Autism genetics: Methodological issues and experimental design

Autism is a complex neuropsychiatric disorder of developmental origin, where multiple genetic and environmental factors likely interact resulting in a clinical continuum between affected and unaffected individuals in the general population. During the last two decades, relevant progress has been made in identifying chromosomal regions and genes in linkage or association with autism, but no single gene has emerged as a major cause of disease in a large number of patients. The purpose of this paper is to discuss specific methodological issues and experimental strategies in autism genetic research, based on fourteen years of experience in patient recruitment and association studies of autism spectrum disorder in Italy.     

The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales.

Proteomic expression patterns derived from mass spectrometry have been put forward as potential biomarkers for the early diagnosis of cancer and other diseases. This approach has generated much excitement and has led to a large number of new experiments and vast amounts of new data. The data, derived at great expense, can have very little value if careful attention is not paid to the experimental design and analysis. Using examples from surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) and matrix-assisted laser desorption-ionisation/time-of-flight (MALDI-TOF) experiments, we describe several experimental design issues that can corrupt a dataset. Fortunately, the problems we identify can be avoided if attention is paid to potential sources of bias before the experiment is run. With an appropriate experimental design, proteomics technology can be a useful tool for discovering important information relating protein expression to disease.     

Statistical quality control of variety trial data

I propose detection criteria for identifying an abnormal or erroneous data vector provided by a single variety trial in a longer series of variety trials. The test criteria are based on the linear effects estimated separately for each studied trial using global variance components estimated from the whole series of variety trials. The criteria comprise three mutually independent test statistics. The first one is a quadratic form in the estimated fixed effects, the second one is a quadratic form in the estimated realized linear random effects not including the residual effects, and the third one is a quadratic form in the estimated realized residual effects. Under the null hypothesis defining a valid data vector, the three quadratics have independent chi2 distributions. Under natural alternative hypotheses, they have noncentral chi2 distributions. Decomposing the total variation of the data vector studied into quadratic forms due to different types of the realized linear effects intuitively justifies the resulting test criteria. The decomposition may also be used to show that the resulting tests are likelihood ratio tests. I further present computational procedures that allow us to dispense with the need for prior estimation of the linear effects.     

Meal fatty acid uptake in human adipose tissue: technical and experimental design issues

The adipose tissue uptake of dietary fat has been studied using fatty acid radiotracers incorporated into a meal, followed by adipose tissue biopsies. A number of experimental design issues, including the use of isotopic tracers to measure meal fatty acid oxidation and plasma appearance of tracer, as well as the heterogeneity of adipose tissue fatty acid uptake, have been addressed. We examined these questions in a study of 24 volunteers (12 men and 12 women) who consumed a meal containing [(3)H]triolein and [(14)C]triolein. Slight differences in the purity of [(3)H]triolein vs. [(14)C]triolein were found, which could affect the apparent adipose tissue uptake of meal fatty acids. The adipose tissue triglyceride specific activity from bilateral biopsy sites agreed well, implying that a unilateral biopsy is satisfactory for measuring tracer uptake. Meal fatty acid oxidation measured using [(3)H]triolein and [(14)C]triolein was well correlated (r = 0.79, P < 0.0001). The peak tracer appearance in plasma chylomicrons occurred 1 h after the ingestion of a second, unlabeled meal. Our findings have implications for the experimental design of future meal fatty acid tracer/adipose tissue biopsy studies.     

Statistical vs. Stochastic experimental design: An experimental comparison on the example of protein refolding

Optimization of experimental problems is a challenging task in both engineering and science. In principle, two different design of experiments (DOE) strategies exist: statistical and stochastic methods. Both aim to efficiently and precisely identify optimal solutions inside the problem‐specific search space. Here, we evaluate and compare both strategies on the same experimental problem, the optimization of the refolding conditions of the lipase from Thermomyces lanuginosus with 26 variables under study. Protein refolding is one of the main bottlenecks in the process development for recombinant proteins. Despite intensive effort, the prediction of refolding from sequence information alone is still not applicable today. Instead, suitable refolding conditions are typically derived empirically in large screening experiments. Thus, protein refolding should constitute a good performance test for DOE strategies. We compared an iterative stochastic optimization applying a genetic algorithm and a standard statistical design consisting of a D‐optimal screening step followed by an optimization via response surface methodology. Our results revealed that only the stochastic optimization was able to identify optimal refolding conditions (～1.400 U g^?1 refolded activity), which were 3.4‐fold higher than the standard. Additionally, the stochastic optimization proved quite robust, as three independent optimizations performed similar. In contrast, the statistical DOE resulted in a suboptimal solution and failed to identify comparable activities. Interactions between process variables proved to be pivotal for this optimization. Hence, the linear screening model was not able to identify the most important process variables correctly. Thereby, this study highlighted the limits of the classic two‐step statistical DOE. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Mass customization: manufacturing issues and taxonomic analyses

As the field of mass customization (MC) attains the status of a mature discipline, two significant research deficits stand out. First, a through metareview of the entire body of MC research that looks at the application value and rigorousness of research is overdue. Second, manufacturing issues, especially those pertaining to quality and the supply chain have been largely ignored. This issue is dedicated to both of these important areas of research. The conclusion with regards to the status of the MC field is that it is currently vibrant, with growing research volume and applications. The manufacturing issues dealt with in this issue are strategically important, dealing with quality and customization issues. The work on quality is the first of its kind: it seeks to generate a defect-tracking matrix consistent with product configurations, enabling agile identification of defects in a mass customization environment. The use of discrete event simulation to deal with the dynamically evolving customized demand so as to minimize cost and schedule disruption is innovative, timely, and profound.     

Statistical issues with microarrays: processing and analysis

The study of gene expression with printed arrays and prefabricated chips is evolving from a qualitative to a quantitative science. Statistical procedures for determining quality control, differential expression, and reproducibility of findings are a natural consequence of this evolution. However, problems inherent to the technologies have raised important issues of how to apply adequate statistical tests. As a consequence, statistical approaches to microarray research are not yet as routine as they are in other sciences. Statistical methods, tailored to microarrays, continue to be adapted and developed. We present an overview of these methods and of outstanding issues in their use and validation.     

Comparative proteomic and radiobiological analyses in human lung adenocarcinoma cells

In clinic, many non-small cell lung cancer (NSCLC) patients receive radiation therapy after chemotherapy failure. However, whether the multidrug resistance (MDR) can elevate the radioresistance (RDR) remains unclear. To evaluate the MDR's effect on the RDR, screen MDR- and RDR-related proteins in human lung adenocarcinoma (HLA) cells and tissues A549, and A549/DDP cells after irradiation were analyzed by colony-forming assay and flow cytometry. Two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were utilized to identify differentially expressed proteins (DEPs) between them. The value of D0, Dq, and SF2 increased, the mean percentage in G2 phase and apoptosis rate significantly decreased in A549/DDP cells compared with A549 cells. 40 DEP points were found, and among them 27 were identified through proteomics. Four up-regulated proteins (HSPB1, Vimentin, Cofilin-1, and Annexin A4) in MDR cells compared with non-MDR cells, were confirmed by Western blot. Immuno-histochemistry showed that they were also over-expressed in MDR tissues compared with non-MDR counterparts of HLA. These results proved that the MDR in HLA cells and tissues increased the RDR. HSPB1, Vimentin, Cofilin-1, and Annexin A4 are potential biomarkers for predicting HLA response to MDR and RDR, and novel treatment targets of HLA.     

Statistical analysis of the experimental variation in the proteomic characterization of human plasma by two-dimensional difference gel electrophoresis

The complexity of human plasma presents a number of challenges to the efficient and reproducible proteomic analysis of differential expression in response to disease. Before individual variation and disease-specific protein biomarkers can be identified from human plasma, the experimental variability inherent in the protein separation and detection techniques must be quantified. We report on the variation found in two-dimensional difference gel electrophoresis (2-D DIGE) analysis of human plasma. Eight aliquots of a human plasma sample were subjected to top-6 highest abundant protein depletion and were subsequently analyzed in triplicate for a total of 24 DIGE samples on 12 gels. Spot-wise standard deviation estimates indicated that fold changes greater than 2 can be detected with a manageable number of replicates in simple ANOVA experiments with human plasma. Mixed-effects statistical modeling quantified the effect of the dyes, and segregated the spot-wise variance into components of sample preparation, gel-to-gel differences, and random error. The gel-to-gel component was found to be the largest source of variation, followed by the sample preparation step. An improved protocol for the depletion of the top-6 high-abundance proteins is suggested, which, along with the use of statistical modeling and future improvements in gel quality and image processing, can further reduce the variation and increase the efficiency of 2-D DIGE proteomic analysis of human plasma.     

ProteinSeq: high-performance proteomic analyses by proximity ligation and next generation sequencing

Despite intense interest, methods that provide enhanced sensitivity and specificity in parallel measurements of candidate protein biomarkers in numerous samples have been lacking. We present herein a multiplex proximity ligation assay with readout via realtime PCR or DNA sequencing (ProteinSeq). We demonstrate improved sensitivity over conventional sandwich assays for simultaneous analysis of sets of 35 proteins in 5 μl of blood plasma. Importantly, we observe a minimal tendency to increased background with multiplexing, compared to a sandwich assay, suggesting that higher levels of multiplexing are possible. We used ProteinSeq to analyze proteins in plasma samples from cardiovascular disease (CVD) patient cohorts and matched controls. Three proteins, namely P-selectin, Cystatin-B and Kallikrein-6, were identified as putative diagnostic biomarkers for CVD. The latter two have not been previously reported in the literature and their potential roles must be validated in larger patient cohorts. We conclude that ProteinSeq is promising for screening large numbers of proteins and samples while the technology can provide a much-needed platform for validation of diagnostic markers in biobank samples and in clinical use.     

Statistical diagnostics emerging from external quality control of real-time PCR

Besides the application of conventional qualitative PCR as a valuable tool to enrich or identify specific sequences of nucleic acids, a new revolutionary technique for quantitative PCR determination has been introduced recently. It is based on real-time detection of PCR products revealed as a homogeneous accumulating signal generated by specific dyes. However, as far as we know, the influence of the variability of this technique on the reliability of the quantitative assay has not been thoroughly investigated. A national program of external quality assurance (EQA) for real-time PCR determination involving 42 Italian laboratories has been developed to assess the analytical performance of real-time PCR procedures. Participants were asked to perform a conventional experiment based on the use of an external reference curve (standard curve) for real-time detection of three cDNA samples with different concentrations of a specific target. In this paper the main analytical features of the standard curve have been investigated in an attempt to produce statistical diagnostics emerging from external quality control. Specific control charts were drawn to help biochemists take technical decisions aimed at improving the performance of their laboratories. Overall, our results indicated a subset of seven laboratories whose performance appeared to be markedly outside the limits for at least one of the standard curve features investigated. Our findings suggest the usefulness of the approach presented here for monitoring the heterogeneity of results produced by different laboratories and for selecting those laboratories that need technical advice on their performance.     

Strategy to design improved proteomic experiments based on statistical analyses of the chemical properties of identified peptides

Proteomics is an emerging field that uses many types of proteomic platforms however has few standardized procedures. Deciding which platform to use to perform large-scale proteomic studies is either based on personal preference or on so-called "figures of merit" such as dynamic range, resolution, and the limit of detection; these factors are often insufficient to predict the outcome of the experiment as the detection of peptides correlates to the chemical properties of each peptide. There is a need for a novel figure of merit that describes the overall performance of a platform based on measured output, which in proteomics is often a list of identified peptides. We report the development of such a figure of merit based on a predictive genetic algorithm. This algorithm takes into account the properties of the observed peptides such as length, hydrophobicity, and pI. Several large-scale studies that differed in sample type or platform were used to demonstrate the usefulness of the algorithm for improved experimental design. The figures that were obtained were clustered to find platforms that were biased in similar ways. Even though some platforms are different, they lead to the identification of similar peptide types and are thus redundant. The algorithm can thus be used as an exploratory tool to suggest a minimal number of complementary experiments in order to maximize experimental efficiency.     

The response of human colonocytes to folate deficiency in vitro: functional and proteomic analyses

Low folate intake is associated with colon cancer. We combined a proteomics and biochemical approach to identify proteins and pathways affected by folate deficiency in human colonocytes. Folate differentially altered activity and expression of proteins involved in proliferation [e.g., PCNA], DNA repair [e.g., XRCC5, MSH2], apoptosis [e.g., BAG family chaperone protein, DIABLO and porin], cytoskeletal organization [e.g., actin, ezrin, elfin], and expression of proteins implicated in malignant transformation [COMT, Nit2].     

Statistical and graphical methods for quality control determination of high-throughput screening data

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server HTS application, a custom-developed software tool built from the commercially available S-PLUS and Stat Server statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.     

Statistical analyses of repeated measures in physiological research: a tutorial

Experimental designs involving repeated measurements on experimental units are widely used in physiological research. Often, relatively many consecutive observations on each experimental unit are involved and the data may be quite nonlinear. Yet evidently, one of the most commonly used statistical methods for dealing with such data sets in physiological research is the repeated-measurements ANOVA model. The problem herewith is that it is not well suited for data sets with many consecutive measurements; it does not deal with nonlinear features of the data, and the interpretability of the model may be low. The use of inappropriate statistical models increases the likelihood of drawing wrong conclusions. The aim of this article is to illustrate, for a reasonably typical repeated-measurements data set, how fundamental assumptions of the repeated-measurements ANOVA model are inappropriate and how researchers may benefit from adopting different modeling approaches using a variety of different kinds of models. We emphasize intuitive ideas rather than mathematical rigor. We illustrate how such models represent alternatives that 1) can have much higher interpretability, 2) are more likely to meet underlying assumptions, 3) provide better fitted models, and 4) are readily implemented in widely distributed software products.     

Peptidomic and proteomic analyses of the systemic immune response of Drosophila

Insects have developed an efficient host defense against microorganisms, which involves humoral and cellular mechanisms. Numerous data highlight similarities between defense responses of insects and innate immunity of mammals. The fruit fly, Drosophila melanogaster, is a favorable model system for the analysis of the first line defense against microorganisms. Taking advantages of improvements in mass spectrometry (MS), two-dimensional (2D) gel electrophoresis and bioinformatics, differential analyses of blood content (hemolymph) from immune-challenged versus control Drosophila were performed. Two strategies were developed: (i) peptidomic analyses through matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS and high performance liquid chromatography for molecules below 15 kDa, and (ii) proteomic studies based on 2D gel electrophoresis, MALDI-TOF fingerprinting and database searches, for compounds of greater molecular masses. The peptidomic strategy led to the detection of a large number of peptides induced in the hemolymph of challenged flies as compared to controls. Of these, 28 were characterized, amongst which were antimicrobial peptides. The 2D gel electrophoresis strategy led to the detection of 70 spots differentially regulated by at least fivefold after microbial infection. This approach yielded the identity of a series of proteins that were related to the Drosophila immune response, such as proteases, protease inhibitors, prophenoloxydase-activating enzymes, serpins and a Gram-negative binding protein-like protein. This strategy also brought to light new candidates with a potential function in the immune response (odorant-binding protein, peptidylglycine alpha-hydroxylating monooxygenase and transferrin). Interestingly, several molecules resulting from the cleavage of proteins were detected after a fungal infection. Together, peptidomic and proteomic analyses represent new tools to characterize molecules involved in the innate immune reactions of Drosophila.