Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research.

Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

Can survival prediction be improved by merging gene expression data sets?

BACKGROUND: High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS: Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS: Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.

Identification of CIITA regulated genetic module dedicated for antigen presentation.

The class II trans-activator CIITA is a transcriptional co-activator required for the expression of Major Histocompatibility Complex (MHC) genes. Although the latter function is well established, the global target-gene specificity of CIITA had not been defined. We therefore generated a comprehensive list of its target genes by performing genome-wide scans employing four different approaches designed to identify promoters that are occupied by CIITA in two key antigen presenting cells, B cells and dendritic cells. Surprisingly, in addition to MHC genes, only nine new targets were identified and validated by extensive functional and expression analysis. Seven of these genes are known or likely to function in processes contributing to MHC-mediated antigen presentation. The remaining two are of unknown function. CIITA is thus uniquely dedicated for genes implicated in antigen presentation. The finding that CIITA regulates such a highly focused gene expression module sets it apart from all other transcription factors, for which large-scale binding-site mapping has indicated that they exert pleiotropic functions and regulate large numbers of genes.

Stealth proteins: in silico identification of a novel protein family rendering bacterial pathogens invisible to host immune defense.

There are a variety of bacterial defense strategies to survive in a hostile environment. Generation of extracellular polysaccharides has proved to be a simple but effective strategy against the host's innate immune system. A comparative genomics approach led us to identify a new protein family termed Stealth, most likely involved in the synthesis of extracellular polysaccharides. This protein family is characterized by a series of domains conserved across phylogeny from bacteria to eukaryotes. In bacteria, Stealth (previously characterized as SacB, XcbA, or WefC) is encoded by subsets of strains mainly colonizing multicellular organisms, with evidence for a protective effect against the host innate immune defense. More specifically, integrating all the available information about Stealth proteins in bacteria, we propose that Stealth is a D-hexose-1-phosphoryl transferase involved in the synthesis of polysaccharides. In the animal kingdom, Stealth is strongly conserved across evolution from social amoebas to simple and complex multicellular organisms, such as Dictyostelium discoideum, hydra, and human. Based on the occurrence of Stealth in most Eukaryotes and a subset of Prokaryotes together with its potential role in extracellular polysaccharide synthesis, we propose that metazoan Stealth functions to regulate the innate immune system. Moreover, there is good reason to speculate that the acquisition and spread of Stealth could be responsible for future epidemic outbreaks of infectious diseases caused by a large variety of eubacterial pathogens. Our in silico identification of a homologous protein in the human host will help to elucidate the causes of Stealth-dependent virulence. At a more basic level, the characterization of the molecular and cellular function of Stealth proteins may shed light on fundamental mechanisms of innate immune defense against microbial invasion.

JACOP: a simple and robust method for the automated classification of protein sequences with modular architecture.

BACKGROUND: Whole-genome sequencing projects are rapidly producing an enormous number of new sequences. Consequently almost every family of proteins now contains hundreds of members. It has thus become necessary to develop tools, which classify protein sequences automatically and also quickly and reliably. The difficulty of this task is intimately linked to the mechanism by which protein sequences diverge, i.e. by simultaneous residue substitutions, insertions and/or deletions and whole domain reorganisations (duplications/swapping/fusion). RESULTS: Here we present a novel approach, which is based on random sampling of sub-sequences (probes) out of a set of input sequences. The probes are compared to the input sequences, after a normalisation step; the results are used to partition the input sequences into homogeneous groups of proteins. In addition, this method provides information on diagnostic parts of the proteins. The performance of this method is challenged by two data sets. The first one contains the sequences of prokaryotic lyases that could be arranged as a multiple sequence alignment. The second one contains all proteins from Swiss-Prot Release 36 with at least one Src homology 2 (SH2) domain--a classical example for proteins with modular architecture. CONCLUSION: The outcome of our method is robust, highly reproducible as shown using bootstrap and resampling validation procedures. The results are essentially coherent with the biology. This method depends solely on well-established publicly available software and algorithms.

Telomere length homeostasis is achieved via a switch between telomerase- extendible and -nonextendible states.

Telomerase counteracts telomere erosion that stems from incomplete chromosome end replication and nucleolytic processing. A precise understanding of telomere length homeostasis has been hampered by the lack of assays that delineate the nonuniform telomere extension events of single chromosome molecules. Here, we measure telomere elongation at nucleotide resolution in Saccharomyces cerevisiae. The number of nucleotides added to a telomere in a single cell cycle varies between a few to more than 100 nucleotides and is independent of telomere length. Telomerase does not act on every telomere in each cell cycle, however. Instead, it exhibits an increasing preference for telomeres as their lengths decline. Deletion of the telomeric proteins Rif1 or Rif2 gives rise to longer telomeres by increasing the frequency of elongation events. Thus, by taking a molecular snapshot of a single round of telomere replication, we demonstrate that telomere length homeostasis is achieved via a switch between telomerase-extendible and -nonextendible states.

Digital expression profiles of human endogenous retroviral families in normal and cancerous tissues.

Human endogenous retroviruses (HERVs) are remnants of ancient retroviral infections that became fixed in the germ line DNA millions of years ago. The fact that humoral and cellular immune responses against HERV-encoded proteins have been identified in cancer patients suggests that these antigens might be used in cancer immunotherapy or diagnosis. We analyzed the digital expression patterns of the HERV-K (HML-2), -W, -H and -E families in normal and cancerous tissues. Thirty-one proviral members of the HERV-K family and one representative each for the other HERV families were used as probes to search human EST data. Matching of HERV proviruses to ESTs was HERV family-specific and the expression profiles of the HERV families distinct. The HERV-K family was expressed in normal tissues such as muscle, skin and brain, as well as in germ cell tumors and other cancerous tissues. HERV-H was the only family expressed in cancers of the intestine, bone marrow, bladder and cervix, and was more highly expressed than the other families in cancers of the stomach, colon and prostate. In contrast, HERV-W was predominantly expressed in normal placenta. Expression patterns were confirmed by MPSS (massively parallel signature sequencing) data where available. For the HERV-K family, we mapped most ESTs to their corresponding proviruses and assessed the coding capacities of the matched proviruses. This study shows that HERV families are more widely expressed than originally thought and that some members of the HERV-K and -H families could encode targets for cancer immunotherapy.

trome, trEST and trGEN: databases of predicted protein sequences.

We previously introduced two new protein databases (trEST and trGEN) of hypothetical protein sequences predicted from EST and HTG sequences, respectively. Here, we present the updates made on these two databases plus a new database (trome), which uses alignments of EST data to HTG or full genomes to generate virtual transcripts and coding sequences. This new database is of higher quality and since it contains the information in a much denser format it is of much smaller size. These new databases are in a Swiss-Prot-like format and are updated on a weekly basis (trEST and trGEN) or every 3 months (trome). They can be downloaded by anonymous ftp from ftp://ftp.isrec.isb-sib.ch/pub/databases.