received: 2 November 2012
- Accepted: 4 June 2013
- Published: 28 June 2013
End-stage renal failure is associated with profound changes in physiology and health, but the molecular causation of these pleomorphic effects termed “uremia” is poorly understood. The genomic changes of uremia were explored in a whole genome microarray case-control comparison of 95 subjects with end-stage renal failure (n = 75) or healthy controls (n = 20).
RNA was separated from blood drawn in PAXgene tubes and gene expression analyzed using Affymetrix Human Genome U133 Plus 2.0 arrays. Quality control and normalization was performed, and statistical significance determined with multiple test corrections (qFDR). Biological interpretation was aided by knowledge mining using NIH DAVID, MetaCore and PubGene
Over 9,000 genes were differentially expressed in uremic subjects compared to normal controls (fold change: -5.3 to +6.8), and more than 65% were lower in uremia. Changes appeared to be regulated through key gene networks involving cMYC, SP1, P53, AP1, NFkB, HNF4 alpha, HIF1A, c-Jun, STAT1, STAT3 and CREB1. Gene set enrichment analysis showed that mRNA processing and transport, protein transport, chaperone functions, the unfolded protein response and genes involved in tumor genesis were prominently lower in uremia, while insulin-like growth factor activity, neuroactive receptor interaction, the complement system, lipoprotein metabolism and lipid transport were higher in uremia. Pathways involving cytoskeletal remodeling, the clathrin-coated endosomal pathway, T-cell receptor signaling and CD28 pathways, and many immune and biological mechanisms were significantly down-regulated, while the ubiquitin pathway and certain others were up-regulated.
End-stage renal failure is associated with profound changes in human gene expression which appears to be mediated through key transcription factors. Dialysis and primary kidney disease had minor effects on gene regulation, but uremia was the dominant influence in the changes observed. This data provides important insight into the changes in cellular biology and function, opportunities for biomarkers of disease progression and therapy, and potential targets for intervention in uremia.
- Gene expression profiling
- Chronic renal failure
Chronic kidney disease (CKD) is a debilitating disorder with profound medical and societal consequences, characterized by a marked reduction in health, quality of life, societal functioning, productivity and survival [1–4]. Pleomorphic manifestations of uremia appear as renal function declines, and include impaired cognition and execution of higher function tasks; disordered neuromuscular function with muscle weakness, seizures and sensorimotor neuropathy; altered endothelial function with accelerated vascular disease; hematological alterations with anemia, platelet dysfunction and bleeding; endocrine and metabolic disorders typified by insulin resistance, gonadal dysfunction, hyperparathyroidism, bone disease and soft-tissue calcification; and disorders of innate and adaptive immunology with features of both inflammation and immune deficiency [1, 2].
The features of uremia have been attributed to disordered homeostasis caused by altered synthetic functions, reduced excretion of biological end-products, and disordered fluid balance associated with failure of renal function. Retention solutes found at higher levels in uremic subjects have been identified as uremic toxins based on their association with uremic symptoms in animals and humans with renal failure, the resolution of these symptoms when levels of these compounds are lowered, and the toxic effects when these substances are added to cells or tissues in vitro [5, 6]. However, despite extensive investigation of the biology of uremia, and the application of recent advances in proteomics technology to investigate the causality of this syndrome , the molecular understanding of the precise disturbances in the uremic syndrome remains incomplete.
The development of high-throughput microarray technology, permitting simultaneous measurement of changes in expression of multiple genes within the human genome, provides the opportunity for novel insight into disease processes and molecular pathways of biological dysfunction [8, 9]. Recent advances have improved the sensitivity, specificity and accuracy of histological diagnosis using this technology, and the field of functional genomics is consequently a focus of intense investigation in many disease states [10–12]. The current study therefore examines the differential patterns of gene expression in normal subjects and patients with renal failure and outlines some of the principal biological alterations observed in the uremic state.
Demographic and clinical characteristics of study subjects
47 ± 11
47 ± 13
42 ± 11
Primary Disease (%)
Polycystic Kidney Disease
Most highly differentially expressed functionally defined genes in uremic subjects by comparison with normal controls
Fold change uremia vs normal
Fold change uremia vs normal
ATPase, Ca++ transporting, ubiquitous
mesoderm development candidate 1
ring finger protein 19B
ATPase inhibitory factor 1
FK506 binding protein 1A, 12kDa
interleukin enhancer binding factor 3, 90kDa
retinoblastoma binding protein 4
phosphatidylethanolamine binding protein 1
C-terminal binding protein 1
histidine triad nucleotide binding protein 1
kelch-like 24 (Drosophila)
interleukin enhancer binding factor 3, 90kDa
lysine (K)-specific demethylase 1B
metastasis associated 1
potassium channel tetramerisation domain containing 5
coiled-coil domain containing 115
solute carrier family 23 (nucleobase transporters), member 2
acyl-CoA dehydrogenase family, member 8
RAB11 family interacting protein 4 (class II)
ring finger protein 19B
non-POU domain containing, octamer-binding
trinucleotide repeat containing 6A
NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 8, 19kDa
O-linked N-acetylglucosamine (GlcNAc) transferase (UDP-N-acetylglucosamine:polyp
ATP synthase, H + transporting, mitochondrial F1 complex, gamma polypeptide 1
membrane-associated ring finger (C3HC4) 5
protein phosphatase 1, regulatory (inhibitor) subunit 8
Ral GTPase activating protein, beta subunit (non-catalytic)
interferon regulatory factor 2
extended synaptotagmin-like protein 2
basic helix-loop-helix family, member e40
RAB GTPase activating protein 1
GA binding protein transcription factor, beta subunit 2
quaking homolog, KH domain RNA binding (mouse)
Friend leukemia virus integration 1
RAB7A, member RAS oncogene family
programmed cell death 4 (neoplastic transformation inhibitor)
B-cell CLL/lymphoma 9-like
ring finger protein 166
sphingosine-1-phosphate receptor 1
glutamate dehydrogenase 1
chromosome 7 open reading frame 64
coiled-coil domain containing 88C
cysteine sulfinic acid decarboxylase
serine/arginine-rich splicing factor 1
ATP citrate lyase
perforin 1 (pore forming protein)
mitogen-activated protein kinase 9
Kruppel-like factor 7 (ubiquitous)
phosphoinositide-3-kinase interacting protein 1
tribbles homolog 2 (Drosophila)
MORN repeat containing 1
fibroblast growth factor 18
chromosome 14 open reading frame 45
zinc finger protein 205
adenosine deaminase, RNA-specific, B1
glioma tumor suppressor candidate region gene 2
outer dense fiber of sperm tails 3B
scavenger receptor cysteine rich domain containing, group B (4 domains)
microtubule-associated protein, RP/EB family, member 3
hypothetical LOC100507328 /// hypothetical LOC100508591
double homeobox 4 /// double homeobox 4 like 2 /// double homeobox 4 like 3 ///
runt-related transcription factor 3
Polycomb group ring finger 5
G protein-coupled receptor 144
mitogen-activated protein kinase 8 interacting protein 2
zinc finger protein-like 1
pleckstrin homology domain containing, family G (with RhoGef domain) member 5
ELAV (embryonic lethal, abnormal vision, Drosophila)-like 3 (Hu antigen C)
F-box protein 44
ubiquitin-conjugating enzyme E2, J2 (UBC6 homolog, yeast)
hypothetical protein DKFZp761P0212
solute carrier family 25, member 37
sodium channel, nonvoltage-gated 1 alpha
surfactant protein C
PDZ and LIM domain 7 (enigma)
armadillo repeat containing 5
pleckstrin homology domain containing, family N member 1
GNAS complex locus
B-cell CLL/lymphoma 2
Coiled-coil-helix-coiled-coil-helix domain containing 5
Transferrin receptor (p90, CD71)
Ribosomal protein S11
chromosome 17 open reading frame 51
cytochrome P450, family 11, subfamily B, polypeptide 2
transmembrane protein with EGF-like and two follistatin-like domains 2
glycoprotein Ib (platelet), beta polypeptide
Tripartite motif-containing 8
vertebrae development homolog (pig)
Both dialysis and primary kidney disease (PKD) influenced gene expression in the study cohort, although this effect was small compared to the variation induced by the presence or absence of uremia. When the sources of variation in the dataset were estimated in a multifactorial ANOVA model, the presence or absence of uremia had the largest influence on the variation in the dataset (F ratio 9.55), while dialysis had a minor effect (F ratio 1.33) and the primary kidney disease, with polycystic kidney disease (PKD) as the reference group compared to the subgroups with renal disease secondary to diabetes mellitus (DM), glomerulonephritis (GN), and other etiologies (other) has the least influence (F ratio 1.01) (Figure 1a).
Principal gene pathways altered in uremia
Principal gene pathways altered in uremia
Transport: Clathrin-coated vesicle cycle
Cytoskeleton remodeling: TGF, WNT and cytoskeletal remodeling
Cytoskeleton remodeling: Cytoskeleton remodeling
Development: EPO-induced Jak-STAT pathway
Translation: Regulation of EIF4F activity
Chemotaxis: CXCR4 signaling pathway
Development: GM-CSF signaling
Immune response: T cell receptor signaling pathway
Immune response: IL-2 activation and signaling pathway
Immune response : Immunological synapse formation
Development: Flt3 signaling
Signal transduction: Activation of PKC via G-Protein coupled receptor
Cell cycle: Influence of Ras and Rho proteins on G1/S Transition
Immune response: Role of DAP12 receptors in NK cells
Immune response: BCR pathway
Transcription: NF-kB signaling pathway
Development: PIP3 signaling in cardiac myocytes
Development: EGFR signaling pathway
Chronic kidney disease is a global problem, with an estimated prevalence of more than 20% in those over 64 years of age  and health care cost approaching $2 billion per year in Canada and 7% of Medicare expenditures in the U.S. [14, 15]. Dialysis may ameliorate the symptoms of uremia, but inadequate clearance of uremic toxins ultimately results in progressive illness manifest by chronic injury to the vascular tree, skeleton, neuronal networks and other critical bodily systems [1, 2]. The European Uremic Toxin Work Group has listed more than 100 retained solutes that variably impair cellular function or survival and are important contributors in the expression of uremia . Among these, small molecules that bind reversibly to serum proteins and “middle molecule” range proteins of 10-30KD are difficult to remove by conventional dialysis . Many of the latter may become irreversibly altered through posttranslational modifications in the uremic environment, resulting in changes in structure and function .
The data reported here show that uremia is accompanied by profound changes in gene expression reflecting perturbation in many aspects of cell biology . Genes encoding regulators of transcription, mRNA transport, protein synthesis, export and localization, and cell-cycle progression are lower, and transcripts associated with membrane lipid metabolism involving phosphotidylinositol 3,4,5; n-acyl sphingosine; ceramide and others are significantly lower in uremia. Cytoskeletal remodeling is markedly impaired, and expression of genes for the binding proteins talin and actin, critical structural components of intracellular microfilaments, regulators such as tuberin, and RAS-superfamily GTP-ases integral to cytoskeletal re-organization are substantially reduced. Interestingly, transcripts central to apoptosis pathways including the Fas receptor, FADD, Granzyme B and members of the caspases family are also reduced arguing against a principal role in premature cell death [18, 19].
Among the complex endocrine changes associated with uremia , we observe that parathyroid hormone gene (PTH) expression is enhanced, consistent with the elevated hormone levels observed . The Wnt signaling pathway is activated in hyperarathyoidism  and is strongly represented in the current dataset by probe sets including Casein kinase 1, Rac1, c-Fos, and p130. Smad2 and Smad4, TGFBR2 and other members of the TGF-beta and BMP pathways, among the most highly dysregulated probe sets in uremia, may reflect altered bone metabolism . Expression of genes coding for the pituitary hormones was unchanged, while the prolactin releasing hormone (PRLH) gene was increased and prolactin regulatory element binding (PREB) gene reduced. Erythropoietin production is normally decreased in uremia. Possibly as a compensation to this, the erythropoietin receptor gene expression was significantly higher, while the down-stream signaling steps were repressed, perhaps contributing to the anemia of renal failure . The effect of uremia on platelet function may be reflected by changes in the probe sets coding for PKCeta, Rac1, ATP2A3, and GP-IB (platelet glycoprotein I beta) and other members of the “platelet aggregation” network.
Insulin resistance is an important endocrine effect of uremia, and is believed to contribute to accelerated vascular disease and muscle wasting . Although insulin binds normally to its receptor in uremia, and receptor density is unchanged, the transfer of insulin resistance by uremic serum suggests a direct contribution of uremic toxins. The data reported here indicates that insulin receptor gene (INSR) expression is modestly increased but the transcriptional level of insulin receptor substrate 2 (IRS2) is lower than normal. This cytoplasmic signaling molecule mediates the effects of insulin, acting as a molecular adaptor between diverse receptor tyrosine kinases and downstream effectors, and mice lacking IRS2 have a diabetic phenotype. Failure of post-receptor signaling has been noted as a fundamental mechanism of insulin resistance in uremic animals and in other disorders including injury, infection, aging and obesity and may reflect an important biological mechanisms in uremia .
Protein-calorie malnutrition is an important predictor of patient survival in uremia. Although the precise cause remains unclear, insulin resistance, inflammation, and elevated circulating levels of ghrelin and leptin have been implicated in this process [25–27]. While transcription of Ghrelin or Leptin genes was not altered, expression of both the leptin receptor overlapping transcript (LEPROT) and transcript-like 1 (LEPROTL1) was increased, which may influence leptin and GH receptor expression and their receptor-mediated signaling . Growth factor and insulin-like growth factor (IGF) gene expression were unchanged, while IGF receptor-1 expression was suppressed and post-receptor signaling through the 14-3-3 protein complex was lower, which may influence protein synthesis, muscle and bone metabolism . AKTIP was lower in uremia, consistent with the proposals that insulin resistance may promote muscle wasting by inhibition of PI3K/Akt leading to activation of caspase 3 and the ubiquitin-proteasome proteolytic . Activation of the ubiquitin-proteosome system (UPS), caused by inflammation, acidosis and other factors is a feature of muscle wasting conditions including sepsis and uremia . However, probe sets of the protein-degradation machinery, e.g. UBE2E1, USP32, UBE2Q2, and UBR3 were inhibited in uremia, indicating that evaluation of the ubiquitin-proteosome machinery requires more detailed investigation.
Uremia is characterized by a complex alteration in the immune response . Systemic inflammation, manifest by elevations in inflammatory markers including C-reactive protein, interleukin-6, and tumor necrosis factor α , is accompanied by polymorph and monocyte dysfunction , and impaired cellular immunity with altered T cell function and proliferation . The data here reflect many of these events at the genomic level. Gene expression associated with the complement pathway and oxidative metabolism is higher in uremia, while transcripts associated with the clathrin-coated vesicle endosomal pathway are markedly reduced consistent with a defect in phagocytosis. Key genes in the immune synapse and the T-cell receptor signaling pathway were reduced, including MHC-class II and the T-cell receptor alpha / beta heterodimer, the co-associated CD3 and CD4 molecules and a variety of downstream signaling components of the T-cell receptor pathway, the CD28 receptor pathway and the IL-2 response and signaling pathway.
Peripheral blood is a common matrix for investigation of human biology and biomarkers, but is subject to certain limitations which may influence the results observed. Fluctuation in peripheral formed elements may influence gene expression patterns, and while we have attempted to minimize this by selecting candidates whose peripheral blood counts resemble as closely as possible those of the normal control population this does not eliminate all bias. In addition, the presence of globin mRNA which represents up to 70% of the total expressed transcripts in peripheral blood, reduces the sensitivity of microarray analysis, particularly in detecting differences among genes transcribed at low levels [34–36]. Strategies to reduce globin mRNA were not employed in these studies, since preliminary data indicated the profound magnitude of the changes in uremia, but it is possible that this step may enhance the sensitivity of these results and define further critical biological alterations in the uremic state .
In summary, the data presented show that uremia is accompanied by a marked change in expression of genes involved in a broad range of physiological processes [1, 6]. Many of these genes appear to be coordinately regulated through networks whose activity is suppressed or enhanced by individual transcription factors. Recent work suggests that epigenetic regulation may exert an important influence in these changes, and that histone hypermethylation may contribute to both the reduced expression and increased inflammatory mechanisms observed in this setting [37, 38]. These observations provide an important insight into the biology of the uremic syndrome and a foundation for more detailed proteogenomic exploration of uremic toxicity. They provide a foundation for exploration of biomarkers for measurement of treatment efficacy, and offer a starting point for identification of new therapeutic targets regulating gene effects to mitigate the consequences of this syndrome and restore biological homeostasis.
The study was conducted at the University of British Columbia and approved by the human ethics research board. A case-control design was employed to compare gene expression in patients with chronic renal failure and healthy controls. Patients with stage 5 renal disease aged 18 to 75 years, who were clinically stable awaiting renal transplantation, were not receiving immunosuppressive medications, and provided written informed consent were enrolled into the study. Patients were treated according to Canadian Guidelines for Chronic Kidney Disease . Dialysis was instituted at a calculated GFR of less than 15 ml/min/m2; peritoneal dialysis was normally performed by continuous ambulatory peritoneal dialysis (CAPD) or a cycler, and hemodialysis (HD) was normally performed 3 times per week for an average of 12 hours. Normal controls of comparable age and gender who were screened to ensure freedom from known illness and medical therapy served as comparators.
Early morning, fasting, whole blood samples (5 ml) were drawn into PAXgeneTM tubes (Qiagen Inc) before dialysis or anticoagulation, and stored at -80° until analysis. Total RNA was extracted from the cells using a PAXgeneTM Blood RNA Kit, and the integrity and concentration determined using the Agilent 2100 BioAnalyzer (Agilent Technologies, Palo Alto, CA). Gene expression was analyzed at the CAP/CLIA certified Genome Core at the Children’s Hospital, Los Angeles, CA using Affymetrix Human Genome U133 Plus 2.0 arrays (Affymetrix Inc). Strategies to reduce globin mRNA were not employed in this study, since preliminary data demonstrated a marked difference between expression patterns in uremic and normal subjects. Quality of the samples, hybridization, chips and scanning was reviewed using the BioConductor packages Affy version 1.16.0 and affyPLM version 1.14.0. Data import, normalization and statistical analysis were performed using the Partek Genomics Suite, version 6.5 (Partek, St Louis, MI). RMA background correction and quantile normalization were applied followed by log2-transformation. An unsupervised raw expression filter was applied with a threshold of signal intensity of 6 in a number of samples equal to 75% of the smallest sample group. RNA samples for qPCR were reverse transcribed using SuperScript III First-Strand Synthesis kit (Invitrogen). qPCR assays were performed using gene-specific primers and Taqman gene expression assays (Applie Bioscience) on the ABI 7900 HT. Expression levels were normalized against β-actin.
Statistical significance was determined by ANOVA, followed by multiple test corrections (qFDR). Probe sets were ranked by fold change after application of a qFDR threshold. A qFDR value < 0.05 was considered significant. Gene-set enrichment analysis (GSEA) was performed using GSEA software (http://www.broad.mit.edu/gsea). The dataset was not collapsed to gene symbols, probe sets were ranked by signal to noise metric, and the number of gene-set permutations was 1000. Biological interpretation was aided by knowledge mining using NIH DAVID (http://david.abcc.ncifcrf.gov/), MetaCore (http://www.GeneGo.com) and PubGene (http://www.Pubgene.org). Gene Ontologies and Networks in GeneGo MetaCore were prioritized based on their statistical significance with respect to the size of the intersection of the http://dataset and the set of genes/proteins corresponding to the Gene Ontology category or network (http://www.portal.genego.com/help/p-value_calculations.pdf).
Research supported by Genome Canada with supporting grants from Novartis Pharma, Basle and IBM Canada.
Paul A Keown, Bruce M McManus, W Robert McMaster and Raymond Ng are co-principal investigators in this research.
Biomarkers in Transplantation Group: We are grateful to the following collaborators who participated in the current study by their valuable scientific advice or clinical contribution to the selection and management of the study subjects who formed the basis of this report: Alice Mui1,3,a, Tim Tricheb, Gabriela Cohen Freue1,5, David Landsberg7, R. Jean Shapiro7, John Gill7, Jagbir Gill7, Olwyn Johnston7, Scott J. Tebbutt1,2,7.
a: Surgery, Vancouver, BC, Canada
b: Department of Pathology and Laboratory Medicine, Los Angeles Children’s Hospital, and University of California, Los Angeles, USA.
Scientficic Advisory Committee: We are grateful to the members of our Scientific Advisory Committee for their critical oversight and scientific guidance in this project: Professor. Kathryn Wood, Oxford University, UK; Ruedi Aebersold, Institute of Molecular Systems Biology, University of Zürich, Switzerland; John Quackenbush, Dana-Farber Cancer Institute, Boston, USA; Leigh Anderson, Plasma Proteome Institute, Washington, DC; Eric Olson, University of Texas – Southwestern, Dallas TX, Maria Rosa Costanzo, Midwest Heart, Edward Heart Institute, Naperville IL; Gunther Engel, Novartis Pharmaceuticals, Basel, and George Schreiner, Raven Biotechnologies, San Francisco, USA.
The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (Edgar et al., 2002) and are accessible through GEO Series accession number GSE37171 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37171).
- Meyer TW, Hostetter TH: Uremia. N Engl J Med. 2007, 357 (13): 1316-1325.View ArticlePubMedGoogle Scholar
- Almeras C, Argilés A: The general picture of uremia. Semin Dial. 2009, 22 (4): 329-333.View ArticlePubMedGoogle Scholar
- Pagels AA, Söderkvist BK, Medin C, Hylander B, Heiwe S: Health-related quality of life in different stages of chronic kidney disease and at initiation of dialysis treatment. Health Qual Life Outcomes. 2012, 10 (1): 71.View ArticlePubMedPubMed CentralGoogle Scholar
- Weiner DE: Public health consequences of chronic kidney disease. Clin Pharmacol Ther. 2009, 86 (5): 566-569.View ArticlePubMedPubMed CentralGoogle Scholar
- Vanholder R, Baurmeister U, Brunet P, Cohen G, Glorieux G, Jankowski J: A bench to bedside view of uremic toxins. J Am Soc Nephrol. 2008, 19 (5): 863-870.View ArticlePubMedGoogle Scholar
- Raff AC, Meyer TW, Hostetter TH: New insights into uremic toxicity. Curr Opin Nephrol Hypertens. 2008, 17 (6): 560-565.View ArticlePubMedGoogle Scholar
- Weissinger E, Kaiser T, Meert N, De Smet R, Walden M, Mischak H, et al: Proteomics: a novel tool to unravel the patho-physiology of uraemia. Nephrol Dial Transplant. 2004, 19 (12): 3068-3077.View ArticlePubMedGoogle Scholar
- Yasuda Y, Cohen CD, Henger A, Kretzler M, Consortium ERBE: Gene expression profiling analysis in nephrology: towards molecular definition of renal disease. Clin Exp Nephrol. 2006, 10 (2): 91-98.View ArticlePubMedGoogle Scholar
- Henger A, Schmid H, Kretzler M: Gene expression analysis of human renal biopsies: recent developments towards molecular diagnosis of kidney disease. Curr Opin Nephrol Hypertens. 2004, 13 (3): 313-318.View ArticlePubMedGoogle Scholar
- Keown PA, McMaster WR, McManus BM: Tools to identify organ rejection and immune quiescence for biological understanding and personalized medical care. Biomark Med. 2010, 4 (1): 115-121.View ArticlePubMedGoogle Scholar
- Boehm JS, Hahn WC: Towards systematic functional characterization of cancer genomes. Nat Rev Genet. 2011, 12 (7): 487-498.View ArticlePubMedGoogle Scholar
- Sarwal MM, Sigdel TK, Salomon DR: Functional proteogenomics–embracing complexity. Semin Immunol. 2011, 23 (4): 235-251.View ArticlePubMedGoogle Scholar
- Zhang QL, Rothenbacher D: Prevalence of chronic kidney disease in population-based studies: systematic review. BMC Publ Health. 2008, 8: 117.View ArticleGoogle Scholar
- Foley R, Collins A: End-stage renal disease in the United States: an update from the United States Renal Data System. J Am Soc Nephrol. 2007, 18 (10): 2644-2648.View ArticlePubMedGoogle Scholar
- Zelmer J: The economic burden of end-stage renal disease in Canada. Kidney Int. 2007, 72 (9): 1122-1129.View ArticlePubMedGoogle Scholar
- Duranton F, Cohen G, De Smet R, Rodriguez M, Jankowski J, Vanholder R, et al: Normal and Pathologic Concentrations of Uremic Toxins. J Am Soc Nephrol. 2012, 23 (7): 1258-1270.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang K: Integration of ER stress, oxidative stress and the inflammatory response in health and disease. Int J Clin Exp Med. 2010, 3 (1): 33-40.PubMedPubMed CentralGoogle Scholar
- Sardenberg C, Suassuna P, Andreoli MC, Watanabe R, Dalboni MA, Manfredi SR, et al: Effects of uraemia and dialysis modality on polymorphonuclear cell apoptosis and function. Nephrol Dial Transplant. 2006, 21 (1): 160-165.View ArticlePubMedGoogle Scholar
- Soriano S, Martín-Malo A, Carracedo J, Ramírez R, Rodríguez M, Aljama P: Lymphocyte apoptosis: role of uremia and permeability of dialysis membrane. Nephron Clin Pract. 2005, 100 (3): c71-c77.View ArticlePubMedGoogle Scholar
- Leavey SF, Weitzel WF: Endocrine abnormalities in chronic renal failure. Endocrinol Metab Clin North Am. 2002, 31 (1): 107-119.View ArticlePubMedGoogle Scholar
- Bjorklund P, Akerstrom G, Westin G: Accumulation of nonphosphorylated beta-catenin and c-myc in primary and uremic secondary hyperparathyroid tumors. J Clin Endocrinol Metab. 2007, 92 (1): 338-344.View ArticlePubMedGoogle Scholar
- Allori AC, Sailon AM, Warren SM: Biological basis of bone formation, remodeling, and repair-part I: biochemical signaling molecules. Tissue Eng Part B Rev. 2008, 14 (3): 259-273.View ArticlePubMedGoogle Scholar
- Siew ED, Ikizler TA: Insulin resistance and protein energy metabolism in patients with advanced chronic kidney disease. Semin Dial. 2010, 23 (4): 378-382.View ArticlePubMedGoogle Scholar
- Lee YH, White MF: Insulin receptor substrate proteins and diabetes. Arch Pharm Res. 2004, 27 (4): 361-370.View ArticlePubMedGoogle Scholar
- Mak RH, Cheung W, Cone RD, Marks DL: Leptin and inflammation-associated cachexia in chronic kidney disease. Kidney Int. 2006, 69 (5): 794-797.View ArticlePubMedGoogle Scholar
- Du J, Mitch WE: Identification of pathways controlling muscle protein metabolism in uremia and other catabolic conditions. Curr Opin Nephrol Hypertens. 2005, 14 (4): 378-382.View ArticlePubMedGoogle Scholar
- Wang X, Hu Z, Hu J, Du J, Mitch WE: Insulin resistance accelerates muscle protein degradation: Activation of the ubiquitin-proteasome pathway by defects in muscle cell signaling. Endocrinology. 2006, 147 (9): 4160-4168.View ArticlePubMedGoogle Scholar
- Touvier T, Conte-Auriol F, Briand O, Cudejko C, Paumelle R, Caron S, et al: LEPROT and LEPROTL1 cooperatively decrease hepatic growth hormone action in mice. J Clin Invest. 2009, 119 (12): 3830-3838.View ArticlePubMedPubMed CentralGoogle Scholar
- Annunziata M, Granata R, Ghigo E: The IGF system. Acta Diabetol. 2011, 48 (1): 1-9.View ArticlePubMedGoogle Scholar
- Dahlmann B: Role of proteasomes in disease. BMC Biochem. 2007, 8 (1): S3.View ArticlePubMedPubMed CentralGoogle Scholar
- Stenvinkel P, Ketteler M, Johnson R, Lindholm B, Pecoits-Filho R, Riella M, et al: IL-10, IL-6, and TNF-alpha: central factors in the altered cytokine network of uremia–the good, the bad, and the ugly. Kidney Int. 2005, 67 (4): 1216-1233.View ArticlePubMedGoogle Scholar
- Lim W, Kireta S, Leedham E, Russ G, Coates P: Uremia impairs monocyte and monocyte-derived dendritic cell function in hemodialysis patients. Kidney Int. 2007, 72 (9): 1138-1148.View ArticlePubMedGoogle Scholar
- Ankersmit HJ, Deicher R, Moser B, Teufel I, Roth G, Gerlitz S, et al: Impaired T cell proliferation, increased soluble death-inducing receptors and activation-induced T cell death in patients undergoing haemodialysis. Clin Exp Immunol. 2001, 125 (1): 142-148.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Ying L, Naesens M, Xiao W, Sigdel T, Hsieh S, et al: Interference of globin genes with biomarker discovery for allograft rejection in peripheral blood samples. Physiol Genomics. 2008, 32 (2): 190-197.View ArticlePubMedGoogle Scholar
- Winn ME, Zapala MA, Hovatta I, Risbrough VB, Lillie E, Schork NJ: The effects of globin on microarray-based gene expression analysis of mouse blood. Mamm Genome. 2010, 21 (5–6): 268-275.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu J, Walter E, Stenger D, Thach D: Effects of globin mRNA reduction methods on gene expression profiles from whole blood. J Mol Diagn. 2006, 8 (5): 551-558.View ArticlePubMedPubMed CentralGoogle Scholar
- Ekström TJ, Stenvinkel P: The epigenetic conductor: a genomic orchestrator in chronic kidney disease complications?. J Nephrol. 2009, 22 (4): 442-449.PubMedGoogle Scholar
- Zhang L, Dai Y, Wang L, Peng W, Zhang Y, Ou Y, et al: CpG array analysis of histone H3 lysine 4 trimethylation in peripheral blood mononuclear cells of uremia patients. DNA Cell Biol. 2011, 30 (3): 179-186.View ArticlePubMedGoogle Scholar
- Levin A, Hemmelgarn B, Culleton B, Tobe S, McFarlane P, Ruzicka M, et al: Guidelines for the management of chronic kidney disease. CMAJ. 2008, 179 (11): 1154-1162.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/6/23/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.