Proton pump inhibitor-induced risk of chronic kidney disease is associated with increase of indoxyl sulfate synthesis via inhibition of CYP2E1 protein degradation.

Proton pump inhibitors (PPIs) are widely used to treat acid-related disorders in the gastrointestinal tract; however, PPI use increases the risk of chronic kidney disease (CKD) through unclear mechanisms. Considering that PPIs disturb the gut microbiome balance, which is involved in the precursor of gut-derived uremic toxin accumulation, and that gut-derived uremic toxins aggravate CKD progression, the aim of this study is to elucidate whether PPIs affect gut-derived uremic toxin metabolism, including indoxyl sulfate (IS), p-cresyl sulfate, and trimethylamine-N-oxide, as a mechanism for causing CKD. The present study showed that 3 week-treatment of PPIs (omeprazole, lansoprazole, and pantoprazole at 30 mg/kg) in mice only increased IS plasma levels among the above three gut-derived uremic toxins. Additionally, lansoprazole increased IS plasma concentrations along with increased exposure dose (7.5-30 mg/kg) and duration (1-3 weeks). However, nephrotoxicity with mild changes in glomerular structure and signs of fibrosis were observed only in groups exposed to a 3-week treatment of PPIs (30 mg/kg). As the concentrations of indole (the precursor of IS from gut metabolism) in the colon were only increased in the pantoprazole-treated group, the mechanism of increased IS exposure remains unclear. Further studies revealed that PPIs (omeprazole and lansoprazole; but not pantoprazole) increased IS production from indole in primary mouse hepatocytes in a concentration-dependent manner. Additionally, the increased protein levels of hepatic CYP2E1 (the key enzyme mediating IS formation) due to suppressed degradation resulted in an increase in IS levels. Although omeprazole and lansoprazole significantly inhibited IS uptake in hOAT1/3 in vitro, 3 weeks of PPI treatment did not reduce IS renal excretion in mice. In conclusion, PPIs induced IS synthesis via increased hepatic CYP2E1 protein level, subsequently leading to increased IS exposure. These findings present a plausible biological mechanism to explain the association of PPI use with the increased risk of CKD.

ParaSAM: a parallelized version of the significance analysis of microarrays algorithm.

MOTIVATION: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. SUMMARY: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations. AVAILABILITY: A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx.

A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets.

MOTIVATION: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30,000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44,460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1). AVAILABILITY: The program is freely available and can be downloaded from http://www.amdcc.org/bioinformatics/bioinformatics.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular architecture of the DNA-binding region and its relationship to classification of basic helix-loop-helix proteins.

Multivariate statistical analyses are used to explore the molecular architecture of the DNA-binding and dimerization regions of basic helix-loop-helix (bHLH) proteins. Alphabetic amino acid data are transformed to biologically meaningful quantitative values using a set of 5 multivariate "indices." These multivariate indices summarize variation in a large suite of amino acid physiochemical attributes and reflect variability in polarity-accessibility-hydrophobicity, propensity for secondary structure, molecular size, codon composition, and electrostatic charge. Using these index score data, discriminant analyses describe the multidimensional aspects of physiochemical variation and clarify the structural basis of the prevailing evolutionary classification of bHLH proteins. A small number of amino acids from both the binding dimerization domains, when considered simultaneously, accurately distinguish the 5 known DNA-binding groups. The relevant sites often have well-documented structural and functional characteristics.

Solving the protein sequence metric problem.

Biological sequences are composed of long strings of alphabetic letters rather than arrays of numerical values. Lack of a natural underlying metric for comparing such alphabetic data significantly inhibits sophisticated statistical analyses of sequences, modeling structural and functional aspects of proteins, and related problems. Herein, we use multivariate statistical analyses on almost 500 amino acid attributes to produce a small set of highly interpretable numeric patterns of amino acid variability. These high-dimensional attribute data are summarized by five multidimensional patterns of attribute covariation that reflect polarity, secondary structure, molecular volume, codon diversity, and electrostatic charge. Numerical scores for each amino acid then transform amino acid sequences for statistical analyses. Relationships between transformed data and amino acid substitution matrices show significant associations for polarity and codon diversity scores. Transformed alphabetic data are used in analysis of variance and discriminant analysis to study DNA binding in the basic helix-loop-helix proteins. The transformed scores offer a general solution for analyzing a wide variety of sequence analysis problems.