Comprehensive analysis of the mouse metabolome based on the transcriptome

Changes in metabolite profiles caused by genetically determined obesity in mice

The Berlin Fat Mouse Inbred (BFMI) line harbors a major recessive gene defect on chromosome 3 (jobes1) leading to juvenile obesity and metabolic syndrome. The present study aimed at the identification of metabolites that might be linked to recessively acting genes in the obesity locus. Firstly, serum metabolites were analyzed between obese BFMI and lean B6 and BFMI × B6 F₁ mice to identify metabolites that are different. In a second step, a metabolite-protein network analysis was performed linking metabolites typical for BFMI mice with genes of the jobes1 region. The levels of 22 diacyl-phosphatidylcholines (PC aa), two lyso-PC and three carnitines were found to be significantly lower in obese mice compared with lean mice, while serine, glycine, arginine and hydroxysphingomyelin were higher for the same comparison. The network analysis identified PC aa C42:1 as functionally linked with the genes Ccna2 and Trpc3 via the enzymes choline kinase alpha and phospholipase A2 group 1B (PLA2G1B), respectively. Gene expression analysis revealed elevated Ccna2 expression in adipose tissue of BFMI mice. Furthermore, unique mutations were found in the Ccna2 promoter of BFMI mice which are located in binding sites for transcription factors or micro RNAs and could cause differential Ccna2 mRNA levels between BFMI and B6 mice. Increased expression of Ccna2 was consistent with higher mitotic activity of adipose tissue in BFMI mice. Therefore, we suggest a higher demand for PC necessary for adipose tissue growth and remodeling. This study highlights the relationship between metabolite profiles and the underlying genetics of obesity in the BFMI line.

Challenges and solutions in proteomics

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.

Genome-scale modeling and in silico analysis of mouse cell metabolic network

Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting.

Association analysis for large-scale gene set data

High-throughput experiments in biology often produce sets of genes of potential interests. Some of those gene sets might be of considerable size. Therefore, computer-assisted analysis is necessary for the biological interpretation of the gene sets, and for creating working hypotheses, which can be tested experimentally. One obvious way to analyze gene set data is to associate the genes with a particular biological feature, for example, a given pathway. Statistical analysis could be used to evaluate if a gene set is truly associated with a feature. Over the past few years many tools that perform such analysis have been created. In this chapter, using WebGestalt as an example, it will be explained in detail how to associate gene sets with functional annotations, pathways, publication records, and protein domains.

Non-coding RNAs: lost in translation?

In the last ten years, several RNAs with no protein-coding potential have been accumulating in RNA databases and are in need of further molecular characterization. At the same time, examples of non-coding RNAs (ncRNAs) such as microRNAs, small RNAs, small interfering RNAs (siRNAs) and medium/large RNAs with various functions have been described in the literature. Recent evidence points to a widespread role of these molecules in eukaryotic cells, suggesting that the majority of the new ncRNA examples might have specific functions. The aim of this review is to describe several new functional ncRNAs that have been recently identified and characterized, providing some clues that these molecules might not be produced by chance or as by-products of transcription as has been speculated.

Metabolomics in human nutrition: opportunities and challenges

Metabolomics has been widely adopted in pharmacology and toxicology but is relatively new in human nutrition. The ultimate goal, to understand the effects of exogenous compounds on human metabolic regulation, is similar in all 3 fields. However, the application of metabolomics to nutritional research will be met with unique challenges. Little is known of the extent to which changes in the nutrient content of the human diet elicit changes in metabolic profiles. Moreover, the metabolomic signal from nutrients absorbed from the diet must compete with the myriad of nonnutrient signals that are absorbed, metabolized, and secreted in both urine and saliva. The large-bowel microflora also produces significant metabolic signals that can contribute to and alter the metabolome of biofluids in human nutrition. Notwithstanding these possible confounding effects, every reason exists to be optimistic about the potential of metabolomics for the assessment of various biofluids in nutrition research. This potential lies both in metabolic profiling through the use of pattern-recognition statistics on assigned and unassigned metabolite signals and in the collection of comprehensive data sets of identified metabolites; both objectives have the potential to distinguish between different dietary treatments, which would not have been targeted with conventional techniques. The latter objective sets out a well-recognized challenge to modern biology: the development of libraries of small molecules to aid in metabolite identification. The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.

The study of metabolic pathways in tumors based on the transcriptome

DNA microarray technology revolutionized gene-expression analysis in molecular biology to observe patterns of gene expression in genomic scale. We review the biological aspects of genome-wide gene-expression activity in tumors specially focusing on the analysis of enzyme coding genes. First, the methods for analyzing gene-expression data for the study of metabolome in silico are discussed showing SV40T antigen expressing liver tumor data as an example. Next, an application for tumor metabolome analysis utilizing a reference set of gene-expression profiles is shown.

Ontogeny of transcription profiles during mouse early craniofacial development

Using the CD-1 mouse conceptus, we investigated gene expression changes found in vivo from gestational day 8 (GD) through GD9 at 6 h intervals, and then at 24 h intervals through GD11. Data sets were analyzed for patterns in transcriptional expression over a time course as well as to compare the GD9 in vivo mouse conceptus with conceptuses cultured for 24 h from GD8 in whole embryo culture (WEC). Our results show that during development from GD8 to GD11, a series of metabolic pathways related to carbohydrate metabolism and energy production, as well as the signal transduction pathways of the MAPK cascade and Wnt signaling were changing. Previous results have shown that WEC successfully sustains morphological development comparable to that in vivo for at least 24 h. We report here that, in the absence of morphological malformations following 24 h WEC, 329 genes were differentially expressed between in vivo and in vitro developing conceptuses.

FunnyBase: a systems level functional annotation of Fundulus ESTs for the analysis of gene expression

BACKGROUND

While studies of non-model organisms are critical for many research areas, such as evolution, development, and environmental biology, they present particular challenges for both experimental and computational genomic level research. Resources such as mass-produced microarrays and the computational tools linking these data to functional annotation at the system and pathway level are rarely available for non-model species. This type of "systems-level" analysis is critical to the understanding of patterns of gene expression that underlie biological processes.

RESULTS

We describe a bioinformatics pipeline known as FunnyBase that has been used to store, annotate, and analyze 40,363 expressed sequence tags (ESTs) from the heart and liver of the fish, Fundulus heteroclitus. Primary annotations based on sequence similarity are linked to networks of systematic annotation in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and can be queried and computationally utilized in downstream analyses. Steps are taken to ensure that the annotation is self-consistent and that the structure of GO is used to identify higher level functions that may not be annotated directly. An integrated framework for cDNA library production, sequencing, quality control, expression data generation, and systems-level analysis is presented and utilized. In a case study, a set of genes, that had statistically significant regression between gene expression levels and environmental temperature along the Atlantic Coast, shows a statistically significant (P < 0.001) enrichment in genes associated with amine metabolism.

CONCLUSION

The methods described have application for functional genomics studies, particularly among non-model organisms. The web interface for FunnyBase can be accessed at http://genomics.rsmas.miami.edu/funnybase/super_craw4/. Data and source code are available by request at jpaschall@bioinfobase.umkc.edu.

Dynamic transcriptome of mice

Life science in the 21st century is developing rapidly through the structural analysis of biomolecules, the completion of the human genome sequence and the analysis of transcriptomes. The mouse transcriptome has been comprehensively analyzed using a gene discovery approach to collect full-length cDNA (FL-cDNA) clones. The framework of the transcriptome was then mapped out by an international Functional ANnoTation Of Mouse cDNA (FANTOM) effort, and a significant new population of noncoding transcripts was discovered. The geographical analogy of a second "RNA continent," separate from the "continent" of expressed proteins, aids the visualization of this concept. An unexpected number of variations was discovered in the mouse transcriptome. The animal transcriptome has evolved to produce several transcripts and proteins from a single "transcriptional unit". Transcriptome analysis has given rise to the FL-cDNA database and to the 60 770 FANTOM FL-cDNA clone set, and the DNABook was developed as an easier way to distribute these clones. In conjunction with genome sequence databases, transcriptome databases and clone banks will be platforms for developing advanced databases of gene function (e.g. the Genome Function Database). This will enable life science to make rapid progress towards understanding life as a system of molecules.

Mouse-centric comparative transcriptomics of protein coding and non-coding RNAs

The largest transcriptome reported so far comprises 60,770 mouse full-length cDNA clones, and is an effective reference data set for comparative transcriptomics. The number of mouse cDNAs identified greatly exceeds the number of genes predicted from the sequenced human and mouse genomes. This is largely because of extensive alternative splicing and the presence of many non-coding RNAs (ncRNAs), which are difficult to predict from genomic sequences. Notably, ncRNAs are a major component of the transcriptomes of higher organisms, and many sense-antisense pairs have been identified. The ncRNAs function in a range of regulatory mechanisms for gene expression and other biological processes. They might also have contributed to the increased functional diversification of genomes during evolution. In this review, we discuss aspects of the transcriptome of various organisms in relation to the mouse data, in order to shed light on the regulatory mechanisms and physiological significance of these abundant RNAs.

Metabolic database systems for the analysis of genome-wide function

Genome sequencing projects provide an inventory of molecular components for a wide variety of organisms. Metabolic databases integrate these functional descriptions of individual modules into a higher-level characterization of cellular metabolism. This article reviews efforts related to the development of metabolic databases and discusses how such systems have aided the delineation of genome properties. We illustrate the design features of metabolic databases and discuss the challenges facing metabolic as well as databases of other functional type.

The iProClass integrated database for protein functional analysis

Increasingly, scientists have begun to tackle gene functions and other complex regulatory processes by studying organisms at the global scales for various levels of biological organization, ranging from genomes to metabolomes and physiomes. Meanwhile, new bioinformatics methods have been developed for inferring protein function using associative analysis of functional properties to complement the traditional sequence homology-based methods. To fully exploit the value of the high-throughput system biology data and to facilitate protein functional studies requires bioinformatics infrastructures that support both data integration and associative analysis. The iProClass database, designed to serve as a framework for data integration in a distributed networking environment, provides comprehensive descriptions of all proteins, with rich links to over 50 databases of protein family, function, pathway, interaction, modification, structure, genome, ontology, literature, and taxonomy. In particular, the database is organized with PIRSF family classification and maps to other family, function, and structure classification schemes. Coupled with the underlying taxonomic information for complete genomes, the iProClass system (http://pir.georgetown.edu/iproclass/) supports associative studies of protein family, domain, function, and structure. A case study of the phosphoglycerate mutases illustrates a systematic approach for protein family and phylogenetic analysis. Such studies may serve as a basis for further analysis of protein functional evolution, and its relationship to the co-evolution of metabolic pathways, cellular networks, and organisms.

DNA book

We propose herein a new method of DNA distribution, whereby DNA clones or PCR products are printed directly onto the pages of books and delivered to users along with relevant scientific information. DNA sheets, comprising water-soluble paper onto which DNA is spotted, can be bound into books. Readers can easily extract the DNA fragments from DNA sheets and amplify them using PCR. We show that DNA sheets can withstand various conditions that may be experienced during bookbinding and delivery, such as high temperatures and humidity. Almost all genes (95%-100% of randomly selected RIKEN mouse cDNA clones) were recovered successfully by use of PCR. Readers can start their experiments after a 2-h PCR amplification without waiting for the delivery of DNA clones. The DNA Book thus provides a novel method for delivering DNA in a timely and cost-effective manner. A sample DNA sheet (carrying RIKEN mouse cDNA clones encoding genes of enzymes for the TCA cycle) is included in this issue for field-testing. We would greatly appreciate it if readers could attempt to extract DNA and report the results and whether the DNA sheet was shipped to readers in good condition.

Systematic expression profiling of the mouse transcriptome using RIKEN cDNA microarrays

The number of known mRNA transcripts in the mouse has been greatly expanded by the RIKEN Mouse Gene Encyclopedia project. Validation of their reproducible expression in a tissue is an important contribution to the study of functional genomics. In this report, we determine the expression profile of 57,931 clones on 20 mouse tissues using cDNA microarrays. Of these 57,931 clones, 22,928 clones correspond to the FANTOM2 clone set. The set represents 20,234 transcriptional units (TUs) out of 33,409 TUs in the FANTOM2 set. We identified 7206 separate clones that satisfied stringent criteria for tissue-specific expression. Gene Ontology terms were assigned for these 7206 clones, and the proportion of 'molecular function' ontology for each tissue-specific clone was examined. These data will provide insights into the function of each tissue. Tissue-specific gene expression profiles obtained using our cDNA microarrays were also compared with the data extracted from the GNF Expression Atlas based on Affymetrix microarrays. One major outcome of the RIKEN transcriptome analysis is the identification of numerous nonprotein-coding mRNAs. The expression profile was also used to obtain evidence of expression for putative noncoding RNAs. In addition, 1926 clones (70%) of 2768 clones that were categorized as "unknown EST," and 1969 (58%) clones of 3388 clones that were categorized as "unclassifiable" were also shown to be reproducibly expressed.

Targeting a complex transcriptome: the construction of the mouse full-length cDNA encyclopedia

We report the construction of the mouse full-length cDNA encyclopedia,the most extensive view of a complex transcriptome,on the basis of preparing and sequencing 246 libraries. Before cloning,cDNAs were enriched in full-length by Cap-Trapper,and in most cases,aggressively subtracted/normalized. We have produced 1,442,236 successful 3'-end sequences clustered into 171,144 groups, from which 60,770 clones were fully sequenced cDNAs annotated in the FANTOM-2 annotation. We have also produced 547,149 5' end reads,which clustered into 124,258 groups. Altogether, these cDNAs were further grouped in 70,000 transcriptional units (TU),which represent the best coverage of a transcriptome so far. By monitoring the extent of normalization/subtraction, we define the tentative equivalent coverage (TEC),which was estimated to be equivalent to >12,000,000 ESTs derived from standard libraries. High coverage explains discrepancies between the very large numbers of clusters (and TUs) of this project,which also include non-protein-coding RNAs,and the lower gene number estimation of genome annotations. Altogether,5'-end clusters identify regions that are potential promoters for 8637 known genes and 5'-end clusters suggest the presence of almost 63,000 transcriptional starting points. An estimate of the frequency of polyadenylation signals suggests that at least half of the singletons in the EST set represent real mRNAs. Clones accounting for about half of the predicted TUs await further sequencing. The continued high-discovery rate suggests that the task of transcriptome discovery is not yet complete.