A design and statistical perspective on microarray gene expression studies in nutrition: the need for playful creativity and scientific hard-mindedness

Discovery of nutritional biomarkers: future directions based on omics technologies

Understanding the interactions between food and human biology is of utmost importance to facilitate the development of more efficient nutritional interventions that might improve our wellness status and future health outcomes by reducing risk factors for non-transmittable chronic diseases, such as cardiovascular diseases, cancer, obesity and metabolic syndrome. Dissection of the molecular mechanisms that mediate the physiological effects of diets and bioactive compounds is one of the main goals of current nutritional investigation and the food industry as might lead to the discovery of novel biomarkers. It is widely recognized that the availability of robust nutritional biomarkers represents a bottleneck that delays the innovation process of the food industry. In this regard, omics sciences have opened up new avenues of research and opportunities in nutrition. Advances in mass spectrometry, nuclear magnetic resonance, next generation sequencing and microarray technologies allow massive genome, gene expression, proteomic and metabolomic profiling, obtaining a global and in-depth analysis of physiological/pathological scenarios. For this reason, omics platforms are most suitable for the discovery and characterization of novel nutritional markers that will define the nutritional status of both individuals and populations in the near future, and to identify the nutritional bioactive compounds responsible for the health outcomes.

Diet, genetics, and disease: a focus on the middle East and north Africa region

The Middle East and North Africa (MENA) region suffers a drastic change from a traditional diet to an industrialized diet. This has led to an unparalleled increase in the prevalence of chronic diseases. This review discusses the role of nutritional genomics, or the dietary signature, in these dietary and disease changes in the MENA. The diet-genetics-disease relation is discussed in detail. Selected disease categories in the MENA are discussed starting with a review of their epidemiology in the different MENA countries, followed by an examination of the known genetic factors that have been reported in the disease discussed, whether inside or outside the MENA. Several diet-genetics-disease relationships in the MENA may be contributing to the increased prevalence of civilization disorders of metabolism and micronutrient deficiencies. Future research in the field of nutritional genomics in the MENA is needed to better define these relationships.

Exploring the mechanisms behind S-adenosylmethionine (SAMe) in the treatment of osteoarthritis

Osteoarthritis is a chronic joint disease characterized by pain and immobility due to a gradual loss of cartilage. Current treatments are palliative; there is no cure. With a growing interest in alternative therapies, due in part to safety issues regarding pharmacological treatments like Celebrex, safe dietary compounds that help the body regenerate cartilage tissue are of great clinical importance. The dietary supplement S-adenosylmethionine (SAMe) shows such potential. Clinical trials have shown reduced pain and stiffness while in vitro and animal studies have shown SAMe can stimulate the production of cartilage which is critical in reversing the disease process. The author examines many potential mechanisms of action including: reduction of inflammatory mediators; increasing levels of glutathione; direct or indirect signaling of cartilage synthesis or survival; maintenance of DNA methylation. Research into the mechanisms of supplemental SAMe in osteoarthritis is necessary to evaluate the clinical effectiveness and safety of this dietary supplement.

Regulation of Cellular Metabolism and Cytokines by the Medicinal Herb Feverfew in the Human Monocytic THP-1 Cells

The herb feverfew is a folk remedy for various symptoms including inflammation. Inflammation has recently been implicated in the genesis of many diseases including cancers, atherosclerosis and rheumatoid arthritis. The mechanisms of action of feverfew in the human body are largely unknown. To determine the cellular targets of feverfew extracts, we have utilized oligo microarrays to study the gene expression profiles elicited by feverfew extracts in human monocytic THP-1 cells. We have identified 400 genes that are consistently regulated by feverfew extracts. Most of the genes are involved in cellular metabolism. However, the genes undergoing the highest degree of change by feverfew treatment are involved in other pathways including chemokine function, water homeostasis and heme-mediated signaling. Our results also suggest that feverfew extracts effectively reduce Lipopolysaccharides (LPS)-mediated TNF-alpha and CCL2 (MCP-1) releases by THP-1 cells. We hypothesize that feverfew components mediate metabolism, cell migration and cytokine production in human monocytes/macrophages.

Microarray data quality - review of current developments

DNA microarray technologies have evolved rapidly to become a key high-throughput technology for the simultaneous measurement of the relative expression levels of thousands of individual genes. However, despite the widespread adoption of DNA microarray technology, there remains considerable uncertainty and scepticism regarding data obtained using these technologies. Comparing results from seemingly identical experiments from different laboratories or even from different days can prove challenging; these challenges increase further when data from different array platforms need to be compared. To comply with emerging regulations, the quality of the data generated from array experiments needs to be clearly demonstrated. This review describes several initiatives that aim to improve confidence in data generated by array experiments, including initiatives to develop standards for data reporting and storage, external spike-in controls, quality control procedures, best practice guidelines, and quality metrics.

Microarray analysis

Microarrays and related technologies have allowed investigators to ask biological questions in far greater detail than has previously been possible. Microarrays had a troubled beginning, but most of these problems resulted from the growing pains of this technology, which, like many new things, was initially more promise than delivery. Nevertheless, over the past few years, investigators have learned how to achieve optimal performance of technology, and now exciting discoveries are made using microarray-based research. Many of the advances have come from the realization that microarrays are not a magic tool but rather are like any other measurement device. Unless microarray experimentation is coupled with good experimental practices, it will not yield valid results or, worse yet, may lead to misleading results. In this chapter, we highlight some of the important steps that should be taken to successfully conduct a microarray study. These steps include a clearly stated biological question, experimental design, careful experimental conduct, complete statistical analysis, validation/verification of results, and dissemination of the data.

Epistemological issues in omics and high-dimensional biology: give the people what they want

Gene expression microarrays have been the vanguard of new analytic approaches in high-dimensional biology. Draft sequences of several genomes coupled with new technologies allow study of the influences and responses of entire genomes rather than isolated genes. This has opened a new realm of highly dimensional biology where questions involve multiplicity at unprecedented scales: thousands of genetic polymorphisms, gene expression levels, protein measurements, genetic sequences, or any combination of these and their interactions. Such situations demand creative approaches to the processes of inference, estimation, prediction, classification, and study design. Although bench scientists intuitively grasp the need for flexibility in the inferential process, the elaboration of formal supporting statistical frameworks is just at the very start. Here, we will discuss some of the unique statistical challenges facing investigators studying high-dimensional biology, describe some approaches being developed by statistical scientists, and offer an epistemological framework for the validation of proffered statistical procedures. A key theme will be the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. The shift from family-wise error rate control to false discovery rate estimation and to assessment of ranking and other forms of stability will be portrayed as illustrative of approaches to this challenge.

The PowerAtlas: a power and sample size atlas for microarray experimental design and research

Background

Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies.

Results

To address this challenge, we have developed a Microrarray PowerAtlas [1]. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC).

Conclusion

This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.

Microarray data analysis: from disarray to consolidation and consensus

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

Impact of microarray technology in nutrition and food research

Microarrays have become standard tools for gene expression profiling as the mRNA levels of a large number of genes can be measured in a single assay. Many technical aspects concerning microarray production and laboratory usage have been addressed in great detail, but it remains still crucial to establish this technology in new research fields such as human nutrition and food-related areas. The correlation between diet and inter-individual variation in gene expression is an important and relatively unexplored issue in human nutrition. Therefore, nutritionists changed their research field dramatically from epidemiology and physiology towards the "omics" sciences. Nutrigenomics as a field of research is based on the complete knowledge of the human genome and refers to the entire spectrum of human genes that determine the interactions of nutrition with the organism. Nutrigenetics is based on the inter-individual, genetically determined differences in metabolism. Nutrigenomics and nutrigenetics carry the hope that individualized diet can improve human health and prevent nutrition-related diseases. In this article we give an overview of current DNA and protein microarray techniques (including fabrication, experimental procedure and data analysis), we describe their applications to nutrition and food research and point out the limitations, problems and pitfalls of microarray experiments.

Proteomic identification of differentially-expressed genes in human gastric carcinomas

Although genetic alterations in proto-oncogenes, tumor-suppressor genes, cell cycle regulators, and cell growth factors have been implicated in the process of human gastric carcinogenesis, the principle carcinogenic mechanisms are not fully understood. In this study, we used a proteomic approach to search for genes that may be involved in gastric carcinogenesis and that might serve as diagnostic markers. We identified nine proteins with increased expression and 13 proteins with decreased expression in gastric carcinomas. The two most notable groups included proteins involved in mitotic checkpoint (MAD1L1 and EB1) and mitochondrial functions (CLPP, COX5A, and ECH1). This suggested that there are links between dysfunctions in these processes and gastric carcinogenesis. We also observed the differential expression of HSP27 and CYR61 proteins in gastric carcinoma, whose expression is known to be altered in other types of tumors. Furthermore, the study identified proteins whose function in gastric carcinomas was previously unsuspected and that may serve as new molecular markers for gastric carcinomas. Importantly, immunohistochemical analyses confirmed that the levels of expression of MAD1L1, HSP27, and CYR61 were altered in gastric carcinoma tissues. Therefore, our study suggested not only that the proteins identified in this study can be useful diagnostic markers but also that a proteomics-based approach is useful for developing a more complete picture of the pathogenesis and function of gastric carcinomas.

Proteomic expression analysis of colorectal cancer by two-dimensional differential gel electrophoresis

The identification of specific protein markers for colorectal cancer would provide the basis for early diagnosis and detection, as well as clues for understanding the molecular mechanisms governing cancer progression. In this report, we describe the proteomic analysis of the samples of colorectal cancer corresponding to seven patients. We have used the highly sensitive two-dimensional differential gel electrophoresis (2-D DIGE) coupled with mass spectrometry (MS) for the identification of proteins differentially expressed in tumoral and neighboring normal mucosa. We have detected differences in abundance of 52 proteins with statistical variance of the tumor versus normal spot volume ratio within the 95th confidence level (Student's t-test; p < 0.05). Forty-one out of 52 analyzed proteins were unambiguously identified by matrix-assisted laser desorption/ionization-time of flight MS coupled with database interrogation as being differentially expressed in colorectal cancer. An ontology analysis of these proteins revealed that they were mainly involved in regulation of transcription (synovial sarcoma X5 protein, metastasis-associated protein 1), cellular reorganization and cytoskeleton (cytokeratins, vimentin, beta actin), cell communication and signal transduction (annexins IV and V, relaxin, APC), and protein synthesis and folding (heat shock protein 60, calreticulin, cathepsin D, RSP4) among others. Preliminary studies demonstrated that the differentially expressed proteins found by 2-D DIGE could be confirmed and validated by immunoblotting and immunohistochemistry analyses in those few cases where antibodies were available. We believe that the incorporation of more samples and new datasets will permit the definition of a collection of proteins with a potential interest as biomarkers for colorectal cancer.

Sources of variation in Affymetrix microarray experiments

Background

A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates.

Results

We performed a microarray experiment using a total of 24 Affymetrix GeneChip^® arrays. The study included 4^th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates.

Conclusion

The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential

Background

The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630–631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676–5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology.

Results

We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall.

Conclusion

Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.

Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets

Coexpression patterns of gene expression across many microarray data sets may reveal networks of genes involved in linked processes. To identify factors involved in cellulose biosynthesis, we used a regression method to analyze 408 publicly available Affymetrix Arabidopsis microarrays. Expression of genes previously implicated in cellulose synthesis, as well as several uncharacterized genes, was highly coregulated with expression of cellulose synthase (CESA) genes. Four candidate genes, which were coexpressed with CESA genes implicated in secondary cell wall synthesis, were investigated by mutant analysis. Two mutants exhibited irregular xylem phenotypes similar to those observed in mutants with defects in secondary cellulose synthesis and were designated irx8 and irx13. Thus, the general approach developed here is useful for identification of elements of multicomponent processes.

Assessing statistical precision, power, and robustness of alternative experimental designs for two color microarray platforms based on mixed effects models

Recommendations on experimental designs for two color microarray systems have been generally conflicting as they pertain to the general choice between reference and non-reference loop designs. This conflict may currently exist because many previously published assessments may not have effectively connected design layout with the level of biological relative to technical replication. We reassess various reference and non-reference designs for statistical efficiency in terms of standard errors of mean differences, power of test, and robustness using recently developed mixed model software tools. In minimally replicated cases (n = 2), it appears that the reference design outperforms the classical loop design whereby a sample from each animal is used for only one particular array hybridization. Alternatively, the reference design was consistently inferior to those connected loop designs in which a sample from each animal is used in two different hybridizations. Nevertheless, the gap in power between these two designs diminished as the biological to residual variance ratio increased. The statistical efficiency of a single large classical loop design for the comparison of many treatments was demonstrated to be highly sensitive to missing arrays relative to a common reference design (n = 2). However, the use of two loops within an interwoven loop design was shown to be substantially more robust to missing arrays and statistically more efficient relative to a common reference design. Furthermore, the use of more than one loop leads to less disparity in precision and power comparisons between any two treatments.

Expression profiling of the estrogen responsive genes in response to phytoestrogens using a customized DNA microarray

Here, we examined phytoestrogens, isoflavones (genistein, daidzein, glycitein, biochanin A and ipriflavone), flavones (chrysin, luteolin and apigenin), flavonols (kaempferol and quercetin), and a coumestan, a flavanone and a chalcone (coumestrol, naringenin and phloretin, respectively) by means of a DNA microarray assay. A total of 172 estrogen responsive genes were monitored with a customized DNA microarray and their expression profiles for the above phytoestrogens were compared with that for 17beta-estradiol (E2) using correlation coefficients, or R values, after a correlation analysis by linear regression. While R values indicate the similarity of the response by the genes, we also examined the genes by cluster analysis and by their specificity to phytoestrogens (specific to genistein, daidzein or glycitein) or gene functions. Several genes were selected from p53-related genes (CDKN1A, TP53I11 and CDC14), Akt2-related genes (PRKCD, BRCA1, TRIB3 and APPL), mitogen-activated protein kinase-related genes (RSK and SH3BP5), Ras superfamily genes (RAP1GA1, RHOC and ARHGDIA) and AP-1 family and related genes (RIP140, FOS, ATF3, JUN and FRA2). We further examined the extracts from two local crops of soy beans (Kuro-daizu or Mochi-daizu) by comparing the gene expression profiles with those of E2 or phytoestrogens as a first step in utilizing the expression profiles for various applications.

Nutritional genomics

Nutritional genomics has tremendous potential to change the future of dietary guidelines and personal recommendations. Nutrigenetics will provide the basis for personalized dietary recommendations based on the individual's genetic make up. This approach has been used for decades for certain monogenic diseases; however, the challenge is to implement a similar concept for common multifactorial disorders and to develop tools to detect genetic predisposition and to prevent common disorders decades before their manifestation. The preliminary results involving gene-diet interactions for cardiovascular diseases and cancer are promising, but mostly inconclusive. Success in this area will require the integration of different disciplines and investigators working on large population studies designed to adequately investigate gene-environment interactions. Despite the current difficulties, preliminary evidence strongly suggests that the concept should work and that we will be able to harness the information contained in our genomes to achieve successful aging using behavioral changes; nutrition will be the cornerstone of this endeavor.

New Developments in Cancer-Related Computational Statistics

A brief overview is presented of recently developed and currently emerging statistical and computational techniques that have been proved to be highly helpful in handling the avalanche of the new type of data generated by modern high-throughput technologies in experimental biology. The review, in no way comprehensive, focuses attention on Bayesian Networks, Hidden Markov Chain, and methods of chaotic dynamics for time-course genomic data; innovative methods in optimization and clustering; and multiple testing in the context of identification of differentially expressed genes.

Nutrigenomics and nutrigenetics

PURPOSE OF REVIEW

Nutritional genomics has tremendous potential to change the future of dietary guidelines and personal recommendations. Nutritional genomics covers nutrigenomics, which explores the effects of nutrients on the genome, proteome and metabolome, and nutrigenetics, the major goal of which is to elucidate the effect of genetic variation on the interaction between diet and disease. Nutrigenetics has been used for decades in certain rare monogenic diseases such as phenylketonuria, and it has the potential to provide a basis for personalized dietary recommendations based on the individual's genetic makeup in order to prevent common multifactorial disorders decades before their clinical manifestation.

RECENT FINDINGS

Preliminary results regarding gene-diet interactions in cardiovascular diseases are for the most part inconclusive because of the limitations of current experimental designs. Success in this area will require the integration of various disciplines, and will require investigators to work on large population studies that are designed to investigate gene-environment interactions.

SUMMARY

Based on the current knowledge, we anticipate that in the future we will be able to harness the information contained in our genomes to achieve successful aging using behavioral changes, with nutrition being the cornerstone of this endeavor.

New technologies for nutrition research

The Experimental Biology 2003 symposium entitled "New Technologies for Nutrition Research" was organized to highlight new and emerging technologies, including nanotechnology and proteomics, and to suggest ways for their integration into nutrition research. Speakers focused on topics that included accelerator mass spectrometry for ultra-low level radiolabel tracing, nanodevices for real-time optical intracellular sensing, mass spectrometric techniques for examining protein expression, as well as potential applications for nanotechnology in the food sciences. These technologies may be particularly useful in obtaining accurate spatial information and low-level detection of essential and nonessential bioactive food components (nutrients) and their metabolites, and in enhancing the understanding of the impact of nutrient/metabolite and biomolecular interactions. Highlights from this symposium are presented briefly herein.

Nutriproteomics: Identifying the Molecular Targets of Nutritive and Non-nutritive Components of the Diet

The study of whole patterns of changes in protein expression and their modifications, or proteomics, presents both technological advances as well as formidable challenges to biological researchers. Nutrition research and the food sciences in general will be strongly influenced by the new knowledge generated by the proteomics approach. This review examines the different aspects of proteomics technologies, while emphasizing the value of consideration of "traditional" aspects of protein separation. These include the choice of the cell, the subcellular fraction, and the isolation and purification of the relevant protein fraction (if known) by protein chromatographic procedures. Qualitative and quantitative analyses of proteins and their peptides formed by proteolytic hydrolysis have been substantially enhanced by the development of mass spectrometry technologies in combination with nanoscale fluidics analysis. These are described, as are the pros and cons of each method in current use.