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ne w s and v ie w s previous approach based on a high-copy suppression strategy yielded multiple genes that confer drug resistance when their copy number is increased, making it difficult to identify the correct drug targets without prior knowledge5. Ho et al.2 also identify the gene mutated in a yeast isolate that is resistant to theopalauamide, a novel bicyclic peptide with antifungal and other activities6. The resistance mutation enables them to characterize theopalauamide’s mechanism of action and to determine that it is a member of a novel class of sterol-binding antifungal compounds, a property not detected by genomic profiling experiments using the yeast deletion strains6. Complementation cloning with MoBYORF dovetails nicely with other genomic technologies. Ho et al.2 show that the mutations they identify are corroborated by data from yeast tiling microarrays, suggesting that by combining the two technologies, bona fide drug-resistant mutations can be resolved with great precision and confidence. Another chemical-genomics method, haploinsufficiency profiling (also known as the fitness test), has been shown to have pharmaceutical utility in both baker’s yeast7,8 and Candida albicans9, an opportunistic pathogen that is the leading cause of fungal infections in hospitals worldwide. In haploinsufficiency profiling, an antiproliferative compound is tested on a pool of barcoded yeast strains that are each heterozygous for a single gene deletion. Usually, only a few strains display significant growth deviations from the population, and these are likely to contain a heterozygous deletion of a gene that encodes a target of the compound. Haploinsufficiency profiling provides insights into the mechanisms of action of antiproliferative compounds with minimal quantity of material, even in mixtures such as natural product extracts6,10. However, some drugs, such as amphotericin B, do not generate informative haploinsufficiency profiles8, and others, like theopalauamide, yield profiles that are difficult to decipher6. Characterization of drug-resistant mutations using the MoBYORF library provides a complementary strategy when haploinsufficiency profiling is inadequate. By expending resources upfront on such chemical-genomics studies, researchers are empowered to make informed decisions on subsequent discovery efforts. The MoBY-ORF library and method will have diverse applications in both drug discovery and basic research. The library is a highly portable biological resource, in contrast to the deletion strains. It can be applied in different genetic backgrounds commonly used in drug discovery, including in strains that have a mutator, a defective drug efflux
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pump or are otherwise hypersensitized. The amount of drug required for cloning using MoBY-ORF could be reduced in these backgrounds, and/or by using smaller culture volumes, making the method even more attractive. Moreover, as each plasmid contains mating-assisted genetically integrated cloning (MAGIC) recombination sites that flank the barcoded gene, the entire MoBYORF library can be readily switched to another vector, such as a high-copy vector, which could enable systematic analysis of the gene dosage effect on drug susceptibility in a format similar to that used by Ho et al.2. An intriguing extension of the method would be
to create similar libraries for other organisms. Indeed, in light of recent advances in bacterial chemical genetics, adapting this approach to Escherichia coli would be timely and highly beneficial to antibacterial discovery. 1. Hoon, S. et al. Nat. Chem. Biol. 4, 498–506 (2008). 2. Ho, C.H. et al. Nat. Biotechnol. 27, 369–377 (2009). 3. Broach, J.R. et al. Genes 8, 121–133 (1979). 4. Heitman, J. et al. Science 253, 905–909 (1991). 5. Butcher, R.A. et al. Nat. Chem. Biol. 2, 103–109 (2006). 6. Parsons, A.B. et al. Cell 126, 611–625 (2006). 7. Lum, P.Y. et al. Cell 116, 121–137 (2004). 8. Giaever, G. et al. Proc. Natl. Acad. Sci. USA 101, 793–798 (2004). 9. Xu, D. et al. PLoS Pathog. 3, e92 (2007). 10. Jiang, B. et al. Chem. Biol. 15, 363–374 (2008).
Getting to the core of the gut microbiome Matthias H Tschöp, Philip Hugenholtz & Christopher L Karp Metagenomic analysis of gastrointestinal bacteria sheds light on obesity. Many may be surprised to learn that the bacterial cells in and on their bodies outnumber the human cells by about a factor of ten. As metagenomics research extends its reach to the microbial populations colonizing the gut, skin and other tissues, it is beginning to generate results that may be of interest from a therapeutic perspective. A case in point is a recent study in Nature by Gordon and colleagues1, which examined the role of gut bacteria in the development of obesity. Obesity has reached epidemic proportions in Westernized cultures, and the diseases associated with it—including insulin resistance and type II diabetes mellitus, hepatic steatosis and steatohepatitis, dyslipidemia and atherosclerotic cardiovascular disease—have become major public health problems. Traditional obesity research has focused on environmental and host genetic factors, including descriptive studies in Mathias H. Tschöp is at the Obesity Research Center and Genome Research Institute, Depts. of Psychiatry & Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA; Philip Hugenholtz is at DOE Joint Genome Institute, Walnut Creek, California, USA; and Christopher L. Karp is at the Division of Molecular Immunology, Cincinnati Children’s Hospital Research Foundation and the University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. e-mail:
[email protected]
humans and mechanistic studies using genetically altered rodent models. More recently, research over the last five years using cultureindependent methods and high-throughput sequencing has suggested that the pathogenesis of obesity may be influenced by our endogenous gastrointestinal microbiota2–5. The Gordon group first reported that germfree mice gained weight after colonization with bacteria from the lower gut of conventionally raised mice2. They also showed that the development of obesity in leptin-deficient mice coincided with broad, phylum-level changes in the gut community structure, with obese mice having a reduced fraction of bacteria belonging to the Bacteroidetes phylum and a proportional increase in Firmicutes bacteria6 (although it should be noted that these patterns were apparent only in data averaged over many specimens and could be poor indicators for individual mice). Mechanistically, such microbiota have been implicated in both increasing energy-harvesting efficiency and altering host genes that regulate fat storage. Subsequent publications demonstrated that obese rodents and humans have a significantly lower percentage of Bacteroidetes in their gut microbiome compared with their lean counterparts and that weight loss by obese individuals corrected this disproportion (again observed in averaged data). It was also shown that the microbiome associated with obesity3 is more efficient at harvesting dietary energy4,
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Gut bacterial sample from obese individuals
Organism-level profiling: 16S rRNA gene sequencing Gene-level profiling: metagenomic sequencing
Comparative analysis of sequence data
Gut bacterial sample from lean individuals
Core microbiome
b Caloric intake
Neuroendocrine appetite regulation GI peptide hormones
Antibiotics Gut glucose production
Genetic influence
GI nutrient digestion/ absorption
Bacterial exposure
Gut lipid production
GI surgery Inflammatory signals
Katie Vicari
© 2009 Nature America, Inc. All rights reserved.
Nutrient composition
Figure 1 The core gut microbiome and energy metabolism. (a) The microbial communities of fecal material from lean and obese individuals were profiled at the organism and gene level by 16S rRNA gene and random shotgun sequencing (metagenomics), respectively. After comparative analysis of the data, a key finding was that all individuals share a core microbiome at the level of gene families but not at the level of organisms (abundant populations). This suggests guild structure in the gut where different functionally-related species (different colored pigeons in the cartoon) can occupy the same niche (same pigeon hole location in the cartoon) in different individuals. The central and lower-right pigeon holes denote nonessential functions in the human gut and hence are empty in this schematic. (b) Knowledge of the gut microbiome adds to our understanding of energy balance regulation and the pathophysiology of obesity. Recent studies suggest that gut bacteria modulate the digestibility and absorbability of ingested nutrients, thereby increasing energy-harvesting efficiency. Multiple factors affect the composition and function of the gut microbiome, including dietary, antibiotic, surgical and genetic influences. Gut microbiota also contribute to host metabolism and energy homeostasis in ways beyond gastrointestinal nutrient processing, including modulation of gut-derived endocrine, neuronal and inflammatory signals.
and, most importantly, that transfer of gut microbiota from obese mice to germ-free mice caused more weight gain than similar transfers from lean mice4. The new study by Gordon and colleagues1 provides further insight. The authors used pyrosequencing of 16S rRNA genes to deeply interrogate community structure (phylotype abundance distribution) and randomly sequenced fecal microbial communities (metagenomics) to obtain functional profiles (Fig. 1a). The individuals studied were obese and nonobese adult human twins, and their mothers. Family members had considerable overlap in their gut microbial communities, with the degree of variation being similar between monozygotic and dizygotic twin pairs. Notably, however, obese individuals showed an impressive overall reduction in microbial diversity. The authors liken this reduced diversity to a fertilizer runoff, in which a subset of the
microbial community blooms in response to abnormally high energy input, as opposed to the rainforest- or reef-like community of the lean gut, which displays high species diversity in the face of high energy flux. Most notably, Gordon and colleagues1 identified a core gut microbiome in unrelated individuals (both lean and obese), which occurs at the level of shared bacterial gene families but not at the level of shared abundant bacterial phylotypes (>0.5% of the community). This suggests the presence of guilds—that is, sets of microbial species sharing a common ecological niche—and implies that different combinations of species could fulfill the same functional roles required by the host (Fig. 1a). An interesting follow-up question is whether the lack of shared phylotypes is still as pronounced if rare populations are taken into account. It may be that each individual harbors many low abundance ‘fail-safe’ populations, in common with other
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individuals, for critical functional roles in case the dominant population filling that niche collapses. Such a collapse could be caused by phage predation, allowing a rare population to bloom and substitute for the loss (monocultures by contrast lack this type of functional redundancy and are vulnerable to catastrophic collapse). The Gordon study hints at this possibility as the relative abundance of phylotypes (measured at the phylum level only) within an individual varied between two sampling times even though the specific phylotypes found remained consistent. Consistent with the idea that the gut microbiota influence the efficiency of energy harvest from the diet, the core microbiome was enriched in genes involved in fat, sugar and protein metabolism. The majority of the potentially relevant genes enriched in obesity was derived from Actinobacteria (75%) and Firmicutes (25%), whereas genes enriched in lean individuals were predominantly from
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© 2009 Nature America, Inc. All rights reserved.
ne w s and v ie w s Bacteroidetes (42%). Specifically, the authors identified almost 400 genes of metabolic pathways that were enriched or depleted in the gut microbiome of obese individuals compared with lean controls, with an interesting example being enrichment of phosphotransferase systems responsible for microbial processing of carbohydrates. Despite the apparent absence of a core microbial community in the human gastrointestinal tract, another recent study did find enrichment of particular microbial groups in the gut of obese individuals: the H2-producing Prevotellaceae (ironically, a family belonging to the Bacteroidetes) and the H2-using Methanobacteriales (an order of methanogenic archaea)5. Methanogens increase the extraction of energy by the host from otherwise indigestible polysaccharides7. The finding by Gordon and colleagues1 of gene-level core microbiomes may have important implications. With regard to the Human Microbiome Project, it raises the question of whether community profiling using 16S rRNA genes should be used to select samples for metagenomic sequencing as unifying functional patterns may be missed in samples with variable community profiles. It is also possible that drug targets or drug candidates for the treatment of obesity could be identified from the obesityassociated microbiome. The sudden obesity epidemic is likely to be the result of changes upstream of the human gastrointestinal microbiome (Fig. 1b). The role of host genetics, for example, is underscored by the fact that monozygotic twins exhibit a higher degree of co-variation in body adiposity compared with dizygotic twins8, despite the similar microbial community structures of monozygotic and dizygotic twins demonstrated by Gordon and colleagues 1. Future progress toward understanding the role of the gut microbiome in body-fat regulation should include collaborative approaches aimed at linking microbial genetic studies with established models of molecular bodyweight regulation. Different gut microbiota may well have differential impacts on (i) afferent gastrointestinal peptide hormones that regulate appetite, energy metabolism and body weight; (ii) portal vein–sensed gut glucose production9; and (iii) gut-derived lipid signals to the brain10. Changes in the microbiome may also affect what has been termed “metabolic endotoxemia”—increases in plasma lipopolysaccharide (and, presumably, bacterial lipopeptide) concentrations in mice and humans exposed to high-fat diets11. In this context, it should be noted that mice with genetic deletions in a variety of proinflammatory mediators exhibit exacerbated diet-induced obesity.
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To test the mechanistic hypotheses arising from the results of Gordon and colleagues1 and to develop preventive and therapeutic strategies, we will need to go beyond analysis of the gut microbiome to interdisciplinary collaborations among genome researchers, microbial ecologists, immunologists and obesity experts. An impressive example of such teamwork, also out of the Gordon group12, who identified short-chain fatty acid products of microbial polysaccharide fermentation as ligands of Gpr41, a gut epithelial cell G protein–coupled receptor that regulates peptide hormone secretion, and went on to show that conventionally raised and gnotobiotic (but not germ free) Gpr41−/− mice showed a leaner phenotype compared with similarly raised Gpr41+/+ controls. These results highlight a potentially relevant mechanistic connection between gut microbial function and endogenous molecular pathways controlling energy balance. Interesting as the new molecular data are, they represent only a dip into the lean
and obese gut microbiota gene pools. As the cost of sequencing continues to fall, the next decade should be a productive one for gastrointestinal biology in which microbial-community analysis will be expanded to thousands of individuals and along the complete length of the gastrointestinal tract, and fully integrated with other analytical approaches. 1. Turnbaugh, P.J. et al. Nature 457, 480–484 (2009). 2. Bäckhed, F. et al. Proc. Natl. Acad. Sci. USA 101, 15718–15723 (2004). 3. Ley, R.E., Turnbaugh, P.J., Klein, S. & Gordon, J.I. Nature 444, 1022–1023 (2006). 4. Turnbaugh, P.J. et al. Nature 444, 1027–1031 (2006). 5. Zhang, H. et al. Proc. Natl. Acad. Sci. USA 106, 2365– 2370 (2009). 6. Ley, R.E. et al. Proc. Natl. Acad. Sci. USA 102, 11070– 11075 (2005). 7. Samuel, B.S. & Gordon, J.I. Proc. Natl. Acad. Sci. USA 103, 10011–10016 (2006). 8. Stunkard, A.J., Foch, T.T. & Hrubec, Z. J. Am. Med. Assoc. 256, 51–54 (1986). 9. Troy, S. et al. Cell Metab. 8, 201–211 (2008). 10. Gillum, M.P. et al. Cell 135, 813–824 (2008). 11. Cani, P.D. et al. Diabetes 56, 1761–1772 (2007). 12. Samuel, B.S. et al. Proc. Natl. Acad. Sci. USA 105, 16767–16772 (2008).
Missing lincs in the transcriptome Thomas Gingeras Are long, intervening noncoding (linc) RNAs a new class of functional transcripts? Ask scientists at an RNA meeting whether most of the recently observed non-proteincoding RNAs are functional, and the group is apt to be divided. Many would acknowledge that these RNAs form the bulk of transcriptomes and that they are characterized by remarkable complexity, but skeptics would cite their lack of sequence conservation and the meager results from functional studies using forward and reverse genetics1. A recent report by Guttman et al.2 helps to shed new light on this debate by identifying a large number of non-protein-coding RNAs that are enriched in evolutionarily conserved sequences and map to intergenic regions. The complexity of non-protein-coding RNAs is evidenced by their broad incorporation of genome sequences, their interleaved organization and the variety of types detected, including long and short polyadenylated and nonpolyadenlyated RNAs 3–5. Thomas Gingeras is at Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA. e-mail:
[email protected]
Until recently, however, the extent of this complexity in cells from yeast6 to humans7 has been underappreciated. Interestingly, Guttman et al.2 began not with experimental discovery of novel RNAs but with a computational approach in which they searched outside of protein-coding sequences for chromatin signatures of actively transcribed genes (Fig. 1a). This chromatin signature was defined as tracts of trimethylated lysine 4 of histone H3 (indicative of transcriptional initiation at promoters) adjacent to tracts of trimethylated lysine 36 of histone H3 (indicative of elongation of transcribed regions). From an analysis of four mouse cell types, the authors identified 1,250 unannotated intergenic regions at least 5 kb in size. Subsequent screening of these regions for evolutionarily conserved sequences provided a prioritized list of candidate non-proteincoding RNAs. Expression levels for 350 of these were measured using custom tiling arrays, northern hybridizations and RT-PCR (Fig. 1b). Similar to what was previously reported for intergenic non-protein-coding
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