From 'omes to biology

Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment

Confounding factors

In transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64).

Analysis Approach

Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes.

ANOVA

ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors.

Conclusion

Hence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

Pediatric systems medicine: evaluating needs and opportunities using congenital heart block as a case study

Medicine and pediatrics are changing and health care is moving from being reactive to becoming preventive. Despite rapid developments of new technologies for molecular profiling and systems analysis of diseases, significant hurdles remain. Here, we use the clinical setting of congenital heart block (CHB) to uncover and illustrate key informatics challenges impeding the development of a systems medicine approach emphasizing the prevention and prediction of disease. We find that there is a paucity of useful bioinformatics tools enabling the integrative analysis of different databases of molecular information and clinical sources in a disease context such as CHB, contrasting with the current emphasis on developing bioinformatics tools for the analysis of individual data types. Moreover, informatics solutions for managing data, such as the Integrating Biology and the Bedside (i2b2) or Stanford Translational Research Integrated Database Environment, require serious software engineering support for the maintenance and import of data beyond the capabilities of clinicians working with CHB. Hence, there is an urgent unmet need for user-friendly tools facilitating the integrative analysis and management of omics data and clinical information. Pediatrics represents an untapped potential to execute such a systems medicine program in close collaboration with clinicians and families who are keen to do what is needed for their children to prevent and predict diseases and nurture wellness.

Bioinformatics applied to gene transcription regulation

Understanding regulation of gene transcription is central to molecular biology as well as being of great interest in medicine. The molecular syntax of the concerted transcriptional activation/repression of gene networks in mammal cells, which shape the physiological response to the molecular signals, is often unknown or not completely understood. Combining genome-wide experiments with in silico approaches opens the way to a more systematic comprehension of the molecular mechanisms of transcription regulation. Diverse bioinformatics tools have been developed to help unravel these mechanisms, by handling and processing data at different stages: from data collection and storage to the identification of molecular targets and from the detection of DNA motif signatures in the regulatory sequences of functionally related genes to the identification of relevant regulatory networks. Moreover, the large amount of genome-wide scale data recently produced has attracted professionals from diverse backgrounds to this cutting-edge realm of molecular biology. This mini-review is intended as an orientation for multidisciplinary professionals, introducing a streamlined workflow in gene transcription regulation with emphasis on sequence analysis. It provides an outlook on tools and methods, selected from a host of bioinformatics resources available today. It has been designed for the benefit of students, investigators, and professionals who seek a coherent yet quick introduction to in silico approaches to analyzing regulation of gene transcription in the post-genomic era.

Linking toxicant physiological mode of action with induced gene expression changes in Caenorhabditis elegans

Background

Physiologically based modelling using DEBtox (dynamic energy budget in toxicology) and transcriptional profiling were used in Caenorhabditis elegans to identify how physiological modes of action, as indicated by effects on system level resource allocation were associated with changes in gene expression following exposure to three toxic chemicals: cadmium, fluoranthene (FA) and atrazine (AZ).

Results

For Cd, the physiological mode of action as indicated by DEBtox model fitting was an effect on energy assimilation from food, suggesting that the transcriptional response to exposure should be dominated by changes in the expression of transcripts associated with energy metabolism and the mitochondria. While evidence for effect on genes associated with energy production were seen, an ontological analysis also indicated an effect of Cd exposure on DNA integrity and transcriptional activity. DEBtox modelling showed an effect of FA on costs for growth and reproduction (i.e. for production of new and differentiated biomass). The microarray analysis supported this effect, showing an effect of FA on protein integrity and turnover that would be expected to have consequences for rates of somatic growth. For AZ, the physiological mode of action predicted by DEBtox was increased cost for maintenance. The transcriptional analysis demonstrated that this increase resulted from effects on DNA integrity as indicated by changes in the expression of genes chromosomal repair.

Conclusions

Our results have established that outputs from process based models and transcriptomics analyses can help to link mechanisms of action of toxic chemicals with resulting demographic effects. Such complimentary analyses can assist in the categorisation of chemicals for risk assessment purposes.

Automatic inference of multicellular regulatory networks using informative priors

To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

High throughput ‘omics’ approaches to assess the effects of phytochemicals in human health studies

Human health is affected by many factors. Diet and inherited genes play an important role. Food constituents, including secondary metabolites of fruits and vegetables, may interact directly with DNA via methylation and changes in expression profiles (mRNA, proteins) which results in metabolite content changes. Many studies have shown that food constituents may affect human health and the exact knowledge of genotypes and food constituent interactions with both genes and proteins may delay or prevent the onset of diseases. Many high throughput methods have been employed to get some insight into the whole process and several examples of successful research, namely in the field of genomics and transcriptomics, exist. Studies on epigenetics and RNome significance have been launched. Proteomics and metabolomics need to encompass large numbers of experiments and linked data. Due to the nature of the proteins, as well as due to the properties of various metabolites, experimental approaches require the use of comprehensive high throughput methods and a sufficiency of analysed tissue or body fluids. In this contribution, we describe the basic tools currently used in nutrigenomics studies and indicate the general requirements for future technology methodological routings.

Protein abundance ratios for global studies of prokaryotes

The use of multidimensional capillary HPLC combined with MS/MS has allowed high qualitative and quantitative proteome coverage of prokaryotic organisms. The determination of protein abundance change between two or more conditions has matured to the point that false discovery rates can be very low and for smaller proteomes coverage is sufficiently high to explicitly consider false negative error. Selected aspects of using these methods for global protein abundance assessments are reviewed. These include instrumental issues that influence the reliability of abundance ratios; a comparison of sources of nonlinearity, errors, and data compression in proteomics and spotted cDNA arrays; strengths and weaknesses of spectral counting versus stable isotope metabolic labeling; and a survey of microbiological applications of global abundance analysis at the protein level. Proteomic results for two organisms that have been studied extensively using these methods are reviewed in greater detail. Spectral counting and metabolic labeling data are compared and the utility of proteomics for global gene regulation studies are discussed for the methanogenic Archaeon Methanococcus maripaludis. The oral pathogen Porphyromonas gingivalis is discussed as an example of an organism where a large percentage of the proteome differs in relative abundance between the intracellular and extracellular phenotype.

Integration of structural and functional genomics

This paper introduces a special issue of Animal Genetics, which is devoted to the recent symposium held at Iowa State University entitled 'Integration of Structural and Functional Genomics'. We describe issues and needs that confront the animal genomics community, and describe how this symposium was structured to address these issues by improving communication and collaboration across species and disciplines. The session topics and oral presentations are briefly described for all invited speakers.