Natasha A. Karp

Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress.

The etiology and pathophysiology of schizophrenia remain unknown. A parallel transcriptomics, proteomics and metabolomics approach was employed on human brain tissue to explore the molecular disease signatures. Almost half the altered proteins identified by proteomics were associated with mitochondrial function and oxidative stress responses. This was mirrored by transcriptional and metabolite perturbations. Cluster analysis of transcriptional alterations showed that genes related to energy metabolism and oxidative stress differentiated almost 90% of schizophrenia patients from controls, while confounding drug effects could be ruled out. We propose that oxidative stress and the ensuing cellular adaptations are linked to the schizophrenia disease process and hope that this new disease concept may advance the approach to treatment, diagnosis and disease prevention of schizophrenia and related syndromes.

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variance-stabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variance-stabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry.

Genome-wide Generation and Systematic Phenotyping of Knockout Mice Reveals New Roles for Many Genes

Summary Mutations in whole organisms are powerful ways of interrogating gene function in a realistic context. We describe a program, the Sanger Institute Mouse Genetics Project, that provides a step toward the aim of knocking out all genes and screening each line for a broad range of traits. We found that hitherto unpublished genes were as likely to reveal phenotypes as known genes, suggesting that novel genes represent a rich resource for investigating the molecular basis of disease. We found many unexpected phenotypes detected only because we screened for them, emphasizing the value of screening all mutants for a wide range of traits. Haploinsufficiency and pleiotropy were both surprisingly common. Forty-two percent of genes were essential for viability, and these were less likely to have a paralog and more likely to contribute to a protein complex than other genes. Phenotypic data and more than 900 mutants are openly available for further analysis. PaperClip

Glucocorticoid signaling synchronizes the liver circadian transcriptome.

Circadian control of physiology is mediated by local, tissue‐based clocks, synchronized to each other and to solar time by signals from the suprachiasmatic nuclei (SCN), the master oscillator in the hypothalamus. These local clocks coordinate the transcription of key pathways to establish tissue‐specific daily metabolic programs. How local transcriptomes are synchronized across the organism and their relative contribution to circadian output remain unclear. In the present study we showed that glucocorticoids alone are able to synchronize expression of about 60% of the circadian transcriptome. We propose that synchronization occurs directly by the action of glucocorticoids on a diverse range of downstream targets and indirectly by regulating the core clock genes mPer1, Bmal1, mCry1, and Dbp. We have identified the pivotal liver transcription factor, HNF4α, as a mediator of circadian and glucocorticoid‐regulated transcription, showing that it is a key conduit for downstream targeting. Conclusion: We have demonstrated that by orchestrating transcriptional cascades, glucocorticoids are able to direct synchronization of a diverse range of functionally important circadian genes. (HEPATOLOGY 2007;45:1478–1488.)

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

In quantitative proteomics, the false discovery rate (FDR) can be defined as the number of false positives within statistically significant changes in expression. False positives accumulate during the simultaneous testing of expression changes across hundreds or thousands of protein or peptide species when univariate tests such as the Students t test are used. Currently most researchers rely solely on the estimation of p values and a significance threshold, but this approach may result in false positives because it does not account for the multiple testing effect. For each species, a measure of significance in terms of the FDR can be calculated, producing individual q values. The q value maintains power by allowing the investigator to achieve an acceptable level of true or false positives within the calls of significance. The q value approach relies on the use of the correct statistical test for the experimental design. In this situation, a uniform p value frequency distribution when there are no differences in expression between two samples should be obtained. Here we report a bias in p value distribution in the case of a three-dye DIGE experiment where no changes in expression are occurring. The bias was shown to arise from correlation in the data from the use of a common internal standard. With a two-dye schema, where each sample has its own internal standard, such bias was removed, enabling the application of the q value to two different proteomics studies. In the case of the first study, we demonstrate that 80% of calls of significance by the more traditional method are false positives. In the second, we show that calculating the q value gives the user control over the FDR. These studies demonstrate the power and ease of use of the q value in correcting for multiple testing. This work also highlights the need for robust experimental design that includes the appropriate application of statistical procedures.

The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data

The International Mouse Phenotyping Consortium (IMPC) web portal (http://www.mousephenotype.org) provides the biomedical community with a unified point of access to mutant mice and rich collection of related emerging and existing mouse phenotype data. IMPC mouse clinics worldwide follow rigorous highly structured and standardized protocols for the experimentation, collection and dissemination of data. Dedicated ‘data wranglers’ work with each phenotyping center to collate data and perform quality control of data. An automated statistical analysis pipeline has been developed to identify knockout strains with a significant change in the phenotype parameters. Annotation with biomedical ontologies allows biologists and clinicians to easily find mouse strains with phenotypic traits relevant to their research. Data integration with other resources will provide insights into mammalian gene function and human disease. As phenotype data become available for every gene in the mouse, the IMPC web portal will become an invaluable tool for researchers studying the genetic contributions of genes to human diseases.

Investigating sample pooling strategies for DIGE experiments to address biological variability.

If biological questions are to be answered using quantitative proteomics, it is essential to design experiments which have sufficient power to be able to detect changes in expression. Sample subpooling is a strategy that can be used to reduce the variance but still allow studies to encompass biological variation. Underlying sample pooling strategies is the biological averaging assumption that the measurements taken on the pool are equal to the average of the measurements taken on the individuals. This study finds no evidence of a systematic bias triggered by sample pooling for DIGE and that pooling can be useful in reducing biological variation. For the first time in quantitative proteomics, the two sources of variance were decoupled and it was found that technical variance predominates for mouse brain, while biological variance predominates for human brain. A power analysis found that as the number of individuals pooled increased, then the number of replicates needed declined but the number of biological samples increased. Repeat measures of biological samples decreased the numbers of samples required but increased the number of gels needed. An example cost benefit analysis demonstrates how researchers can optimise their experiments while taking into account the available resources.

DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results

MOTIVATION Two-dimensional Difference Gel Electrophoresis (DIGE) measures expression differences for thousands of proteins in parallel. In contrast to DNA microarray analysis, however, there have been few systematic studies on the validity of differential protein expression analysis, and the effects of normalization methods have not yet been investigated. To address this need, we assessed a series of same-same comparisons, evaluating how random experimental variance influenced differential expression analysis. RESULTS The strong fluctuations observed were reflected in large discrepancies between the distributions of the spot intensities for different gels. Correct normalization for pooling of multiple gels for analysis is, therefore, essential. We show that both dye-specific background levels and the differences in scale of the spot intensity distributions must be accounted for. A variance stabilizing transform that had been developed for DNA microarray analysis combined with a robust Z-score allowed the determination of gel-independent signal thresholds based on the empirical distributions from same-same comparisons. In contrast, similar thresholds holding up to cross-validation could not be proposed for data normalized using methods established in the field of proteomics. AVAILABILITY Software is available on request from the authors. SUPPLEMENTARY INFORMATION There is supplementary material available online at http://www.flychip.org.uk/kreil/pub/2dgels/

Protein profiling of human postmortem brain using 2-dimensional fluorescence difference gel electrophoresis (2-D DIGE).

Two-dimensional gel electrophoresis (2-D GE) is a key tool for comparative proteomics research. With its ability to separate complex protein mixtures with high resolution, 2-D GE is a technique commonly employed for protein profiling studies. Significant improvements have been made in 2-D GE technology with the development of two-dimensional fluorescence difference gel electrophoresis (2-D DIGE), where proteins are first labelled with one of three spectrally resolvable fluorescent cyanine dyes before being separated over first and second dimensions according to their charge and size, respectively. When used in conjunction with automated analysis packages, this multiplexing approach can accurately and reproducibly quantify protein expression for control and experimental groups. Differentially expressed proteins can be subsequently identified by mass spectrometric methods. Here, we describe the successful application and optimisation of 2-D DIGE technology for human postmortem brain studies. This technology, especially when coupled with other functional genomics approaches, such as transcriptomics and metabolomics studies, will enhance our current understanding of human disease and lead to new therapeutic and diagnostic possibilities.

Comparison of DIGE and post‐stained gel electrophoresis with both traditional and SameSpots analysis for quantitative proteomics

2‐DE is an important tool in quantitative proteomics. Here, we compare the deep purple (DP) system with DIGE using both a traditional and the SameSpots approach to gel analysis. Missing values in the traditional approach were found to be a significant issue for both systems. SameSpots attempts to address the missing value problem. SameSpots was found to increase the proportion of low volume data for DP but not for DIGE. For all the analysis methods applied in this study, the assumptions of parametric tests were met. Analysis of the same images gave significantly lower noise with SameSpots (over traditional) for DP, but no difference for DIGE. We propose that SameSpots gave lower noise with DP due to the stabilisation of the spot area by the common spot outline, but this was not seen with DIGE due to the co‐detection process which stabilises the area selected. For studies where measurement of small abundance changes is required, a cost–benefit analysis highlights that DIGE was significantly cheaper regardless of the analysis methods. For studies analysing large changes, DP with SameSpots could be an effective alternative to DIGE but this will be dependent on the biological noise of the system under investigation.