Omics speak

Because of the immediately attractive upswing created by the genomics revolution, and the large financial resources made available in many industrialized countries, adjacent fields of science have adopted similar terms, leading to a great proliferation of designations such as tran-scriptomics, proteomics, and metabolomics, such that some biologists have complained that what was molecular biology before is now named after one of the '-omics' but in fact is still molecular biology. Zhou et al. (2004) proposed a classification of genomics according to three main categories: approach (structural or functional), scientific discipline (evolutionary genomics, ecological geno-mics, etc.), and object of study (plant genomics, microbial genomics, etc.). An Internet page maintained by Mary Chitty (Cambridge Healthtech Institute) provides a glossary containing no less than 60 single-word entries ending with -omics ( The list includes obvious terms such as pharmacogenomics and cardiogenomics, and awkward ones such as sac-charomics (the study of all the carbohydrates in the cell) and vaccinomics (the use of bioinformatics and genomics for vaccine development). The three most common extensions of genomics are tran-scriptomics, proteomics, and metabolomics, and these are introduced briefly here, with reference to Fig. 1.6.

Transcriptomics is the study of all the transcripts that are present at any time in the cell. In principle the transcriptome includes messenger RNAs (mRNAs) in addition to ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and small nuclear RNAs (snRNAs), but transcriptomics is usually limited to mRNA, the template for translation into protein. The main activity in transcriptomics is to obtain a profile of global gene expression in relation to some condition of interest. Which genes are turned

The genome


The transcriptome

The proteome

Transcription RNA splicing RNA editing

£ | ^ Transcriptomics rRNA mRNA tRNA Translation

Enzymes Proteomics Structural proteins

Transcription factors

Ion channels

Signalling proteins


Catalysis Synthesis Transport Transformation

The metabolome


Sugars Amino acids Lipids Secondary metabolites Hormones

Figure 1.6 The relationship between genomics, transcriptomics, proteomics, and metabolomics.

'on' and 'off' during certain phases of the cell cycle? Which genes are upregulated by certain physiological conditions? Which genes change their expression in response to adaptation to the environment? The study of transcriptomes is part of functional genomics, because it does not look at the DNA as such, but at its functions.

In general, it is expected that there are more transcripts than there are protein-encoding genes in the genome, even when considering only those genes that are actually transcribed. This is due to the mechanism of alternative splicing: the generation of different mRNAs from the same pre-mRNA during the removal of introns. RNA editing (post-transcriptional insertion or deletion of nucleotides, or conversion of one base for another) is another reason for incongruence between the genome and the transcriptome.

There are more reasons why a functional analysis of the genome can provide a different picture than an inventory of genes. Obviously, all cells of an organism have the same genome, but not the same transcriptome. Even when looking at cells of the same type, the transcriptome depends on environmental conditions, physiological state, developmental state, etc. So the transcriptome allows a glimpse of the living cell much more than the genome itself. The argument also holds when making comparisons across species. Classical molecular phylogenetics (see Graur and Li 2000) is based on variation of homologous DNA sequences across species. However, the same structural DNA can be regulated in different ways in different species. We illustrate this argument with an example from Enard et al. (2002), who did one of the first studies in what may be called comparative transcriptomics.

Enard et al. (2002) analysed the expression of 18000 genes in liver, blood leucocytes, and brain tissue of humans, chimpanzee (Pan troglodytes), and rhesus monkey (Macaca mulatta). The expression patterns in human blood and liver turned out to be more similar to chimpanzees than to rhesus monkeys, which is in accordance with the phylo-genetic distances between the three primate species; however, the expression profiles in the brain were more similar between chimpanzee and rhesus monkey than between either of the two monkey species and human (Fig. 1.7). So, although chimpanzees share 98.7% of their DNA with humans, the human species expresses that DNA in a different manner, especially in the brain. Gene expression in the brain has undergone accelerated evolution compared to gene expression elsewhere in the body, and evolution has resulted in a divergence of humans from chimpanzees, mostly due to regulatory change rather than structural reorganization of the DNA.

Proteomics is the study of all the proteins in the cell. As with genomics, proteomics arose thanks to technological innovation, which in this case is





1.0 Human

Chimp, 1.3 Human


Figure 1.7 Distance trees showing the similarity of gene-expression profiles in brain, blood leucocytes, and liver of human, chimpanzee, and rhesus monkey. Numbers refer to the ratio between the rate of evolution in the human and the chimpanzee lineages, taking the rhesus monkey as an outgroup. Reprinted with permission from Enard et al. (2002). Copyright 2002 AAAS.

tandem mass spectrometry (MS/MS) and liquid chromatography coupled to tandem mass spec-trometry (LC/MS/MS). The idea is to separate a mixture of soluble proteins by means of chromatography and then to estimate masses, first of the larger peptide and, after a second ionization, of fragments of the same peptide. The fragment patterns provide a fingerprint characteristic of the protein. Interpretation of proteomics data is usually supported by genomic sequence information, in such a way that an observed peptide fragment pattern may be compared to a database of proteins predicted from the genome. Mass spec-trometry may also be used to determine the amino acid sequence of a protein. For this application, the protein is cleaved with a protease, for example trypsin, which generates a collection of fragments characteristic of the protein. These fragments may be compared to an in silico (computer-simulated) digestion derived from the genome and the known cleavage sites of the protease.

The proteome provides a different picture of a cell's activities to the transcriptome. Several authors have indeed wondered about the lack of correlation between mRNA and protein abundances. One of the reasons for this is the existence of control mechanisms at the ribosomes, where mRNA is translated to peptides. Translational control allows the cell to select only certain mRNAs for translation and block others. The selection is often dependent on environmental conditions, so this mechanism allows for physiological adaptation on the level of the proteome, even though the transcriptome remains the same. Another issue is post-translational modification or protein processing, processes that can greatly affect the function of a protein, for example by acetylation or ubiquitina-tion of the N-terminal residue, hydroxylation of prolines, or cleavage of the molecule into smaller units. The proteome and the genome are linked by many feedback mechanisms, because some proteins are transcription factors necessary for gene activation, others are enzymes involved in transcription or translation, and still others are structural components of chromosomes. So, in a molecular biology context, the living cell can only be understood fully by considering genome, tran-scriptome, and proteome together.

As an example of a study applying proteomics in an environmental context, consider the work of Shrader et al. (2003). These authors studied protein fingerprinting in embryos of zebrafish (Danio rerio) exposed to environmental endocrine disrupters. The compound p-nonylphenol is a degradation product of certain detergents and is discharged into the aquatic environment through sewage effluent. Because of its structural similarity to vertebrate steroid hormones, especially oestrogens, nonylphenol has been associated with feminiza-tion of male fish. Fig. 1.8 shows a two-dimensional gel of differential protein expression of fish exposed to nonylphenol. This so-called proteinexpression profile was composed by matching the treatment profile with the control profile and subtracting them from each other. The Venn diagram in Fig. 1.8b provides a pictorial illustration of the number of proteins that are shared between treatments. It is interesting to note that non-ylphenol induced several proteins that were also induced by oestradiol (23 in total), but that a

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