Oligonucleotide microarray expression profiling ... - Wiley Online Library

IUBMB

Life, 58(1): 15 – 24, January 2006

Critical Review Oligonucleotide microarray expression profiling: Human skeletal muscle phenotype and aerobic exercise training James A. Timmons and Carl Johan Sundberg Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden

Summary Regular aerobic exercise reduces risk of cardiovascular disease far more effectively than any pharmaceutical agent. The precise mechanisms contributing to these health benefits are unknown. Currently, much of our knowledge regarding the molecular regulators of skeletal muscle phenotype remodeling in response to muscle activity is derived from rodent models. Over the past five years large scale gene analysis has emerged as a promising research strategy for studying complex processes in human tissue. This review will principally discuss the application of large scale gene expression profiling to study the molecular responses to longitudinal aerobic exercise training studies in humans. The focus is largely on the Affymetrix technology platform, as this can be most easily compared, in a quantitative manner, across laboratories. Indeed, there are compelling reasons to adopt a common standard to obtain maximum synergy across complex, expensive and invasive human studies. Direct comparisons between array data sets can be made, and these should be considered novel ‘experiments’, often providing great insight into disease mechanisms. Weaknesses in existing human studies are identified and future objectives are discussed. IUBMB Life, 58: 15–24, 2006 Keywords

Affymetrix; transcriptome; endurance performance; gene; microarray; skeletal muscle.

INTRODUCTION Regular aerobic exercise is one of the most effective methods for reducing the risk of cardiovascular related disease (1 – 5). The precise mechanisms contributing to these health benefits are unknown and the type and frequency of exercise that best serves the purpose of reducing the risk of cardiovascular-metabolic disease are unknown. A large intersubject variation exists, when determining the physiological benefits obtained from supervised regular aerobic exercise training (6 – 12). This includes variable improvements in Received 31 October 2005; accepted 5 December 2005 Address correspondence to: James A. Timmons, Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden. E-mail: [email protected] ISSN 1521-6543 print/ISSN 1521-6551 online Ó 2006 IUBMB DOI: 10.1080/15216540500507390

aerobic capacity and insulin sensitivity, such that a proportion of the population simply demonstrate no ‘adaptation’ or ‘benefit’ (11, 12). Such observations may be important for predicting future cardiovascular health, as an inherent lack of ‘trainability’ associates with increased cardiovascular risk factors in rodents (5) and is present in otherwise healthy young subjects (11). Determination of the factors which influence human skeletal muscle phenotype will provide greater insight into the link between muscular activity and cardiovascular health. Such factors are likely to include interactions between skeletal muscle and blood pressure regulation, insulin action and include identification of novel secreted proteins, produced within the muscle, that demonstrate systemic metabolic actions (defined as ‘myokines’(13)). Within the human muscle physiology field, population genetics approaches (2, 14, 15) have generated a great deal of data yet identified relatively few reliable molecular determinants of muscle adaptation in humans (15). An alternative approach is to directly study gene expression responses of individuals which demonstrate variable adaptation to exercise training (11, 16). It is here that over the past five years large-scale gene analysis has emerged as a promising strategy. While data is emerging from this approach (10 – 12) it is clear that a variety of experimental models, ethnicities and genders will have to be characterized before we determine with reasonable certainty which gene expression programs contribute to the variability. Interestingly, from the largest human data set collected so far (12) there is limited evidence that mechanisms identified in rodent models (17, 18) represent main transducers of skeletal muscle phenotype remodeling in response to muscle activity in humans (12, 19 – 22), emphasizing a need to utilize human ‘models’. Focused studies have provided evidence that PGC-1a and MEF2 may play a role in muscle phenotype regulation in humans (23, 24) and appropriate large scale profiling should enable determination of what proportion of acute ‘exercise’ genes are under direct PGC-1a and MEF2 control in humans. Microarray technology is critical for the delivery of such information.

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TIMMONS AND SUNDBERG

AN ARRAY OF CONSIDERATIONS Technology Platforms Microarrays represent one of the most significant developments in our ‘tool kit’ for the exploration of human muscle biology. The outdated concept of defining muscle phenotype based on fiber type or a handful of genes (18, 25) is clearly at an end. In this review focus is given to human aerobic exercise training studies, with an emphasis on the Affymetrix platform. The motivation for this is multiple. Firstly, as data is comparable across laboratories (26), reproducibility established (27), and core facilities accessible, it is likely to be the most common method utilized in future studies. Moreover there are compelling reasons to adopt a common standard, as from a bioinformatics perspective this greatly facilitates the cross-comparison of multiple human studies even though there can be reasonable agreement across platforms under highly controlled conditions (26). Advanced analysis strategies for microarray studies require data sets from different laboratories to be modeled, making best use of finite medical research funding. Direct comparison across existing cDNA array

studies is hindered by normalization, RNA amplification and other significant technical differences (28). However, even when the same array platform is utilized, principal component analysis (29) demonstrates that a large percentage of the data set variance can be attributed to technical/interlab issues (an illustrative example is given in Fig. 1, where gene-by-gene normalization was utilized prior to plotting the principal variance components (O. Larsson, personal communication). Additional array platforms exist, and while some – like the Illumina gene expression system – appear to provide data consistent with the Affymetrix platform (30) none have been used to study human muscle or published on sufficiently to merit further discussion at this stage. Custom or ‘home-made’ cDNA arrays (10, 22, 31, 32) have been used to study muscle phenotype responses to endurance training, yielding data that can be compared utilizing a ‘gene ontology’ approach but in general the lack of availability and variation in RNA handling and labeling procedures preclude large scale quantitative comparison across laboratories. One should also be cautious about utilizing small scale arrays, where several hundred biologically-related genes are studied, as it is the invariant

Figure 1. The first 10 variance components derived from three data sets of differentiating mesenchymal cells detected using the Affymetrix U74A-v2 array platform. All PCAs were performed in R (R-project.org) using ‘covariances’. Component 1 represents the majority of the data set variation, typically reflecting inter-laboratory variance, while the small components contained biological relevant gene expression programs when analyzed using gene ontology enrichment analysis in EASE (unpublished observation).

OLIGONUCLEOTIDE MICROARRAY EXPRESSION PROFILING

transcripts, detected using the large scale chips, which are utilized to aid normalization strategies. Given limited economic resources, it is critical that intercomparable array platforms are adopted to facilitate the bioinformatic cross-comparison of array data (26). Such comparisons can themselves be considered novel ‘experiments’ and often provide greater insight into disease mechanisms than individual studies (12). A key concept to grasp for array analysis is that focus should move away from the detection of individual genes of interest to functional groups as this a more reliable endpoint for the technology (28) and can circumvent some of the issues related to various technology platforms. It should be kept in mind that despite the hype, the ‘human genome’ actively transcribed is not fully characterized, gene transcripts not fully catalogued and the anti-sense transcriptome barely understood. All these factors contribute to the challenge of adequate analysis of microarray data, beyond any technical consideration relating to off the shelf technology. Ultimately, this review aims to sufficiently inform and to emphasize that there is scope for substantial novelty in the microarray field when applied to human muscle physiology. Indeed, one can argue that direct evidence of activation of a cluster of co-regulated genes is as powerful a method as demonstration that the regulating protein ‘factor’ is activated. Large scale expression profiling should be considered complementary and not subservient too, low throughput protein expression methods.

Muscle Physiology and Gene Expression In a field often influenced by dogma, ‘muscle gene expression profiling’ has rapidly accumulated a number of its own. It has been claimed that the majority of genes that respond to muscle contraction, do so within a few hours, diminish with time and thus this early ‘window’ is the critical period to study. Further, it has been presumed (33) that longterm adaptation reflects the initial signaling responses present after a single training session. These suppositions have led to a focus on individual gene expression profiling following a single period of exercise. On the contrary, at least 500 genes are differentially expressed 24 hours following the final training period of 6 weeks of endurance training (12), and under conditions where physiological adaptation has been proven. Clearly, an active transcriptional response is longer lasting that has been assumed from profiling a limited number of genes. The concept that acute kinase signaling events are the primary mediator of chronic adaptation (33) can be challenged if one considers such acute changes most likely reflect acute responses to energy homeostasis and muscle fuel replenishment. Whether such factors mediate long term adaptation is unproven and detailed bioinformatics analysis of large scale transcript changes with endurance training (12) versus acute endurance exercise (22) remains to be carried out. In the future, it will be critical that acute studies which utilize microarray technology also produce direct evidence for

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physiological adaptation. This means in practice that gene expression profiling following acute muscle contraction should be studied in subjects that are then characterized during a subsequent training period, such that the physiological adaptability of the subjects can be proven (11).

Tissue Issues The absence of adequate physiological characterization of the subject from whom the muscle tissue sample was obtained (21, 34, 35), has undermined the value of early studies. It is critical that tissue samples originate from subjects where quantitative physiological and biochemical variables have been determined and that such data is utilized to analyze the expression data generated. The reason for this is intuitive; when studying a genetically out-bred population, both physiological regulation and gene expression is likely to be highly variable. In order to better understand the determinants of such highly variable responses, one needs to be able to categorize the heterogeneity that allows for novel hypothesis to be developed. Arithmetically ‘pooling’ the output from a small number of human subjects that demonstrate a highly variable response has limited value. Recently, a sub-group of genes that associate with a superior ability to respond to aerobic exercise training (11) has been described. In Fig. 2, extracellular matrix genes were assessed using real time qPCR in two groups of subjects that undertook an identical supervised exercise program (11, 12). Critically, the high responder group demonstrated a 4 fold greater improvement in aerobic capacity compared with the low responder group. This observation merely helps to illustrate the dangers of pooling biological material (36, 37) prior to gene expression profiling. Given the possibilities that can now be applied to array analysis, discussed below, there can rarely be a valid argument for pooling biopsy material prior to laboratory analysis. Other tissue sampling issues exist, which are not unique to gene expression studies. These include the issue of multiple biopsies (38, 39) where the biopsy itself may alter muscle gene expression. This may be of consideration with the biopsies samples are taken within 2 cm of each other, over a period of hours or a few days but is less likely to be an issue when the biopsies are taken several weeks apart. Currently, there is little evidence that when best practice is followed that repeated sampling represents a major problem (38). Input-Output Issues The ‘house keeping gene’ (HKG) approach is utilize to correct for the abundance of a ‘novel gene’. Correction of the amount of RNA input into the reverse transcriptase (RT) reaction and the efficiency of that reaction for cDNA synthesis requires a normalization procedure. Critically, ‘HKG’ expression (40) can change in response to exercise training (12). The dilution of cDNA, many hundreds of fold, coupled with a exponential detection system (real time qPCR) ensures that minor pipetting error may be a common source of analysis

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variation when the HKG is assessed in a different cDNA dilution from the target gene. Accurate determination of starting RNA material, utilization of a limited number of serial dilutions coupled with 18s RNA determination is adequate under conditions where total RNA is not expected to change. The utilization of a non UV based detection system, such as the Agilent 2100 bioanalyser, to quantify total RNA offers advantages in the sense that it is not sensitive to phenol contamination (although costs are greater). Additional flourophore based methods for standardization offer advantages in terms of precision but add to costs and also have their own limitations (38). The accuracy of the dilution of the cDNA can be validated using a moderately abundant target gene and comparison of the non-normalized ct value. In addition, 18s RNA correction can be linear across the range of 200 ng to 1ug total RNA input into a fixed RT assay protocol (personal observation, several genes). However 18s RNA may not directly reflect RT efficiency for every gene under investigation, and the general uncertainties regarding the variables impacting on the efficiency of the RT reaction (41) ensure that careful comparison of inter-laboratory methods is always essential, regardless of HKG choice. International standardization is extremely desirable but awaits serious implementation (e.g., the use of a single set of RT reagents for all muscle experiments). Prior knowledge of a valid HKG for a novel experiment does not exist and correction of gene expression by a variety of HKG clearly has potential to mislead. This is especially concerning when authors ‘randomly’ switch between HKG across otherwise similar studies. Although considered critical to ‘validate’ a microarray experiment, real time qPCR has both advantages and weaknesses when compared with the Affymetrix oligonucleotide array, such that these methods should only be considered complementary. Real time qPCR utility comes to the forefront when the gene of interest is of low abundance or complex splicing of the gene occurs and detailed analysis is required.

General issues with array data handling The principal aim of a microarray study is to generate a large and valid data set. This obvious statement allows for the introduction of an important aspect of data handling that is often carried out prior to statistical testing (21). Many studies apply arbitrary data filtering decisions (42) prior to statistical analysis (21, 43). For example, if 10 subjects are analyzed before and after muscle strength training then a gene response may only be included if 7 from 10 subjects demonstrate a change above a given threshold (e.g., 2 fold change from baseline). While the motivation for such an approach is understandable at an emotional level (‘increased analysis stringency’) this approach is likely to be fundamentally unsound for a variety of reasons. In an attempt to remove what has been referred to as ‘SNP noise’ (19, 21) such arbitrary procedures introduce unpredictable consequences to the subsequent statistical analysis (44). It is also a strategy

which ignores the observation that understanding inter-subject variability is central to generating a deeper understanding of the molecular control of human muscle phenotype. Such variation between human subjects can be of physiological importance (11, 12, 45) and therefore arbitrary data filtering confuses the issue of laboratory and physiological variability. In the example presented (Fig. 2), if 30% of your subjects demonstrate little improvement in aerobic capacity of aerobic training, why would you utilize the gene expression profile from these subjects to score the exclusion of a gene which may help explain why a sub-set of subjects demonstrated enhanced aerobic capacity? While such approaches have probably been applied to compensate lack of adjustment for multiple testing and small subject numbers, it does not fulfill this task. On the other hand, how does one avoid being overly rigorous with the initial data analysis without a valid rational? Luckily there are alternative strategies for reducing the chance of false positives that include statistical analysis of gene clusters or biological themes rather than individual gene changes.

INTERPRETATION OF DATA: IMPACT OF STUDY DESIGN There are numerous concerns with the interpretation of microarray experiments that have utilized tissue biopsy material and these have been discussed extensively elsewhere (46). One advantage of working with human muscle tissue, over and above other tissue sources, such as tumor biopsies or atherosclerotic plaques (46) is that relatively speaking muscle is more homogenous and with the exception of extreme inflammatory conditions (47), gene expression changes do not reflect gross shifts in cell type composition (which can result in an array study simply confirming established histological knowledge). If a large scale shift in cell type is not a major concern what are the major challenges for using gene expression profiling to study human muscle physiology? Ideally, repeated measurements (biopsies taken at various time points) obtained from a number of subjects, where the subjects demonstrate a range of physiological responses. This ideal situation is still prohibitively expensive for most laboratories, yet importantly it is not required to deal with Affymetrix chip-to-chip variation, as such variance is acceptably low. On the other hand, the issue of muscle sample heterogeneity, using RNA isolated from multiple biopsy pieces from the same subject, has not been systematically considered using microarrays, but real time qPCr data suggests this may not transpire to be of major concern (38). Solid experimental design is, as always, a critical consideration. A description of ‘good design’ for human studies is far beyond the scope of this short article. I refer the reader to recent work applied to acute and cross-sectional studies of human muscle which exemplify numerous important considerations which should be considered over and above biopsy sampling, RNA preparation and array analysis (22, 48). One


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Figure 2. Change in extracellular-matrix-related gene expression following endurance training. Values are -fold changes in human skeletal muscle gene expression (mean + SE) following six weeks of aerobic training. Gene expression was determined using real-time quantitative PCR. Following six weeks training (n ¼ 24), the eight highest and eight lowest responders to exercise training were identified using the sum of (a) the percent improvement in maximal aerobic capacity, (b) the percent reduction in submaximal heart rate during 15 min fixed-workload, submaximal cycling and (c) the percent improvement in work done during a 15 min maximal cycling test. This ranking was carried out before any genomic analysis was carried out. The training responses were evaluated by two-way ANOVA an Bonferoni post-hoc tests. *P 5 0.05, **P 5 0.01 and ***P 5 0.001. a2-Macroglobulin (AM2); Angiopoietin 1 (ANG 1); Angiopoietin 2 (ANG2); Collagen type IIIa1 (COL3A1); Collagen type XVa1 (COL15A1); Neuropilin (NP-1); Thrombospondin-4 (THBS4); Transforming growth factor b2 (TGFB2); Transforming growth factor b receptor II (TGFBR2); Tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1); Tyrosine kinase with immunoglobulin-like and EGF-like domains 2 (TIE2). Taken from Timmons et al. (11). issue is definitely worth specific mention. It has been recently demonstrated that for acute studies of human skeletal muscle gene expression appropriate time control biopsies is desirable (49). In addition, a recent study has demonstrated both a time

and exercise dependent component for global muscle gene expression (50), interactions between blood pressure regulation, time and posture ensure interpretation of muscle gene expression can be challenging if biopsies are not

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obtained, before and after an intervention, under comparable conditions. Limited RNA availability and small subject numbers has also constrained investigators analysis strategies (19, 21, 51). This has led to the use of additional amplification protocols that make cross-array comparison challenging. As mentioned, cross comparison of inter-laboratory studies is of great value but is hampered by lack of standardization between labs. The alternative to additional amplification (the Affymetrix standard protocol involves a single amplification round) is to pool RNA samples from a 2 – 3 subjects so that one can obtain sufficient RNA (2 – 5 mg total RNA). If such an approach were to be taken, then this should be done only when sub-groups of relatively ‘homogeneous’ subjects can be identified. The physiological parameters that defines how such RNA samples are pooled thereafter limits the usefulness of the data as ‘re-pooling’ (clustering) is only possible if an ‘electronic record’ for each sample exists. Computational clustering techniques used to ‘pool subjects’ based on biochemical or physiological criteria is far more desirable (12). Numerous groups working with microarray technology and human muscle tissue have produced excellent publications and critiques of chip reproducibility, data handling strategies including statistical analysis and normalization (27, 36, 52). Normalization is somewhat analogous to the use of a HKG gene for real time qPCR. Normalization of the chip hybridization signal, so that chip/studies can be compared with each other, is a critical step in data handling. Each algorithm utilized makes different assumptions about the data set. A typical assumption is that the majority of genes are not altered during the study. When utilizing a large array, with 25 – 35 thousand probe sets, this approach is powerful when only a few hundred or thousand genes are modulated. If one compares this with earlier discussions concerning the use of a single ‘HKG’ for normalizing RT qPCR data, the strength of the microarray approach becomes rather obvious. In fact, when investigators have favored biologically ‘focused’ cDNA arrays, they have done so at the expense of losing this useful background ‘invariance’. Bigger is almost certainly better from this perspective. On a practical level, we observed that the original Affymetrix normalization software (the MAS5.0 algorithm) was a poor option when normalizing an endurance exercise training array data set (12) when contrasted with the robust multi-array average (RMA) method from Terry Speeds laboratory (53, 54). In other studies the reverse has been true and it now seems reasonable to utilize 2 or 3 normalization methods prior to subsequent differential gene expression analysis. Gene changes can either be presented separately or combined into a ‘master’ list. As always, clear documentation of the analysis strategies used is of great importance. Other primary data filtering relevant for the Affymetrix platform include the utilization of the miss-match probe set to ‘assess’ the selectivity of the perfect-match probe set. There is limited support for this (54) as the miss-match probe set may hybridize with the target mRNA both efficiently and

selectively. In the latest release of Affymetrix gene analysis software, it is even possible to combine both the perfect match and the miss-match probe set data to generate your gene expression signal. In general we have concluded that it is best to avoid, wherever possible, relatively arbitrary exclusion and filtering methods prior to carrying out formal statistical analysis. Once you obtain your normalized expression data, you will then be required to define which genes have significantly changed following muscle contraction or exercise training. A fixed threshold for expression change has been commonly utilized. In doing so we are probably excluding genes that are genuinely regulated at the transcript level. In principal, there is limited reason to use this approach as modest changes in transcript abundance may be biologically sufficient depending on how gene protein turnover is regulated. If a number of ‘probe sets’, for the same ‘gene’, detect modest change then the observation becomes even more convincing. However, lack of concurrent of multiple probes sets for the same gene does not undermine the validity of data as there are multiple technical and biological reasons why some probe sets may be more valid than others for a given gene (55). The calculation of a False Discovery Rate (FDR) is an attempt to control for false positives (i.e., Type II error) and is important when carrying out multiple statistical testing (44, 56, 57). In underpowered studies, with low quality array hybridization data, this issue has largely been ignored in favor of the introduction of arbitrary thresholds and t-tests. No statistical ‘trick’ circumvents the need for high quality data from a reasonable number of human subjects. With greater numbers and time points much more powerful analysis methods can be applied. For example, ‘gene shaving’ ‘principal component analysis’ (PCA) and more conventional (but less robust) clustering methods can be applied (29, 58 – 60). Gene ontology (GO) categorization (61) is particularly useful as it moves away for relying on individual gene expression changes in favor of the discovery of modulated ‘biological pathways’. The categorization of genes into biological ‘themes’ is not a precise science and redundancy exists where genes often appear in more than one category. These issues aside, it is possible to take your statistically significant gene expression changes (with or without a fold change threshold) and apply a second level of statistical analysis, again calculating a FDR for the overrepresentation of ‘functional processes’ or gene ontology groups. This approach is simple and statistically robust (12, 61) and it is also possible to apply such analysis to PCA components. Superficially, more sophisticated strategies have emerged that create ‘maps’ or ‘networks’ of interconnecting genes (62). Such ‘interactomes’ rely on commercial ontology classification strategies, where gene-gene associations are obtained from primary publications. However, much like Gene Set Enrichment Analysis (34), such network-based analysis strategies require the implementation of data permutation strategies to assess statistical significance more


robustly. Discussion of false positive and false negative data generation rates can be found in more detail in the article by Choe et al. (55).

HUMAN SKELETAL MUSCLE AND AFFYMETRIX MICROARRAY STUDIES The first human (sense) transcriptome-wide analysis of the response to endurance exercise training utilized the Affymetrix platform (12). A total of 80 U95 (A-E) Affymetrix chips, were utilized with 16 biopsies from 8 subjects (from 24) that demonstrated the greatest evidence for cardiovascular adaptation following 6 weeks of endurance training. A constant training load rather than a continuously adjusted training load was utilized, to more closely resemble recreational physical activity. Although it is the most robust study to date, the study has a number of limitations, namely lack of female subjects, single time point biopsy assessments and single mode of exercise training (cycling). That being said, the results were interesting. Twenty percent of subjects demonstrated little evidence for physiological response to training and this allowed the relationship between functional adaptation and gene expression to be studied using qPCR follow-up. Firstly, and as expected, there was a modest increase in the expression of mitochondrial or metabolic mRNA’s (*65); few were increased more than 30% (12). It would appear that most mitochondrial related OXPHOS transcripts are not modulated to a great extent in human skeletal muscle by exercise training (and this is also consistent with their suppression during inactivity in human muscle tissue (63)). Gene ontology analysis demonstrated significant modulation of extracellular matrix genes as a major characteristic of trained human skeletal muscle. There was a lack of concurrence between this data and models of muscle damage demonstrating that the ‘endurance exercise transcriptome’ was not a damage phenotype (42, 64). Cross array study analysis demonstrated a striking similarity between the endurance training ‘transcriptome’ 24 h post exercise and that observed in young children suffering from Duchenne muscular dystrophy (12). Given the molecular evidence for enhanced a7b1 integrin signaling when dystrophin is mutated, and the potential for integrin signaling to act as a mechano-transducer of muscle activity, this is the first clear evidence (based on hundreds of data points) that integrin signaling may be an important mediator for exercise induced changes in human muscle gene expression. Future analysis will allow for more intelligent interpretation of diabetes transcriptome data (34, 35) where ‘disease’ and physical inactivity gene expression programs can be separately identified (63). In a related analysis, genes related to extracellular matrix remodeling and growth factors were differentially modulated between subjects that did, and did not, demonstrate a measurable training response (11). This demonstrates that the subject’s physiological response must be taken into account when profiling gene expression responses in human

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skeletal muscle and highlights possible limitations when no change in gene expression was detected in prior studies (65, 66). Intriguingly this physiologically relevant finding (11) was not welcomed by any mainstream physiological journal. An earlier study published in the Journal of Physiology utilized the Affymetrix U95A chip to study gene expression changes in three middle aged men, following 9 months of endurance type training (21). There were 20 genes nominally modulated in a common fashion between this and the larger scale study (see supplementary section of (12)). The same group (47) produced a similar analysis using the Affymetrix U133 chip using some of the same biological material. On re-analysis of their U133 raw dataset, using RMA normalization and SAM analysis, no genes were differentially expressed (44, 56). In their published analysis Hittel et al. (19) utilized three male subjects and three female subjects to study the impact of 9 months of aerobic training in middle-aged subjects. The array data was published in the Journal of Applied Physiology, while the raw data was available at an earlier time via their ‘interactive’ array website http://pepr.cnmcresearch.org. When MAS 5.0 and dChip normalization was carried out approximately double the number of significant probe sets were identified for female subjects than males (utilizing paired t-tests). It was then calculated that 136 genes were modulated 41.5 fold in the female subjects, while only 96 were altered by a similar magnitude in male subjects. The authors concluded that ‘women exhibit a more pronounced transcriptional response to training than men’ (19). What we have noticed, is that inter-subject variability is an extremely important factor, greatly influencing the outcome of array analysis (12). If we consider the sexual dimorphism question, then firstly it would be critical to know whether the training status or aerobic fitness of the three males and three females differed substantially from the median aerobic capacity in the STRRIDE study. Secondly, using correlation analysis of the gene expression changes, were the responses of the three female subjects more alike than the similarity between the male subjects? If we consider the greater number of significant genes in the female subjects (19), then this will have little to do with sexual dimorphism and more to do with variation between subjects. Other findings included the well established changes in mitochondrial gene and protein expression. A recent cDNA array study by Tarnopolsky and colleagues (22) confirmed modulation of mitochondrial related genes, and future GO categorization of their full data set would be interesting to compare with the available Affymetrix data (12). Interestingly they demonstrated that *120 genes were modulated acutely post exercise (3 h), while by 48 h only *35 genes were modulated in the four subjects studied. Assuming that all four subjects were ‘responders’ (11) and inter-subject variation was low, then this would suggest that the 500 genes modulated 24 h post the last training period of a 6-week training program (12), does indeed reflect a relatively stable change in the skeletal muscle transcriptome. Teran-Garcia et al. (10) recently

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demonstrated that in young adults, endurance training improvements in insulin mediated glucose uptake were highly variable and related to differential gene expression (using a custom oligonucleotide array platform). When sophisticated bioinformatics analysis on this data set and additional diabetes and non-diabetes data sets is made available much greater insight into the relationship between physical activity, muscle phenotype and insulin action will be possible.

CONCLUSIONS If we examine the handful of Affymetrix-based gene expression studies of exercise and exercise training then it is clear that editorial practice and peer review is far from standardized. Publication of premature analysis has created a challenge for the researchers wishing to claim novelty during the publication process. This applies as much to the aforementioned articles as it does to articles appearing in loftier journals. It is critical, that when using advanced technology that attention to detail regarding the physiology and biochemistry is still maintained. It is essential to use statistical methods which attempt to estimate a ‘false discovery rate’ when carrying out array analysis. It also seems unacceptable to discuss gene expression changes in the absence of evidence for physiological change. Clarity in the presentation of the choice of data normalization and post-hoc analysis is trivial to achieve and essential for the reader to judge the merits of the article. Finally, a move toward a common array platform and standardization of RT and real time qPCR methods is needed to maximize our gains in knowledge of human muscle physiology.

ACKNOWLEDGEMENTS We would like to acknowledge the efforts and ingenuity of our colleagues, Ola Larsson and Kristian Wennmalm. We would like to acknowledge the support of the Swedish Diabetes Association, Swedish Sports Council and the Karolinska Institute. We are collaborators on the Karolinska InstituteAffymetrix strategic alliance.

REFERENCES 1. Kraus, W. E., Houmard, J. A., Duscha, B. D., Knetzger, K. J., Wharton, M. B., McCartney, J. S., Bales, C. W., Henes, S., Samsa, G. P., Otvos, J. D., Kulkarni, K. R., and Slentz, C. A. (2002) Effects of the amount and intensity of exercise on plasma lipoproteins. N. Engl. J. Med. 347, 1483 – 1492. 2. Frederiksen, H., and Christensen, K. (2003) The influence of genetic factors on physical functioning and exercise in second half of life. Scand. J. Med. Sci. Sports 13, 9 – 18. 3. Manson, J. E., Greenland, P., LaCroix, A. Z., Stefanick, M. L., Mouton, C. P., Oberman, A., Perri, M. G., Sheps, D. S., Pettinger, M. B., and Siscovick, D. S. (2002) Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N. Engl. J. Med. 347, 716 – 725.

4. Wei, M., Gibbons, L. W., Mitchell, T. L., Kampert, J. B., Lee, C. D., and Blair, S. N. (1999) The association between cardiorespiratory fitness and impaired fasting glucose and type 2 diabetes mellitus in men. Ann. Intern. Med. 130, 89 – 96. 5. Wisloff, U., Najjar, S. M., Ellingsen, O., Haram, P. M., Swoap, S., AlShare, Q., Fernstrom, M., Rezaei, K., Lee, S. J., Koch, L. G., and Britton, S. L. (2005) Cardiovascular risk factors emerge after artificial selection for low aerobic capacity. Science 307, 418 – 420. 6. Lortie, G., Simoneau, J. A., Hamel, P., Boulay, M. R., Landry, F., and Bouchard, C. (1984) Responses of maximal aerobic power and capacity to aerobic training. Int. J. Sports Med. 5, 232 – 236. 7. Prud’homme, D., Bouchard, C., Leblanc, C., Landry, F., and Fontaine, E. (1984) Sensitivity of maximal aerobic power to training is genotype-dependent. Med. Sci. Sports Exer. 16, 489 – 493. 8. Simoneau, J. A., Lortie, G., Boulay, M. R., Marcotte, M., Thibault, M. C., and Bouchard, C. (1986) Inheritance of human skeletal muscle and anaerobic capacity adaptation to high-intensity intermittent training. Int. J. Sports Med. 7, 167 – 171. 9. Bouchard, C., and Rankinen, T. (2001) Individual differences in response to regular physical activity. Med. Sci. Sports Exer. 33, S446 – 451; discussion S452 – 443. 10. Teran-Garcia, M., Rankinen, T., Koza, R. A., Rao, D. C., and Bouchard, C. (2005) Endurance training-induced changes in insulin sensitivity and gene expression. Am. J. Physiol. Endocrinol. Metab. 288, E1168 – 1178. 11. Timmons, J. A., Jansson, E., Fischer, H., Gustafsson, T., Greenhaff, P. L., Ridden, J., Rachman, J., and Sundberg, C. J. (2005) Modulation of extracellular matrix genes reflects the magnitude of physiological adaptation to aerobic exercise training in humans. BMC Biol. 3, 19. 12. Timmons, J. A., Larsson, O., Jansson, E., Fischer, H., Gustafsson, T., Greenhaff, P. L., Ridden, J., Rachman, J., Peyrard-Janvid, M., Wahlestedt, C., and Sundberg, C.J. (2005) Human muscle gene expression responses to endurance training provide a novel perspective on Duchenne muscular dystrophy. FASEB J. 19, 750 – 760. 13. Febbraio, M. A., and Pedersen, B. K. (2005) Contraction-induced myokine production and release: is skeletal muscle an endocrine organ? Exerc. Sport Sci. Rev. 33, 114 – 119. 14. Rico-Sanz, J., Rankinen, T., Rice, T., Leon, A. S., Skinner, J. S., Wilmore, J. H., Rao, D. C., and Bouchard, C. (2004) Quantitative trait loci for maximal exercise capacity phenotypes and their responses to training in the HERITAGE Family Study. Physiol. Genomics 16, 256 – 260. 15. Rupert, J. L. (2003) The search for genotypes that underlie human performance phenotypes. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 136, 191 – 203. 16. Hubal, M. J., Gordish-Dressman, H., Thompson, P. D., Price, T. B., Hoffman, E. P., Angelopoulos, T. J., Gordon, P. M., Moyna, N. M., Pescatello, L. S., Visich, P. S., Zoeller, R. F., Seip, R. L., and Clarkson, P. M. (2005) Variability in muscle size and strength gain after unilateral resistance training. Med. Sci. Sports Exerc. 37, 964 – 972. 17. Wu, H., Naya, F. J., McKinsey, T. A., Mercer, B., Shelton, J. M., Chin, E. R., Simard, A. R., Michel, R. N., Bassel-Duby, R., Olson, E. N., and Williams, R. S. (2000) MEF2 responds to multiple calciumregulated signals in the control of skeletal muscle fiber type. EMBO J. 19, 1963 – 1973. 18. Chin, E. R., Olson, E. N., Richardson, J. A., Yang, Q., Humphries, C., Shelton, J. M., Wu, H., Zhu, W., Bassel-Duby, R., and Williams, R. S. (1998) A calcineurin-dependent transcriptional pathway controls skeletal muscle fiber type. Genes Dev. 12, 2499 – 2509. 19. Hittel, D. S., Kraus, W. E., Tanner, C. J., Houmard, J. A., and Hoffman, E. P. (2005) Exercise training increases electron and substrate shuttling proteins in muscle of overweight men and women with the metabolic syndrome. J. Appl. Physiol. 98, 168 – 179.


20. Chen, J., Zhao, P., Massaro, D., Clerch, L. B., Almon, R. R., DuBois, D. C., Jusko, W. J., and Hoffman, E. P. (2004) The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface. Nucleic Acids Res. 32, D578 – 581. 21. Hittel, D. S., Kraus, W. E., and Hoffman, E. P. (2003) Skeletal muscle dictates the fibrinolytic state after exercise training in overweight men with characteristics of metabolic syndrome. J. Physiol. 548, 401 – 410. 22. Mahoney, D. J., Parise, G., Melov, S., Safdar, A., and Tarnopolsky, M. A. (2005) Analysis of global mRNA expression in human skeletal muscle during recovery from endurance exercise. FASEB J. 19, 1498 – 1500. 23. Pilegaard, H., Saltin, B., and Neufer, P. D. (2003) Exercise induces transient transcriptional activation of the PGC-1alpha gene in human skeletal muscle. J. Physiol. 546, 851 – 858. 24. McGee, S. L., and Hargreaves, M. (2004) Exercise and myocyte enhancer factor 2 regulation in human skeletal muscle. Diabetes 53, 1208 – 1214. 25. Olson, E. N., and Williams, R. S. (2000) Calcineurin signaling and muscle remodeling. Cell 101, 689 – 692. 26. Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., Gabrielson, E., Garcia, J. G., Geoghegan, J., Germino, G., Griffin, C., Hilmer, S. C., Hoffman, E., Jedlicka, A. E., Kawasaki, E., Martinez-Murillo, F., Morsberger, L., Lee, H., Petersen, D., Quackenbush, J., Scott, A., Wilson, M., Yang, Y., Ye, S. Q., and Yu, W. (2005) Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 345 – 350. 27. Nimgaonkar, A., Sanoudou, D., Butte, A. J., Haslett, J. N., Kunkel, L. M., Beggs, A. H., and Kohane, I. S. (2003) Reproducibility of gene expression across generations of Affymetrix microarrays. BMC Bioinformatics 4, 27. 28. Bammler, T., Beyer, R. P., Bhattacharya, S., Boorman, G. A., Boyles, A., Bradford, B. U., Bumgarner, R. E., Bushel, P. R., Chaturvedi, K., Choi, D., Cunningham, M. L., Deng, S., Dressman, H. K., Fannin, R. D., Farin, F. M., Freedman, J. H., Fry, R. C., Harper, A., Humble, M. C., Hurban, P., Kavanagh, T. J., Kaufmann, W. K., Kerr, K. F., Jing, L., Lapidus, J. A., Lasarev, M. R., Li, J., Li, Y. J., Lobenhofer, E. K., Lu, X., Malek, R. L., Milton, S., Nagalla, S. R., O’Malley J. P., Palmer, V. S., Pattee, P., Paules, R. S., Perou, C. M., Phillips, K., Qin, L. X., Qiu, Y., Quigley, S. D., Rodland, M., Rusyn, I., Samson, L. D., Schwartz, D. A., Shi, Y., Shin, J. L., Sieber, S. O., Slifer, S., Speer, M. C., Spencer, P. S., Sproles, D. I., Swenberg, J. A., Suk, W. A., Sullivan, R. C., Tian, R., Tennant, R. W., Todd, S. A., Tucker, C. J., Van Houten, B., Weis, B. K., Xuan, S., and Zarbl, H. (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat. Methods 2, 351 – 356. 29. Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863 – 14868. 30. Barnes, M., Freudenberg, J., Thompson, S., Aronow, B., and Pavlidis, P. (2005) Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms. Nucleic Acids Res. 33, 5914 – 5923. 31. Wittwer, M., Billeter, R., Hoppeler, H., and Fluck, M. (2004) Regulatory gene expression in skeletal muscle of highly endurancetrained humans. Acta Physiol. Scand. 180, 217 – 227. 32. Wu, H., Gallardo, T., Olson, E. N., Williams, R. S., and Shohet, R. V. (2003) Transcriptional analysis of mouse skeletal myofiber diversity and adaptation to endurance exercise. J. Muscle Res. Cell Motil. 24, 587 – 592. 33. Widegren, U., Ryder, J. W., and Zierath, J. R. (2001) Mitogenactivated protein kinase signal transduction in skeletal muscle: effects of exercise and muscle contraction. Acta Physiol. Scand. 172, 227 – 238.

23

34. Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D., and Groop, L. C. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267 – 273. 35. Patti, M. E., Butte, A. J., Crunkhorn, S., Cusi, K., Berria, R., Kashyap, S., Miyazaki, Y., Kohane, I., Costello, M., Saccone, R., Landaker, E. J., Goldfine, A. B., Mun, E., DeFronzo, R., Finlayson, J., Kahn, C. R., and Mandarino, L. J. (2003) Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: potential role of PGC1 and NRF1. Proc. Natl. Acad. Sci. USA 100, 8466 – 8471. 36. Bakay, M., Chen, Y. W., Borup, R., Zhao, P., Nagaraju, K., and Hoffman, E. P. (2002) Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics 3, 4. 37. Peng, X., Wood, C. L., Blalock, E. M., Chen, K. C., Landfield, P. W., and Stromberg, A.J. (2003) Statistical implications of pooling RNA samples for microarray experiments. BMC Bioinformatics 4, 26. 38. Lundby, C., Nordsborg, N., Kusuhara, K., Kristensen, K. M., Neufer, P. D., and Pilegaard, H. (2005) Gene expression in human skeletal muscle: alternative normalization method and effect of repeated biopsies. Eur. J. Appl. Physiol. 95, 351 – 360. 39. Constantin-Teodosiu, D., Casey, A., Short, A. H., Hultman, E., and Greenhaff, P. L. (1996) The effect of repeated muscle biopsy sampling on ATP and glycogen resynthesis following exercise in man. Eur. J. Appl. Physiol. Occup. Physiol. 73, 186 – 190. 40. Mahoney, D. J., Carey, K., Fu, M. H., Snow, R., Cameron-Smith, D., Parise, G., and Tarnopolsky, M. A. (2004) Real-time RT-PCR analysis of housekeeping genes in human skeletal muscle following acute exercise. Physiol. Genomics 18, 226 – 231. 41. Stahlberg, A., Hakansson, J., Xian, X., Semb, H., and Kubista, M. (2004) Properties of the reverse transcription reaction in mRNA quantification. Clin. Chem. 50, 509 – 515. 42. Chen, Y. W., Hubal, M. J., Hoffman, E. P., Thompson, P. D., and Clarkson, P. M. (2003) Molecular responses of human muscle to eccentric exercise. J. Appl. Physiol. 95, 2485 – 2494. 43. Lu, T., Pan, Y., Kao, S. Y., Li, C., Kohane, I., Chan, J., and Yankner, B. A. (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429, 883 – 891. 44. Larsson, O., Wahlestedt, C., and Timmons, J. A. (2005) Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC Bioinformatics 6, 129. 45. Tai, K. K., and Truong, D. D. (2002) Activation of adenosine triphosphate-sensitive potassium channels confers protection against rotenone-induced cell death: therapeutic implications for Parkinson’s disease. J. Neurosci. 69, 559 – 566. 46. Hoffman, E. P. (2004) Expression profiling – best practices for data generation and interpretation in clinical trials. Nat. Rev. Genet. 5, 229 – 237. 47. Bakay, M. Zhao, P., Chen, J. and Hoffman, E. P. (2002) A webaccessible complete transcriptome of normal human and DMD muscle. Neuromuscul. Disord. 12, (Suppl 1), S125 – 141. 48. Welle, S., Brooks, A. I., Delehanty, J. M., Needler, N., and Thornton, C. A. (2003) Gene expression profile of aging in human muscle. Physiol. Genomics 14, 149 – 159. 49. Vissing, K., Andersen, J. L., and Schjerling, P. (2005) Are exerciseinduced genes induced by exercise? FASEB J. 19, 94 – 96. 50. Zambon, A. C., McDearmon, E. L., Salomonis, N., Vranizan, K. M., Johansen, K. L., Adey, D., Takahashi, J. S., Schambelan, M., and Conklin, B. R. (2003) Time- and exercise-dependent gene regulation in human skeletal muscle. Genome Biol. 4, R61.

24


51. Kraus, W. E., Torgan, C. E., Duscha, B. D., Norris, J., Brown, S. A., Cobb, F. R., Bales, C. W., Annex, B. H., Samsa, G. P., Houmard, J. A., and Slentz, C. A. (2001) Studies of a targeted risk reduction intervention through defined exercise (STRRIDE). Med. Sci. Sports Exerc. 33, 1774 – 1784. 52. Welle, S., Brooks, A. I., and Thornton, C. A. (2002) Computational method for reducing variance with Affymetrix microarrays. BMC Bioinformatics 3, 23. 53. Bolstad, B. M., Irizarry, R. A., Astrand, M., and Speed, T. P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185 – 193. 54. Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., and Speed, T. P. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249 – 264. 55. Choe, S. E., Boutros, M., Michelson, A. M., Church, G. M., and Halfon, M. S. (2005) Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol. 6, R16. 56. Tusher, V. G., Tibshirani, R., and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116 – 5121. 57. Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P. (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83 – 92. 58. Raychaudhuri, S. Stuart, J. M. and Altman, R. B. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455 – 466. 59. Hibbs, M. A., Dirksen, N. C., Li, K., and Troyanskaya, O. G. (2005) Visualization methods for statistical analysis of microarray clusters. BMC Bioinformatics 6, 115.

60. Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., and Brown, P. (2000) ‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 1, RESEARCH0003. 61. Hosack, D. A., Dennis, G., Jr., Sherman, B. T., Lane, H. C., and Lempicki, R. A. (2003) Identifying biological themes within lists of genes with EASE. Genome Biol. 4, R70. 62. Calvano, S. E., Xiao, W., Richards, D. R., Felciano, R. M., Baker, H. V., Cho, R. J., Chen, R. O., Brownstein, B. H., Cobb, J. P., Tschoeke, S. K., Miller-Graziano, C., Moldawer, L. L., Mindrinos, M. N., Davis, R. W., Tompkins, R. G., and Lowry, S. F. (2005) A networkbased analysis of systemic inflammation in humans. Nature 437, 1032 – 1037. 63. Timmons, J. A., Norrbom, J., Scheele, C., Thonberg, H., Wahlestedt, C., and Tesch, P. A. (2006) Expression profiling following local muscle inactivity in humans provides new perspective on diabetes related genes. Genomics 87, 165 – 172. 64. Yan, Z., Choi, S., Liu, X., Zhang, M., Schageman, J. J., Lee, S. Y., Hart, R., Lin, L., Thurmond, F. A., and Williams, R. S. (2003) Highly coordinated gene regulation in mouse skeletal muscle regeneration. J. Biol. Chem. 278, 8826 – 8836. 65. Norrbom, J., Sundberg, C. J., Ameln, H., Kraus, W. E., Jansson, E., and Gustafsson, T. (2004) PGC-1alpha mRNA expression is influenced by metabolic perturbation in exercising human skeletal muscle. J. Appl. Physiol. 96, 189 – 194. 66. Bengtsson, J., Gustafsson, T., Widegren, U., Jansson, E., and Sundberg, C. J. (2001) Mitochondrial transcription factor A and respiratory complex IV increase in response to exercise training in humans. Pflugers Arch. 443, 61 – 66.