Spotlights still one step ahead. This might be due to the fact that these approaches inherently come with a mode of action that helps further development. It is noteworthy that one of the compounds with a potential anti host-response action predicted in the Jossef et al. study, the p38 inhibitor SB203580, was already shown in an independent report to protect mice from lethal H5N1 by impairing IAV-induced primary and secondary host gene responses [6]. Also other evidence-guided host-directed approaches that revert host responses to IAV have proven their potential in the animal model with a fully known mode of action [7–9]. Finally, the very first phase II clinical trial of a hostdirected drug against severe influenza was initiated on the basis of molecular mechanisms of action (www.clinicaltrialsregister.eu/ctr-search/trial/2012-004072-19/DE). In conclusion, omics screening approaches are without doubt extremely helpful as tools to gain insights into cellular responses in a broader comprehensive sense. Due to a steady increase in available data we will see a further boost in this field. However, we have to be careful with the interpretation of these kinds of data. These screening data sets do not fully represent conclusive results per se, they just provide a new hypothesis. In that respect, they are no more than a starting point that requires further in-depth studies. One might compare the data sets with a big library without any labels on the bookshelves. It now needs the rationale-driven wet lab scientists that select the right candidates for follow-up functional studies. Thus, while the question posed in the
Trends in Microbiology May 2014, Vol. 22, No. 5
title can be answered with a clear yes, omics alone will not provide conclusive answers. They will rather feed the evidence-driven pipeline by creating new research questions and hypotheses. References 1 Poovorawan, Y. et al. (2013) Global alert to avian influenza virus infection: from H5N1 to H7N9. Pathog. Glob. Health 107, 217–223 2 Hai, R. et al. (2013) Influenza A (H7N9) virus gains neuraminidase inhibitor resistance without loss of in vivo virulence or transmissibility. Nat. Commun. 4, 2854 3 Planz, O. (2013) Development of cellular signaling pathway inhibitors as new antivirals against influenza. Antiviral Res. 98, 457–468 4 Josset, L. et al. (2014) Transcriptomic characterization of the novel avian-origin influenza A (H7N9) virus: specific host response and responses intermediate between avian (H5N1 and H7N7) and human (H3N2) viruses and implications for treatment options. MBio 5, e01102– e01113 5 Watanabe, T. et al. (2010) Cellular networks involved in the influenza virus life cycle. Cell Host Microbe 7, 427–439 6 Borgeling, Y. et al. (2014) Inhibition of p38 mitogen-activated protein kinase impairs influenza virus-induced primary and secondary host gene responses and protects mice from lethal H5N1 infection. J. Biol. Chem. 289, 13–27 7 Droebner, K. et al. (2011) Antiviral activity of the MEK-inhibitor U0126 against pandemic H1N1v and highly pathogenic avian influenza virus in vitro and in vivo. Antiviral Res. 92, 195–203 8 Ehrhardt, C. et al. (2013) The NF-kappaB inhibitor SC75741 efficiently blocks influenza virus propagation and confers a high barrier for development of viral resistance. Cell. Microbiol. 15, 1198–1211 9 Khoufache, K. et al. (2013) PAR1 contributes to influenza A virus pathogenicity in mice. J. Clin. Invest. 123, 206–214
Omics: Fulfilling the Promise
Single cell genomics of deep ocean bacteria Weizhou Zhao and Siv G.E. Andersson Department of Molecular Evolution, Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
SAR11 is one of the most abundant bacterioplanktons in the upper surface waters of the oceans. In a recent issue of The ISME Journal, Thrash and colleagues present the genomes of four single SAR11 cells isolated from the deep oceans that are enriched in genes for membrane biosynthetic functions. Microbial life in the upper surface waters of the oceans is well studied, with genome sequence data collected for hundreds of cultivated strains [1]. Additionally, cultivation-independent approaches, such as metagenomics [2], and single cell genomics [3] have been used to gather information about cells that cannot be cultivated using standard methods. These studies have shown that the majority of cells in the upper surface waters clusters with Prochlorococcus and the SAR11 group of the Alphaproteobacteria. The SAR11 clade of bacteria is abundant also in Corresponding author: Andersson, S.G.E. ([email protected]). 0966-842X/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tim.2014.03.002
the deep oceans [4] and of particular interest for studies of niche specialization. The clade contains a highly diverse group of bacteria, further classified into different subclades. The upper surface water strains have been classified into subclade Ia, whereas subclades IIIa and IIIb consist of coastal and freshwater strains, respectively. Single cell genomics of freshwater SAR11 cells suggest that the transition from marine to freshwater systems has purged most of the genetic diversity present in the oceanic SAR11 strains [5]. In a recent report, Thrash et al. sequenced 4 single amplified genomes (SAGs) isolated from the mesopelagic zone at 770 meters [6]. The cells were selected to represent the breath of a monophyletic group called subclade Ic, and known from 16S rRNA gene sequences to be prevalent in the deep oceans [7]. Subclade Ia and Ic are quite divergent from each other, with a rRNA sequence identity of 95% and an amino acid identity of 62%, suggesting that they should be considered different genera. The genome sizes of the SAGs were estimated to 1.5 Mb, similar to the 1.4–1.6 Mb genomes of cells classified into subclade Ia. The relative 233
Spotlights abundances of subclades Ia and Ic were estimated by recruiting reads from metagenome datasets from different depths and locations to a set of reference genomes using reciprocal best BLAST hits. Although this rather stringent approach reduces the risk of recruiting unrelated reads to the reference SAGs, the full diversity of reads from related cells might not be recovered. Nevertheless, the analysis showed an inverse correlation such that subclade Ia represented >70% of all reads in the upper surface waters whereas subclade Ic represented >50% of reads from samples taken at depths from 200 to 6,000 meters. This confirms that the SAR11 group consists of subgroups with distinct habitat preferences in the oceans. A key question concerns the identification of adaptive features by comparative analyses of the genomic differences between the two subclades. Minor increases in intergenic lengths and genome sizes were noted in the deep oceans SAGs, but these are unlikely to represent adaptive strategies to the deep oceans. More interestingly, genes for cell wall and membrane proteins were enriched in the deep oceans SAGs, including glycosyltransferases, methyltransferases, sugar epimerases and polysaccharide export proteins. Likewise, the freshwater SAGs were found to contain a unique complement of membrane and outer surface proteins [5]. Several of these genes were located in a hypervariable region, suggesting that they are prone to dynamic changes. As such, they are prime candidates of adaptive traits. As a complement to the comparative approach, Trash et al. [6] searched for adaptive traits by comparing gene abundances in the deep oceans, as inferred from the coverage of reads recruited to each of the individual genes. Here, the argument was that genes that are highly conserved among strains are more important than those that are less conserved between strains and thereby recruit fewer reads. This concept is simple in principle, but complicated in practise. For example, outer surface structures that evolve rapidly in sequence and are only present in a few strains of the population could easily be missed despite encoding important adaptive traits. Indeed, many genes in the hypervariable region, such as those for flagellar proteins, showed little or no recruitment of reads. Nevertheless, with this caveat in mind, 39 protein families in all SAR11 genomes were significantly more abundant in metagenome datasets obtained from waters below 200 meters compared with the surface datasets. These included Fe-S oxidoreductases encoded by multiple genes in each SAG as well as sulfite oxidase and adenosine phosphosulfate reductase genes, indicative of sulfur chemolitotrophy. Thus, partially reduced sulfur compounds may be utilized by the SAR11 bacteria below 200 meters of depth where oxygen concentrations are low and sulfur cycling has been identified.
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Genes for proteorhodopsins were identified in two of the deep ocean SAGs. This finding was surprising since these genes are thought to be essential for growth in the upper surface waters, but not in the deep oceans. The proteorhodopsin genes showed modest recruitment of metagenomic reads from all depths. Moreover, the gene phylogeny matched the SAR11 phylogeny, arguing against recent horizontal gene transfers. To explain this finding it was suggested that strains of subclade Ic occasionally circulate up to the euphotic zone. Phage predation is considered an important driver of diversification processes in bacterial populations [8]. According to the constant-diversity model, expansion of high-fitness cells will eventually be suppressed by a corresponding increase in the abundance of phages that target these cells restoring a balanced mixture of bacteria and their phages. The presence of hypervariable regions containing genes for outer membrane components in all SAR11 strains could indicate selection to avoid phage predation. In this context, it is interesting to note that pelagispecific phages that infect surface strains were recently discovered [9] and that phage genes are enriched in the deeper regions of the oceans [9,10]. The ability to survive at multiple depths might enable escape from phage infections. Thus, the deep oceans could contain reservoirs of genetic diversity that occasionally rises to the upper surface waters and mixes with the diversity of cells at other depths, resulting in an efficient utilization of the available resources in the oceans. References 1 Yooseph, S. et al. (2010) Genomic and functional adaptation in surface ocean planktonic bacteria. Nature 468, 60–67 2 Rusch, D.B. et al. (2007) The Sorcerer II Global Ocean Sampling expedition: Northwest Atlantic through Eastern Tropical Pacific. PLoS Biol. 5, e77 3 Swan, B.K. et al. (2013) Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl. Acad. Sci. U.S.A. 110, 11463–11468 4 Konstantinidis, K.T. et al. (2009) Comparative metagenomic analysis of a microbial community residing at a depth of 4,000 meters at station ALOHA in the North Pacific subtropical gyre. Appl. Environ. Microbiol. 75, 5345–5355 5 Zaremba-Niedzwiedzka, K. et al. (2013) Single-cell genomics reveal low recombination frequencies in freshwater bacteria of the SAR11 clade. Genome Biol. 14, R130 6 Thrash, J.C. et al. (2014) Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. http://dx.doi.org/10.1038/ ismej.2013.243 7 Lauro, F.M. and Bartlett, D.H. (2008) Prokaryotic lifestyles in deep sea habitats. Extremophiles 12, 15–25 8 Rodriguez-Valera, F. et al. (2009) Explaining microbial population genomics through phage predation. Nat. Rev. Microbiol. 7, 828–836 9 Zhao, Y. et al. (2013) Abundant SAR11 viruses in the ocean. Nature 494, 357–360 10 Martin-Cuadro, A-B. et al. (2007) Metagenomics of the deep Mediterranean, a warm bathypelagic habitat. PLoS ONE 2, e914
Trends in Microbiology May 2014, Vol. 22, No. 5
title can be answered with a clear yes, omics alone will not provide conclusive answers. They will rather feed the evidence-driven pipeline by creating new research questions and hypotheses. References 1 Poovorawan, Y. et al. (2013) Global alert to avian influenza virus infection: from H5N1 to H7N9. Pathog. Glob. Health 107, 217–223 2 Hai, R. et al. (2013) Influenza A (H7N9) virus gains neuraminidase inhibitor resistance without loss of in vivo virulence or transmissibility. Nat. Commun. 4, 2854 3 Planz, O. (2013) Development of cellular signaling pathway inhibitors as new antivirals against influenza. Antiviral Res. 98, 457–468 4 Josset, L. et al. (2014) Transcriptomic characterization of the novel avian-origin influenza A (H7N9) virus: specific host response and responses intermediate between avian (H5N1 and H7N7) and human (H3N2) viruses and implications for treatment options. MBio 5, e01102– e01113 5 Watanabe, T. et al. (2010) Cellular networks involved in the influenza virus life cycle. Cell Host Microbe 7, 427–439 6 Borgeling, Y. et al. (2014) Inhibition of p38 mitogen-activated protein kinase impairs influenza virus-induced primary and secondary host gene responses and protects mice from lethal H5N1 infection. J. Biol. Chem. 289, 13–27 7 Droebner, K. et al. (2011) Antiviral activity of the MEK-inhibitor U0126 against pandemic H1N1v and highly pathogenic avian influenza virus in vitro and in vivo. Antiviral Res. 92, 195–203 8 Ehrhardt, C. et al. (2013) The NF-kappaB inhibitor SC75741 efficiently blocks influenza virus propagation and confers a high barrier for development of viral resistance. Cell. Microbiol. 15, 1198–1211 9 Khoufache, K. et al. (2013) PAR1 contributes to influenza A virus pathogenicity in mice. J. Clin. Invest. 123, 206–214
Omics: Fulfilling the Promise
Single cell genomics of deep ocean bacteria Weizhou Zhao and Siv G.E. Andersson Department of Molecular Evolution, Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
SAR11 is one of the most abundant bacterioplanktons in the upper surface waters of the oceans. In a recent issue of The ISME Journal, Thrash and colleagues present the genomes of four single SAR11 cells isolated from the deep oceans that are enriched in genes for membrane biosynthetic functions. Microbial life in the upper surface waters of the oceans is well studied, with genome sequence data collected for hundreds of cultivated strains [1]. Additionally, cultivation-independent approaches, such as metagenomics [2], and single cell genomics [3] have been used to gather information about cells that cannot be cultivated using standard methods. These studies have shown that the majority of cells in the upper surface waters clusters with Prochlorococcus and the SAR11 group of the Alphaproteobacteria. The SAR11 clade of bacteria is abundant also in Corresponding author: Andersson, S.G.E. ([email protected]). 0966-842X/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tim.2014.03.002
the deep oceans [4] and of particular interest for studies of niche specialization. The clade contains a highly diverse group of bacteria, further classified into different subclades. The upper surface water strains have been classified into subclade Ia, whereas subclades IIIa and IIIb consist of coastal and freshwater strains, respectively. Single cell genomics of freshwater SAR11 cells suggest that the transition from marine to freshwater systems has purged most of the genetic diversity present in the oceanic SAR11 strains [5]. In a recent report, Thrash et al. sequenced 4 single amplified genomes (SAGs) isolated from the mesopelagic zone at 770 meters [6]. The cells were selected to represent the breath of a monophyletic group called subclade Ic, and known from 16S rRNA gene sequences to be prevalent in the deep oceans [7]. Subclade Ia and Ic are quite divergent from each other, with a rRNA sequence identity of 95% and an amino acid identity of 62%, suggesting that they should be considered different genera. The genome sizes of the SAGs were estimated to 1.5 Mb, similar to the 1.4–1.6 Mb genomes of cells classified into subclade Ia. The relative 233
Spotlights abundances of subclades Ia and Ic were estimated by recruiting reads from metagenome datasets from different depths and locations to a set of reference genomes using reciprocal best BLAST hits. Although this rather stringent approach reduces the risk of recruiting unrelated reads to the reference SAGs, the full diversity of reads from related cells might not be recovered. Nevertheless, the analysis showed an inverse correlation such that subclade Ia represented >70% of all reads in the upper surface waters whereas subclade Ic represented >50% of reads from samples taken at depths from 200 to 6,000 meters. This confirms that the SAR11 group consists of subgroups with distinct habitat preferences in the oceans. A key question concerns the identification of adaptive features by comparative analyses of the genomic differences between the two subclades. Minor increases in intergenic lengths and genome sizes were noted in the deep oceans SAGs, but these are unlikely to represent adaptive strategies to the deep oceans. More interestingly, genes for cell wall and membrane proteins were enriched in the deep oceans SAGs, including glycosyltransferases, methyltransferases, sugar epimerases and polysaccharide export proteins. Likewise, the freshwater SAGs were found to contain a unique complement of membrane and outer surface proteins [5]. Several of these genes were located in a hypervariable region, suggesting that they are prone to dynamic changes. As such, they are prime candidates of adaptive traits. As a complement to the comparative approach, Trash et al. [6] searched for adaptive traits by comparing gene abundances in the deep oceans, as inferred from the coverage of reads recruited to each of the individual genes. Here, the argument was that genes that are highly conserved among strains are more important than those that are less conserved between strains and thereby recruit fewer reads. This concept is simple in principle, but complicated in practise. For example, outer surface structures that evolve rapidly in sequence and are only present in a few strains of the population could easily be missed despite encoding important adaptive traits. Indeed, many genes in the hypervariable region, such as those for flagellar proteins, showed little or no recruitment of reads. Nevertheless, with this caveat in mind, 39 protein families in all SAR11 genomes were significantly more abundant in metagenome datasets obtained from waters below 200 meters compared with the surface datasets. These included Fe-S oxidoreductases encoded by multiple genes in each SAG as well as sulfite oxidase and adenosine phosphosulfate reductase genes, indicative of sulfur chemolitotrophy. Thus, partially reduced sulfur compounds may be utilized by the SAR11 bacteria below 200 meters of depth where oxygen concentrations are low and sulfur cycling has been identified.
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Trends in Microbiology May 2014, Vol. 22, No. 5
Genes for proteorhodopsins were identified in two of the deep ocean SAGs. This finding was surprising since these genes are thought to be essential for growth in the upper surface waters, but not in the deep oceans. The proteorhodopsin genes showed modest recruitment of metagenomic reads from all depths. Moreover, the gene phylogeny matched the SAR11 phylogeny, arguing against recent horizontal gene transfers. To explain this finding it was suggested that strains of subclade Ic occasionally circulate up to the euphotic zone. Phage predation is considered an important driver of diversification processes in bacterial populations [8]. According to the constant-diversity model, expansion of high-fitness cells will eventually be suppressed by a corresponding increase in the abundance of phages that target these cells restoring a balanced mixture of bacteria and their phages. The presence of hypervariable regions containing genes for outer membrane components in all SAR11 strains could indicate selection to avoid phage predation. In this context, it is interesting to note that pelagispecific phages that infect surface strains were recently discovered [9] and that phage genes are enriched in the deeper regions of the oceans [9,10]. The ability to survive at multiple depths might enable escape from phage infections. Thus, the deep oceans could contain reservoirs of genetic diversity that occasionally rises to the upper surface waters and mixes with the diversity of cells at other depths, resulting in an efficient utilization of the available resources in the oceans. References 1 Yooseph, S. et al. (2010) Genomic and functional adaptation in surface ocean planktonic bacteria. Nature 468, 60–67 2 Rusch, D.B. et al. (2007) The Sorcerer II Global Ocean Sampling expedition: Northwest Atlantic through Eastern Tropical Pacific. PLoS Biol. 5, e77 3 Swan, B.K. et al. (2013) Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl. Acad. Sci. U.S.A. 110, 11463–11468 4 Konstantinidis, K.T. et al. (2009) Comparative metagenomic analysis of a microbial community residing at a depth of 4,000 meters at station ALOHA in the North Pacific subtropical gyre. Appl. Environ. Microbiol. 75, 5345–5355 5 Zaremba-Niedzwiedzka, K. et al. (2013) Single-cell genomics reveal low recombination frequencies in freshwater bacteria of the SAR11 clade. Genome Biol. 14, R130 6 Thrash, J.C. et al. (2014) Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. http://dx.doi.org/10.1038/ ismej.2013.243 7 Lauro, F.M. and Bartlett, D.H. (2008) Prokaryotic lifestyles in deep sea habitats. Extremophiles 12, 15–25 8 Rodriguez-Valera, F. et al. (2009) Explaining microbial population genomics through phage predation. Nat. Rev. Microbiol. 7, 828–836 9 Zhao, Y. et al. (2013) Abundant SAR11 viruses in the ocean. Nature 494, 357–360 10 Martin-Cuadro, A-B. et al. (2007) Metagenomics of the deep Mediterranean, a warm bathypelagic habitat. PLoS ONE 2, e914
