proteins, and regions of antisense transcription (Figure 5-12C).
Probably the biggest application of RNA-seq is quantitation of the relative amounts of gene transcripts in a wild-type bacterium compared to that of a mutant or to that of the wild-type bacterium subjected to a stress condition (Figure 5-12B, right). The basis of this method is that the amount of cDNA synthesized and sequenced using the RNA-seq technology is proportional to the amount of mRNA or sRNA in the initial samples (Figure 5-12A). That is, the number of nucleotide base reads for each mRNA or sRNA is proportional to the amount of mRNA and sRNA in the samples. More reads across a gene or operon relative to a wild-type standard or to control conditions indicate an increased transcript amount, whereas fewer reads indicate less relative transcription.
In quantitation experiments, total RNA is extracted from the two strains whose transcriptomes are to be compared (e.g., a wild-type versus mutant strain), prepared for RNA-seq with two sets of barcodes, and then subjected to Illumina sequencing. Normalization between samples is performed by summing the number of reads for each gene and dividing by the gene length and the total number of reads in that sample. The relative change in transcript amount can then be calculated and compared for each gene in the bacterium. An example is shown in Figure 5-12D of the application of RNA-seq to determine the genes in E. coli whose relative transcript amounts increase or decrease in response to the glucose analogue, α-methylglucoside (αMG). As a final step in this transcriptome analysis, relative changes in transcript amounts detected by RNA-seq were confirmed by the independent method of quantitative reverse-transcription PCR (qRT-PCR). RNA-seq has largely replaced tiled microarrays for transcriptome analyses in bacteria and other organisms.
Coupling of RNA-seq results from total mRNA of a microbial community (metatranscriptomics) with the total genomic DNA content (metagenomics) enables an estimation of which metabolic pathways and protein functions are expressed in a particular sample under certain conditions. In this approach, relative mRNA transcript amounts are determined by the RNA-seq methods described previously. This approach can provide an estimation of the metabolic potential and physiological makeup of that particular microbiota. For example, one study comparing the gut metagenome (i.e., the predicted metabolic genes present) with the corresponding metatranscriptome (i.e., the relative transcription levels of the metabolic genes present) revealed that certain pathways, such as sporulation and amino acid biosynthesis, were consistently under-expressed, whereas other pathways, such as ribosome biogenesis, stress response, and methanogenesis, were consistently overexpressed in relation to their DNA abundance. These findings are consistent with the presumed roles of these pathways under the conditions in the gut. For instance, you would expect an abundance of amino acids in the gut from the digestion of foodstuffs so that bacteria would not need to make their own, and thus amino acid biosynthetic genes would be downregulated. On the other hand, the rich nutrient environment of the gut would encourage bacterial growth, such that there would be a lot of protein synthesis activity and ribosomal subunit genes would be strongly upregulated.
Proteomics and Metabolomics—the Emergence of Multi-Omics. Even the advances in metagenomics and metatranscriptomics may not be enough to fully characterize the functional activity of microbial communities. Two genes in a microbial population may encode the same type of enzyme or transport protein. But different enzymes and different transport proteins have different affinities for their substrates and different levels of activity. Additionally, there may be regulatory factors that impinge on the observed functional activity. Thus, a gene that is expressed at a higher level than another gene may not be more important metabolically or physiologically. To address this need for functional understanding at a population level, other measures of functional potential or activity of the microbiota are necessary.
Measuring the relative abundance of protein and metabolite (small molecule) amounts provides another indicator of the functional activity of a microbial community. Two approaches are now taking off that enable the assessment of proteins and metabolites in biological samples. Proteomics allows assessment of relative protein amounts and stabilities and functional states (activation, inactivation, or modification) of the protein content of samples (Figure 5-13). Metabolomics allows the determination of the composition of all of the metabolites and other small molecules (usually any molecule with a molecular mass less than 1,500 Da) present in a sample (Figure 5-14). Both approaches used to determine protein or metabolite content and abundance depend on chromatographic separation methods, usually high-performance liquid chromatography (HPLC) or gas chromatography (GC), followed by detection, structural determination, identification, and quantification of the contents using analytical techniques, such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, or some other type of spectroscopy.
Figure 5-13. Overview of proteomics technology. Proteomics provides a comprehensive assessment of the protein identity and relative abundance in biological samples. (A) In the simplest identification, proteins in extracts of a bacterial culture are first fractionated by liquid chromatography (LC) and SDS polyacrylamide gel electrophoresis. Individual bands are cut out from gels, and the proteins are digested into peptides with trypsin protease. The resulting peptides are fractionated by high-performance liquid chromatography (HPLC) that is connected in-line to a mass spectrometer detector. The mass/charge (m/z) ratio is then determined for the peptides in the HPLC fraction that can be detected by mass spectrometry. Computer programs are then used to generate in silico predictions of the m/z ratios of all of the tryptic peptides that correspond to proteins predicted from the bacterial genome. The computer then matches the predictions with the peptide profile to identify the unknown protein. The identity of tryptic peptides can be further confirmed by tandem mass spectrometry (MS-MS) analysis, where fragments of the peptides to allow further structural determination of the peptides. With the newest ultra-performance LC systems and mass spectrometer detectors, this analysis has been expanded to complex mixtures of proteins in crude extracts. The crude protein mixtures are digested by trypsin and analyzed by liquid chromatography-mass spectrometry. The presence of as many as 700 proteins can be demonstrated by this method. However, these methods are only semiquantitative for determining the relative abundance of proteins in samples. Nevertheless, protein interaction or regulatory networks can be built for various purposes, such as signaling pathways, biosynthetic pathways, or protein-protein interaction pathways from these proteomic approaches. (B) iTRAQ method to quantitate relative protein amounts in different samples. As with RNA-seq, a major goal of proteomics is often to determine how the relative amounts of proteins change during bacterial growth in a standard unstressed condition compared with stressed conditions or in a wild-type compared to a mutant strain. An ingenious method, called iTRAQ, has been developed for this purpose. In an iTRAQ experiment, protein profiles from as many as eight separate cultures or conditions can be compared. The separate extracts are denatured and then digested to completion with trypsin. Each extract is reacted with one of the eight available iTRAQ labels, which do not change the relative m/z ratio for any given tryptic peptide. However, when each iTRAQ-labeled peptide is fragmented by MS-2, the peptides from the different samples end up with slightly different m/z values that allow resolution and quantitation of the iTRAQ-labeled fragments. The relative amount of each of the different iTRAQ-labeled peptide fragments is directly proportional to the amount of each protein that contains the peptide in the original samples. Adapted from PTM Biolabs (http://ptm-biolab.com/itraq-proteomics), with permission.
Figure 5-14. Overview of metabolomics technology. Metabolomics
