As technology for microarray analysis becomes widespread, it is becoming increasingly important to be able to compare and combine the results of experiments that explore the same scientific question. In this article, we present a rank-aggregation approach for combining results from several microarray studies. The motivation for this approach is twofold; first, the final results of microarray studies are typically expressed as lists of genes, rank-ordered by a measure of the strength of evidence that they are functionally involved in the disease process, and second, using the information on this rank-ordered metric means that we do not have to concern ourselves with data on the actual expression levels, which may not be comparable across experiments. Our approach draws on methods for combining top-k lists from the computer science literature on meta-search. The meta-search problem shares several important features with that of combining microarray experiments, including the fact that there are typically few lists with many elements and the elements may not be common to all lists. We implement two meta-search algorithms, which use a Markov chain framework to convert pairwise preferences between list elements into a stationary distribution that represents an aggregate ranking (Dwork et al, 2001). We explore the behavior of the algorithms in hypothetical examples and a simulated dataset and compare their performance with that of an algorithm based on the order-statistics model of Thurstone (Thurstone, 1927). We apply all three algorithms to aggregate the results of five microarray studies of prostate cancer.

The circadian clock generates daily rhythms in mammalian liver processes, such as glucose and lipid homeostasis, xenobiotic metabolism, and regeneration. The mechanisms governing these rhythms are not well understood, particularly the distinct contributions of the cell-autonomous clock and central pacemaker to rhythmic liver physiology. Through microarray expression profiling in Met murine hepatocytes (MMH)-D3, we identified over 1,000 transcripts that exhibit circadian oscillations, demonstrating that the cell-autonomous clock can drive many rhythms, and that MMH-D3 is a valid circadian model system. The genes represented by these circadian transcripts displayed both cophasic and antiphasic organization within a protein-protein interaction network, suggesting the existence of competition for binding sites or partners by genes of disparate transcriptional phases. Multiple pathways displayed enrichment in MMH-D3 circadian transcripts, including the polyamine synthesis module of the glutathione metabolic pathway. The polyamine synthesis module, which is highly associated with cell proliferation and whose products are required for initiation of liver regeneration, includes enzymes whose transcripts exhibit circadian oscillations, such as ornithine decarboxylase and spermidine synthase. Metabolic profiling revealed that the enzymatic product of spermidine synthase, spermidine, cycles as well. Thus, the cell-autonomous hepatocyte clock can drive a significant amount of transcriptional rhythms and orchestrate physiologically relevant modules such as polyamine synthesis.networks | chronobiology | resistance distance M any aspects of mammalian physiology and behavior display circadian (∼24-h) rhythms, including the sleep/wake cycle, blood pressure, heart rate, metabolism, and liver regeneration (1, 2). These rhythms are regulated by the circadian clock, which enables consolidation and coordination of physiological events to specific phases of the 24-h cycle in anticipation of daily environmental changes. Dysfunction of the clock is associated with serious human health conditions, including shift work syndrome, sleep disorders, increased risk of cancer, cardiovascular disease, and metabolic syndrome (1, 2).The circadian clock is a self-sustaining, entrainable, cell-autonomous network of three interlocked transcriptional negative feedback loops (2). The primary loop consists of BMAL1/CLOCK transcriptional activators, which dimerize and turn on transcription of Period (Per1, Per2, and Per3) and Crytochrome (Cry1 and Cry2) genes through E-box elements. PER and CRY proteins dimerize and feed back to inhibit BMAL1/CLOCK activation. Two associate loops interlock with the core loop: the ROR/REV-ERB element (RRE) loop composed of ROR activators (RORa, RORb, and RORc) and REV-ERB repressors (REV-ERBα and REV-ERBβ), which compete for RRE transcription factor binding sites (TFBS), and the D-box loop composed of the activator DBP and repressor E4BP4, which act through D-box TFBS (2).In addition to internal regulation of clock genes,...

Purpose: To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m 2 ) 6 months from extirpative renal surgery (nephron-sparing surgery [NSS] or radical nephrectomy [RN]). Patients and Methods: Retrospective analysis of patients who underwent NSS or RN at our institution from January 2000 to June 2013 with a compatible CT scan 6-month renal function follow-up was performed. Primary outcome was defined as the accuracy of 6-month postoperative eGFR compared with actual postoperative eGFR based on root mean square error (RMSE). Models were constructed using renal volumes and externally validated. A clinical tool was developed on the best model after a given surgical procedure using area under the curve (AUC). Results: We identified 130 (51 radical, 79 partial) patients with a median age of 58 years (interquartile range [IQR] 48-67) and preoperative eGFR of 82.1 (IQR 65.9-104.3); postoperative CKD (eGFR <60) developed in 42% (55/130). We performed various linear regression models to predict postoperative eGFR. The Quadratic model was the highest performing model, which relied only on preoperative GFR and the volumetric data for a RMSE of 15.3 on external validation corresponding to a clinical tool with an AUC of 0.89. Conclusion: Volumetric-based assessment provides information to predict postoperative eGFR. A tool based on this equation may assist surgical counseling regarding renal functional outcomes before renal tumor surgical procedures.

No abstract