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Will omics help to cure the flu?
Spotlights
Omics: Fulfilling the Promise
Will omics help to cure the flu? Stephan Ludwig Institute of Molecular Virology, Centre for Molecular Biology of Inflammation, Westfaelische-Wilhelms-University Muenster, Von Esmarch-Str. 56, 48149 Muenster, Germany
Influenza virus infections are still a major burden to mankind and our antiviral arsenal against these pathogens is limited. The cellular responses to infection might provide novel targets for intervention strategies. Josset et al. combined comparative transcriptome analysis with literature-based prediction tools for in silico identification of novel host-directed drugs. Influenza A viruses (IAVs) have killed millions of people in the past century and will always pose a severe threat to humans and animals due to the fact that these pathogens cannot be eradicated. This constant threat is highlighted by the increasing number of avian-derived IAVs that infect and cause severe diseases in humans [1]. This includes the highly pathogenic avian H5N1 viruses from 1997 that still persist and sporadically infect humans. Recently, a novel avian-origin H7N9 IAV emerged in China, causing mild to lethal human respiratory infections. There is an urgent concern whether H7N9 may further adapt to humans and may be the next pandemic influenza virus. Classical anti-influenza therapies target viral proteins and are consequently subject to resistance. Accordingly, H7N9 viruses easily acquire resistance to neuramindase inhibitors without losing fitness [2]. To counteract this limitation, alternative strategies are under investigation that either directly target cellular factors required for replication or invert detrimental innate host responses [3]. This field of research has received an enormous boost in the past couple of years due to technical developments, such as transcriptome analysis or genome wide siRNA screens that shed light on cellular responses to infection. To better characterize the novel H7N9 virus, Josset et al. [4] recently used a high-throughput profiling approach to explore H7N9 host responses. The aim of the study was not only to accelerate our understanding of the biology of these viruses but also to help guiding hostdirected antiviral development. The authors applied global transcriptomic profiling to human cells infected with the H7N9 strain A/Anhui/01/ 2013 or with other candidate avian or human IAV strains of different subtypes. They showed that H7N9 induces both a specific gene expression response and responses intermediate between those to H3N2 and those to avian H5N1 and H7N7. H7N9 induced a downregulation of genes involved in the antigen presentation pathway and delayed Corresponding author: Ludwig, S. ([email protected]). 0966-842X/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tim.2014.03.003
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proinflammatory cytokine induction to a greater extent than H5N1 and H7N7, which could have an important impact on in vivo immune responses. Similar to seasonal human H3N2, H7N9 induced minor changes in genes connected with eicosanoid signaling and genes implicated in chromatin modification. This similarity may reflect the potential of this new virus for adaptation to humans. One strength of the study is that different viruses were used in comparison, helping to unravel specific responses that characterize H7N9. On top of that, the authors further used the data of differentially regulated genes to identify potential antivirals that target cellular responses to these IAVs. They used a combination of two complementary methods, a knowledge-based approach using the Ingenuity Knowledge Database prioritizing drugs with published effects that are opposite to IAV-induced gene expression changes, and an approach using drug-affected gene expression profiles present in a publicly available Connectivity Map (Cmap) database, a collection of genome-wide transcriptional data from cultured human cells treated with 1309 different compounds. Out of the 26 drugs predicted by this means to have an effect on at least one IAV under investigation, 17 have already been tested against influenza in cell or mouse models and 14 had antiviral effects in those tests. Among those, several FDA-approved drugs were able to reverse the host response to H7N9 and may thus serve as novel candidate antivirals against influenza with a significantly shortened development time. While the overlap with published antiviral agents shows the power of the approach, which should not be underestimated, it is now of urgent importance to design follow-up studies. It has to be stressed that the data so far are still predictions that have to be experimentally or clinically verified. In fact, given the increasing number of transcriptome, proteome and siRNA screening approaches of influenza virus-infected cells that have been published recently, the benefits in terms of further developments deriving from these studies are so far frustratingly low. This might be due to the fact that there are still major variations in the experimental settings that may greatly limit the conclusions that can be drawn. One example that highlights this problem are the results of a meta-analysis of a recent set of siRNA approaches in influenza virus-infected cells which showed surprisingly little overlap [5]. Thus, there are still many question marks with regard to data comparison and data interpretation. While this might soon change, so far evidence-driven approaches to identify cellular points of intervention are
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
Omics: Fulfilling the Promise
Will omics help to cure the flu? Stephan Ludwig Institute of Molecular Virology, Centre for Molecular Biology of Inflammation, Westfaelische-Wilhelms-University Muenster, Von Esmarch-Str. 56, 48149 Muenster, Germany
Influenza virus infections are still a major burden to mankind and our antiviral arsenal against these pathogens is limited. The cellular responses to infection might provide novel targets for intervention strategies. Josset et al. combined comparative transcriptome analysis with literature-based prediction tools for in silico identification of novel host-directed drugs. Influenza A viruses (IAVs) have killed millions of people in the past century and will always pose a severe threat to humans and animals due to the fact that these pathogens cannot be eradicated. This constant threat is highlighted by the increasing number of avian-derived IAVs that infect and cause severe diseases in humans [1]. This includes the highly pathogenic avian H5N1 viruses from 1997 that still persist and sporadically infect humans. Recently, a novel avian-origin H7N9 IAV emerged in China, causing mild to lethal human respiratory infections. There is an urgent concern whether H7N9 may further adapt to humans and may be the next pandemic influenza virus. Classical anti-influenza therapies target viral proteins and are consequently subject to resistance. Accordingly, H7N9 viruses easily acquire resistance to neuramindase inhibitors without losing fitness [2]. To counteract this limitation, alternative strategies are under investigation that either directly target cellular factors required for replication or invert detrimental innate host responses [3]. This field of research has received an enormous boost in the past couple of years due to technical developments, such as transcriptome analysis or genome wide siRNA screens that shed light on cellular responses to infection. To better characterize the novel H7N9 virus, Josset et al. [4] recently used a high-throughput profiling approach to explore H7N9 host responses. The aim of the study was not only to accelerate our understanding of the biology of these viruses but also to help guiding hostdirected antiviral development. The authors applied global transcriptomic profiling to human cells infected with the H7N9 strain A/Anhui/01/ 2013 or with other candidate avian or human IAV strains of different subtypes. They showed that H7N9 induces both a specific gene expression response and responses intermediate between those to H3N2 and those to avian H5N1 and H7N7. H7N9 induced a downregulation of genes involved in the antigen presentation pathway and delayed Corresponding author: Ludwig, S. ([email protected]). 0966-842X/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tim.2014.03.003
232
Trends in Microbiology, May 2014, Vol. 22, No. 5
proinflammatory cytokine induction to a greater extent than H5N1 and H7N7, which could have an important impact on in vivo immune responses. Similar to seasonal human H3N2, H7N9 induced minor changes in genes connected with eicosanoid signaling and genes implicated in chromatin modification. This similarity may reflect the potential of this new virus for adaptation to humans. One strength of the study is that different viruses were used in comparison, helping to unravel specific responses that characterize H7N9. On top of that, the authors further used the data of differentially regulated genes to identify potential antivirals that target cellular responses to these IAVs. They used a combination of two complementary methods, a knowledge-based approach using the Ingenuity Knowledge Database prioritizing drugs with published effects that are opposite to IAV-induced gene expression changes, and an approach using drug-affected gene expression profiles present in a publicly available Connectivity Map (Cmap) database, a collection of genome-wide transcriptional data from cultured human cells treated with 1309 different compounds. Out of the 26 drugs predicted by this means to have an effect on at least one IAV under investigation, 17 have already been tested against influenza in cell or mouse models and 14 had antiviral effects in those tests. Among those, several FDA-approved drugs were able to reverse the host response to H7N9 and may thus serve as novel candidate antivirals against influenza with a significantly shortened development time. While the overlap with published antiviral agents shows the power of the approach, which should not be underestimated, it is now of urgent importance to design follow-up studies. It has to be stressed that the data so far are still predictions that have to be experimentally or clinically verified. In fact, given the increasing number of transcriptome, proteome and siRNA screening approaches of influenza virus-infected cells that have been published recently, the benefits in terms of further developments deriving from these studies are so far frustratingly low. This might be due to the fact that there are still major variations in the experimental settings that may greatly limit the conclusions that can be drawn. One example that highlights this problem are the results of a meta-analysis of a recent set of siRNA approaches in influenza virus-infected cells which showed surprisingly little overlap [5]. Thus, there are still many question marks with regard to data comparison and data interpretation. While this might soon change, so far evidence-driven approaches to identify cellular points of intervention are
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