Serum Concentrations of Symmetric Dimethylarginine and Creatinine in Dogs with Naturally Occurring Chronic Kidney Disease

Citation: Hall, J. A., Yerramilli, M., Obare, E., Yerramilli, M., Almes, K., & Jewell, D. E. (2016). Serum Concentrations of Symmetric Dimethylarginine and Creatinine in Dogs with Naturally Occurring Chronic Kidney Disease. Journal of Veterinary Internal Medicine, 30(3), 794-802. doi:10.1111/jvim.13942Background: Serum concentrations of symmetric dimethylarginine (SDMA) detected chronic kidney disease (CKD) in cats an average of 17.0 months before serum creatinine (Cr) concentrations increased above the reference interval. Objectives: To report on the utility of measuring serum SDMA concentrations in dogs for detection of CKD before diagnosis by measurement of serum Cr. Animals: CKD dogs (n = 19) included those persistently azotemic for ?3 months (n = 5), dogs that were azotemic at the time of death (n = 4), and nonazotemic dogs (n = 10). CKD dogs were compared with healthy control dogs (n = 20). Methods: Retrospective study, whereby serum Cr concentrations were determined by enzymatic colorimetry and serum SDMA concentrations were determined by liquid chromatography-mass spectrometry in dogs with necropsy confirmed CKD. Results: Serum SDMA increased before serum Cr in 17 of 19 dogs (mean, 9.8 months; range, 2.2-27.0 months). Duration of elevations in serum SDMA concentrations before the dog developed azotemia (N = 1) or before the dog died (N = 1) was not determined. Serum SDMA and Cr concentrations were linearly related (r = 0.84; P < .001). Serum SDMA (r = -0.80) and serum Cr (r = -0.89) concentrations were significantly related to glomerular filtration rate (both P < .001). Conclusion and Clinical Importance: Using serum SDMA as a biomarker for CKD allows earlier detection of kidney dysfunction in dogs than does measurement of serum Cr. Earlier detection might be desirable for initiating renoprotective interventions that slow progression of kidney disease. © 2016 American College of Veterinary Internal Medicine

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Adipose tissue pathways involved in weight loss of cancer cachexia

White adipose tissue (WAT) constitutes our most expandable tissue and largest endocrine organ secreting hundreds of polypeptides collectively termed adipokines. Changes in WAT mass induce alterations in adipocyte secretion and function, which are linked to disturbed whole-body metabolism. Although the mechanisms controlling this are not clear they are dependent on changes in gene expression, a complex process which is regulated at several levels. Results in recent years have highlighted the role of small non-coding RNA molecules termed microRNAs (miRNAs), which regulate gene expression via post-transcriptional mechanisms. The aim of this thesis was to characterize global gene expression levels and describe novel miRNAs and adipokines controlling the function of human WAT in conditions with pathological increases or decreases in WAT mass. Obesity and cancer cachexia were selected as two models since they are both clinically relevant and characterized by involuntary changes in WAT mass. In Study I, expressional analyses were performed in subcutaneous WAT from cancer patients with or without cachexia and obese versus non-obese subjects. In total, 425 transcripts were found to be regulated in cancer cachexia. Pathway analyses based on this set of genes revealed that processes involving extracellular matrix, actin cytoskeleton and focal adhesion were significantly downregulated, whereas fatty acid metabolism was upregulated comparing cachectic with weight-stable cancer subjects. Furthermore, by overlapping these results with microarray data from an obesity study, many transcripts were found to be reciprocally regulated comparing the two conditions. This suggests that WAT gene expression in cancer cachexia and obesity are regulated by similar, albeit opposing, mechanisms. In Study II, the focus was on the family of fibroblast growth factors (FGFs), members of which have recently been implicated in the development of obesity and insulin resistance. A retrospective analysis of global gene expression data identified several FGFs (FGF1/2/7/9/13/18) to be expressed in WAT. However, only one, FGF1, was actively secreted from WAT and predominantly so from the adipocyte fraction. Moreover, FGF1 release was increased in obese compared to non-obese subjects, but was not normalized by weight loss. Although the clinical significance of these findings is not yet clear, it can be hypothesized that FGF1 may play a role in WAT growth, possibly by promoting fat cell proliferation and/or differentiation. In Study III, we identified adipose miRNAs regulated in obesity. Out of eleven miRNAs regulated by changes in body fat mass, ten controlled the production of the pro-inflammatory chemoattractant chemokine (C-C motif) ligand 2 (CCL2) when overexpressed in fat cells and for two, miR-126 and -193b, signaling circuits were defined. In Study IV, a novel adipokine, semaphorin 3C (SEMA3C), was identified by combining transcriptome and secretome data. Detailed studies focusing on SEMA3C revealed that this factor was secreted from adipocytes and induced the expression of extracellular matrix and matricellular genes in preadipocytes. Furthermore, SEMA3C mRNA levels correlated with interstitial fibrosis and insulin resistance in WAT derived from subjects with a wide range in BMI. In summary, the results presented in this thesis have delineated transcriptional alterations in WAT in two clinically relevant conditions, obesity and cancer cachexia. This has allowed the identification of novel adipokines and microRNAs with potential pathophysiological importance. These findings form the basis for further studies aiming at understanding the central role of WAT in disorders associated with metabolic complications

Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis