The quest for the mechanisms of life

A GC-MS metabolic profiling study of plasma samples from mice on low- and high-fat diets

Metabolic profiling of biofluids, based on the quantitative analysis of the concentration profile of their free low molecular mass metabolites, has been playing increasing role employed as a means to gain understanding of the progression of metabolic disorders, including obesity. Chromatographic methods coupled with mass spectrometry have been established as a strategy for metabolic profiling. Among these, GC-MS, targeting mainly the primary metabolism intermediates, offers high sensitivity, good peak resolution and extensive databases. However, the derivatization step required for many involatile metabolites necessitates specific data validation, normalization and analysis protocols to ensure accurate and reproducible performance. In this study, the GC-MS metabolic profiles of plasma samples from mice maintained on 12- or 15-month long low (10 kcal%) or high (60 kcal%) fat diets were obtained. The profiles of the trimethylsilyl(TMS)-methoxime(MeOx) derivatives of the free polar metabolites were acquired through GC-(ion trap)MS, using [U-(13)C]-glucose as the internal standard. After the application of a recently developed data correction and normalization/filtering protocol for GC-MS metabolomic datasets, the profiles of 48 out of the 77 detected metabolites were used in multivariate statistical analysis. Data mining suggested a decrease in the activity of the energy metabolism with age. In addition, the metabolic profiles indicated the presence of subpopulations with different physiology within the high- and low-fat diet mice, which correlated well with the difference in body weight among the animals and current knowledge about hyperglycemic conditions.

Systems biology of industrial microorganisms

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

Time-series integrated "omic" analyses to elucidate short-term stress-induced responses in plant liquid cultures

The research that aims at furthering our understanding of plant primary metabolism has intensified during the last decade. The presented study validated a systems biology methodological framework for the analysis of stress-induced molecular interaction networks in the context of plant primary metabolism, as these are expressed during the first hours of the stress treatment. The framework involves the application of time-series integrated full-genome transcriptomic and polar metabolomic analyses on plant liquid cultures. The latter were selected as the model system for this type of analysis, because they provide a well-controlled growth environment, ensuring that the observed plant response is due only to the applied perturbation. An enhanced gas chromatography-mass spectrometry (GC-MS) metabolomic data correction strategy and a new algorithm for the significance analysis of time-series "omic" data are used to extract information about the plant's transcriptional and metabolic response to the applied stress from the acquired datasets; in this article, it is the first time that these are applied for the analysis of a large biological dataset from a complex eukaryotic system. The case-study involved Arabidopsis thaliana liquid cultures subjected for 30 h to elevated (1%) CO2 stress. The advantages and validity of the methodological framework are discussed in the context of the known A. thaliana or plant, in general, physiology under the particular stress. Of note, the ability of the methodology to capture dynamic aspects of the observed molecular response allowed for 9 and 24 h of treatment to be indicated as corresponding to shifts in both the transcriptional and metabolic activity; analysis of the pathways through which these activity changes are manifested provides insight to regulatory processes.

A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of postgenomic clinical trials on cancer

This paper reports on original results of the Advancing Clinico-Genomic Trials on Cancer integrated project focusing on the design and development of a European biomedical grid infrastructure in support of multicentric, postgenomic clinical trials (CTs) on cancer. Postgenomic CTs use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypothesis in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. This paper provides a presentation of the needs of users involved in postgenomic CTs, and presents such needs in the form of scenarios, which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. Also, the main technological challenge, i.e. the design and development of semantically rich grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented, and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.

Significance analysis of time-series transcriptomic data: a methodology that enables the identification and further exploration of the differentially expressed genes at each time-point

Time-series transcriptional profiling experiments are becoming increasingly popular, in light of the abundance of information regarding a biological system's regulation that they are expected to reveal. However, identification of differentially expressed genes as a function of time and comparison between physiological states based on the genes' variability in significance level over time remain intriguing tasks, due to certain limitations in the currently available algorithms. Based on the principles of significance analysis of microarrays (SAM) method, we developed an algorithm that allows for the identification of the differentially expressed genes at each time-point of a time sequence, using a common reference distribution and significance threshold for all time-points. These results are further explored in a systematic way to extract information about (a) individual gene and gene class variability in significance level with time, (b) gene and time-point correlation based on (a), and (c) gene class comparison based on (a). All algorithms have been programmed in C language in the form of four executable files for both Windows and Macintosh platforms under the overall name MiTimeS. MiTimeS was validated in the context of real transcriptomic data. It enables the extraction of biologically relevant information from the dynamic transcriptomic profiles currently unnoticed from the available algorithms. The applicability of MiTimeS is not limited to transcriptomic data, but it could be accordingly used for the analysis of dynamic data from other cellular fingerprints.

DNA microarrays in herbal drug research

Natural products are gaining increased applications in drug discovery and development. Being chemically diverse they are able to modulate several targets simultaneously in a complex system. Analysis of gene expression becomes necessary for better understanding of molecular mechanisms. Conventional strategies for expression profiling are optimized for single gene analysis. DNA microarrays serve as suitable high throughput tool for simultaneous analysis of multiple genes. Major practical applicability of DNA microarrays remains in DNA mutation and polymorphism analysis. This review highlights applications of DNA microarrays in pharmacodynamics, pharmacogenomics, toxicogenomics and quality control of herbal drugs and extracts.

Mechanistic data and cancer risk assessment: the need for quantitative molecular endpoints

The cancer risk assessment process as currently proposed by the U.S. Environmental Protection Agency allows for the use of mechanistic data to inform the low-dose tumor response in humans and in laboratory animals. The aim is to reduce the reliance on defaults that introduce a relatively high level of uncertainty to the risk estimates. The types of data required for this purpose are those that help identify key events in tumor formation following exposure to environmental chemicals. Informative biomarkers of tumor responses could then be developed for describing the shape of a dose-response curve at low doses (i.e., a qualitative assessment) and for predicting tumor frequency at these low doses (i.e., a quantitative assessment). A number of recently developed molecular approaches could aid in the development of qualitatively and quantitatively informative biomarkers. An overview of these with examples of their use is presented. These methods include quantitative gene expression array techniques, quantitative proteomic assays, and the assessment of DNA alterations at the single gene level and at the genome level of detection. It is most likely that a combination of approaches at different levels of cellular organization (i.e., DNA, RNA, and protein) will be the most productive for biomarker development. The rapid progress that is being made will make this tool kit even more applicable for the cancer risk assessment process.

Using functional genomics to improve productivity in the manufacture of industrial biochemicals

Recent developments in the field of functional genomics have been used to increase productivity in the manufacture of industrial biochemicals. Technologies like transcriptomics and proteomics have profited from the increasing number of genome sequencing projects. Meanwhile functional genomics has evolved from several isolated technologies, such as DNA chip technology and proteomics, to combined approaches that can help us to understand why organisms produce a certain product. The combination of expression studies and kinetic studies, such as carbon flux determination or metabolite measurements, has significantly improved productivity in production processes.