High Resolution Discovery Proteomics Reveals Candidate Disease Progression Markers of Alzheimer's Disease in Human Cerebrospinal Fluid.

Disease modifying treatments for Alzheimer's disease (AD) constitute a major goal in medicine. Current trends suggest that biomarkers reflective of AD neuropathology and modifiable by treatment would provide supportive evidence for disease modification. Nevertheless, a lack of quantitative tools to assess disease modifying treatment effects remains a major hurdle. Cerebrospinal fluid (CSF) biochemical markers such as total tau, p-tau and Ab42 are well established markers of AD; however, global quantitative biochemical changes in CSF in AD disease progression remain largely uncharacterized. Here we applied a high resolution open discovery platform, dMS, to profile a cross-sectional cohort of lumbar CSF from post-mortem diagnosed AD patients versus those from non-AD/non-demented (control) patients. Multiple markers were identified to be statistically significant in the cohort tested. We selected two markers SME-1 (p<0.0001) and SME-2 (p = 0.0004) for evaluation in a second independent longitudinal cohort of human CSF from post-mortem diagnosed AD patients and age-matched and case-matched control patients. In cohort-2, SME-1, identified as neuronal secretory protein VGF, and SME-2, identified as neuronal pentraxin receptor-1 (NPTXR), in AD were 21% (p = 0.039) and 17% (p = 0.026) lower, at baseline, respectively, than in controls. Linear mixed model analysis in the longitudinal cohort estimate a decrease in the levels of VGF and NPTXR at the rate of 10.9% and 6.9% per year in the AD patients, whereas both markers increased in controls. Because these markers are detected by mass spectrometry without the need for antibody reagents, targeted MS based assays provide a clear translation path for evaluating selected AD disease-progression markers with high analytical precision in the clinic.

A performance evaluation of three drug-induced liver injury biomarkers in the rat: alpha-glutathione S-transferase, arginase 1, and 4-hydroxyphenyl-pyruvate dioxygenase.

Alanine aminotransferase (ALT) activity is the most frequently relied upon reference standard for monitoring liver injury in humans and nonclinical species. However, limitations of ALT include a lack of specificity for diagnosing liver injury (e.g., present in muscle and the gastrointestinal tract), its inability to monitor certain types of hepatic injury (e.g., biliary injury), and ambiguity with respect to interpretation of modest or transient elevations (< 3× upper limit of normal). As an initial step to both understand and qualify additional biomarkers of hepatotoxicity that may add value to ALT, three novel candidates have been evaluated in 34 acute toxicity rat studies: (1) alpha-glutathione S-transferase (GSTA), (2) arginase 1 (ARG1), and (3) 4-hydroxyphenylpyruvate dioxygenase (HPD). The performance of each biomarker was assessed for its diagnostic ability to accurately detect hepatocellular injury (i.e., microscopic histopathology), singularly or in combination with ALT. All three biomarkers, either alone or in combination with ALT, improved specificity when compared with ALT alone. Hepatocellular necrosis and/or degeneration were detected by all three biomarkers in the majority of animals. ARG1 and HPD were also sensitive in detecting single-cell necrosis in the absence of more extensive hepatocellular necrosis/degeneration. ARG1 showed the best sensitivity for detecting biliary injury with or without ALT. All the biomarkers were able to detect biliary injury with single-cell necrosis. Taken together, these novel liver toxicity biomarkers, GSTA, ARG1, and HPD, add value (both enhanced specificity and sensitivity) to the measurement of ALT alone for monitoring drug-induced liver injury in rat.

Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium.

The first formal qualification of safety biomarkers for regulatory decision making marks a milestone in the application of biomarkers to drug development. Following submission of drug toxicity studies and analyses of biomarker performance to the Food and Drug Administration (FDA) and European Medicines Agency (EMEA) by the Predictive Safety Testing Consortium's (PSTC) Nephrotoxicity Working Group, seven renal safety biomarkers have been qualified for limited use in nonclinical and clinical drug development to help guide safety assessments. This was a pilot process, and the experience gained will both facilitate better understanding of how the qualification process will probably evolve and clarify the minimal requirements necessary to evaluate the performance of biomarkers of organ injury within specific contexts.

Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens.

RNA interference (RNAi) not only plays an important role in drug discovery but can also be developed directly into drugs. RNAi high-throughput screening (HTS) biotechnology allows us to conduct genome-wide RNAi research. A central challenge in genome-wide RNAi research is to integrate both experimental and computational approaches to obtain high quality RNAi HTS assays. Based on our daily practice in RNAi HTS experiments, we propose the implementation of 3 experimental and analytic processes to improve the quality of data from RNAi HTS biotechnology: (1) select effective biological controls; (2) adopt appropriate plate designs to display and/or adjust for systematic errors of measurement; and (3) use effective analytic metrics to assess data quality. The applications in 5 real RNAi HTS experiments demonstrate the effectiveness of integrating these processes to improve data quality. Due to the effectiveness in improving data quality in RNAi HTS experiments, the methods and guidelines contained in the 3 experimental and analytic processes are likely to have broad utility in genome-wide RNAi research.

Androgens drive divergent responses to salt stress in male versus female rat kidneys.

Dahl-Iwai (DI) salt-sensitive rats were studied using microarrays to identify sex-specific differences in the kidney, both basal differences and differences in responses to a high-salt diet. In DI rat kidneys, gene expression profiles demonstrated inflammatory and fibrotic responses selectively in females. Gonadectomy of DI rats abrogated sex differences in gene expression. Gonadectomized female and gonadectomized male DI rats both responded to high salt with the same spectrum of gene expression changes as intact female DI rats. Androgens dominated the sex-selective responses to salt. Several androgen-responsive genes with roles potentiating the differential responses to salt were identified, including increased male expression of angiotensin-vasopressin receptor and prolactin receptor, decreased 5 alpha-reductase, and mixed increases and decreases in expression of Cyp4a genes that can produce eicosanoid hormones. These sex differences potentiate sodium retention by males and increase kidney function during gestation in females.

Robust statistical methods for hit selection in RNA interference high-throughput screening experiments.

RNA interference (RNAi) high-throughput screening (HTS) experiments carried out using large (>5000 short interfering [si]RNA) libraries generate a huge amount of data. In order to use these data to identify the most effective siRNAs tested, it is critical to adopt and develop appropriate statistical methods. To address the questions in hit selection of RNAi HTS, we proposed a quartile-based method which is robust to outliers, true hits and nonsymmetrical data. We compared it with the more traditional tests, mean +/- k standard deviation (SD) and median +/- 3 median of absolute deviation (MAD). The results suggested that the quartile-based method selected more hits than mean +/- k SD under the same preset error rate. The number of hits selected by median +/- k MAD was close to that by the quartile-based method. Further analysis suggested that the quartile-based method had the greatest power in detecting true hits, especially weak or moderate true hits. Our investigation also suggested that platewise analysis (determining effective siRNAs on a plate-by-plate basis) can adjust for systematic errors in different plates, while an experimentwise analysis, in which effective siRNAs are identified in an analysis of the entire experiment, cannot. However, experimentwise analysis may detect a cluster of true positive hits placed together in one or several plates, while platewise analysis may not. To display hit selection results, we designed a specific figure called a plate-well series plot. We thus suggest the following strategy for hit selection in RNAi HTS experiments. First, choose the quartile-based method, or median +/- k MAD, for identifying effective siRNAs. Second, perform the chosen method experimentwise on transformed/normalized data, such as percentage inhibition, to check the possibility of hit clusters. If a cluster of selected hits are observed, repeat the analysis based on untransformed data to determine whether the cluster is due to an artifact in the data. If no clusters of hits are observed, select hits by performing platewise analysis on transformed data. Third, adopt the plate-well series plot to visualize both the data and the hit selection results, as well as to check for artifacts.

A comprehensive transcript index of the human genome generated using microarrays and computational approaches.

BACKGROUND: Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. RESULTS: The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. CONCLUSIONS: These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized.