Editorial For reprint orders, please contact: [email protected]
Systems biology in nanosafety research
“...a majority of studies published to date fall short of achieving a systems biology view (or model) of the perturbations inflicted by exposure to nanomaterials...” Keywords: computational biology • engineered nanomaterials • gene-expression profiling • nanosafety • systems biology
In his famous essay, Lazebnik [1] discussed whether a biologist can fix a broken radio and suggested that applying the ‘biologist approach’ to a broken radio will likely not lead to a successful end, while any engineer or even a trained repairman evidently could fix the radio. In biology, the study of molecules one by one is fine for gathering information on how molecules work, but deciphering biological mechanisms, or the function of biological systems, is a different story. One difference between an engineer and a biologist, according to Lazebnik, lies in the language: in biology, language is oftentimes vague and nonquantitative which limits the possibility of making predictions [1] . In systems biology, a quantitative and predictive language is adopted to describe biological knowledge in order to understand how molecules act together within the network of interactions that makes up a living system. Systems biology, in other words, is the holistic analysis of complex systems. Indeed, systems biology can be defined as the computational and mathematical modeling of complex biological systems, in contrast to the traditional, reductionist approach to biology. Common technology platforms that are deployed to obtain complex datasets are transcriptomics, proteomics, metabolomics and epigenomics. However, systems biology should not be seen merely as the generation of lists of genes, proteins or metabolites using such omics platforms; the objective is to exploit
10.2217/NNM.15.17 © 2015 Future Medicine Ltd
these data and to develop quantitative models that describe the biological system and its response to individual perturbations (see [2] for an excellent introduction to this subject). Furthermore, systems biology can be seen as an iterative interplay between discovery- and hypothesis-driven science: “global observations (discoveries) are matched against model predictions (hypotheses) in an iterative manner, leading to the formation of new models, new predictions, and new experiments to test them” [2] . Systems toxicology, in turn, has been described as “the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization”, and it has been suggested that its application could be part of a new and improved risk assessment [3] . This is nothing less than a paradigm shift in the way that toxicology is conducted. Importantly, systems toxicology – and systems biology in general – relies heavily on computational approaches to manage, analyze and interpret the data generated by large-scale experiments, and computer databases are thus an integral feature of this approach [2] . To this end, commonly accepted repositories and software environments are crucially important [4] . This does not mean that the introduction of systems toxicology approaches is breaking the rice bowl of the classically trained toxicologist. Instead, the ‘new’ toxicology is
Nanomedicine (Lond.) (2015) 10(7), 1039–1041
Bengt Fadeel Nanosafety & Nanomedicine Laboratory, Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, 171 77 Stockholm, Sweden bengt.fadeel@ ki.se
part of
ISSN 1743-5889
1039
Editorial Fadeel multidisciplinary, combining the expertise and principles of computer science, engineering, mathematics, physics and chemistry, with those of biology and toxicology [3] . Detailed knowledge of biology will always be required to make sense of the data. The purpose of this editorial is to give examples of emerging systems biology approaches for the assessment of nanomaterial effects in living organisms and to highlight some of the promises and challenges.
“
...systems biology can be defined as the computational and mathematical modeling of complex biological systems...
”
First, as discussed by Sturla et al., experimental transparency is one of the major requirements for omics experiments [3] . Indeed, standards have been developed for the ‘minimum information about a microarray experiment’ (MIAME) and the ‘minimum information about a proteomics experiment’ (MIAPE) and this has also become the standard for datasets deposited in public databases [3] . Do we need to invent new standards for omics experiments specifically in nanosafety research? The answer to this question is ‘yes’ and ‘no’. No, because it makes far more sense to adopt existing standards for data generation and data sharing in the omics and systems biology community. Yes, because there are still major deficiencies in the way that nanotoxicological experiments are designed [5] , and if the experiment is poorly designed or controlled, and/or if the nanomaterials that are under study are poorly characterized in terms of their physico-chemical properties, or the dispersions are poorly controlled, then the omics data that are generated will, naturally, be of poor quality too, regardless of the degree of sophistication of the omics technologies applied. Hence, it would seem appropriate for the global community of nanosafety researchers to devise a ‘minimum information about a nanotoxicology experiment’ (MIANE) standard, including guidelines on how to report the dose metric used, and how to record physico-chemical properties of the materials studied (and reference materials), that can be applied across different laboratories. It deserves to be noted that while nanomaterials may be categorized and regulated as chemicals, their behavior in, and interactions with, biological systems differs from that of chemicals; in fact, it has been argued that chemically well-defined nanoparticles are “an extension of the classical concept of the molecule” insofar as they combine the properties of solids – such as magnetism, or fluorescence – with mobility, a property of molecules [6] . When we surveyed the literature 5 years ago [7] , there were only scattered examples of gene or protein expres-
1040
Nanomedicine (Lond.) (2015) 10(7)
sion studies to address the toxicities of nanomaterials. Now, more and more studies are being published in which different omics technologies have been applied to study responses to nanoparticles, with a predominance of gene-expression profiling studies, but studies employing proteomics, epigenomics and metabolomics methodologies are also emerging; in some, but not all cases, phenotypic hypotheses generated through computational analysis of the data were tested. In fact, a majority of studies published to date fall short of achieving a systems biology view (or model) of the perturbations inflicted by exposure to nanomaterials, and appear mainly to catalog changes in gene or protein expression. For comparison, Pillai et al. have provided an illustrative example of a systems-based approach in toxicology in which transcriptome and proteome data are paired with physiological responses to silver, using the green algae Chlamydomonas reinhardtii as a model system [8] . The authors achieved a mechanistic understanding not only of the toxicity pathways elicited by exposure to Ag+, but also explored the adaptive response pathways, which may be equally as important for understanding the outcome of exposure to a toxicant. One may ask whether there are any ‘novel’ (i.e., unexpected) toxicities associated with nanomaterial exposure, and whether systems toxicology approaches can aid in unveiling such toxicities. Some researchers have argued that “the focus on the search for ‘nano-specific’ [toxicity] may have the effect of ‘re-inventing the wheel’ of what is already known for conventional particles” [9] , and suggested that “the final common pathways for pathological effects are entirely shared by both nano particles and conventional particles and no novel pathogenic pathways are anticipated” [9] . However, while the final outcome at the tissue or organ level (for instance, inflammation or cancer) may be shared with other toxicants, simply due to the fact that conserved signaling pathways are activated in response to diverse stimuli, it remains possible that the proximal events, such as translocation of nanoparticles across biological barriers, may be unusual or unanticipated or novel; indeed, as discussed before, nanoparticles, by virtue of their small size, may combine the properties of classical solids with those of molecules [6] . Furthermore, systems toxicology will surely have a place in nanosafety research insofar as omics-based approaches – combined with appropriate computational analysis of the data – may be applied to study more subtle effects not readily detected using conventional toxicological assays. Kodali et al. [10] reported that preincubation of primary murine bone marrow-derived macrophages with superparamagnetic iron oxide nanoparticles (SPIONs) that did not elicit acute cytotoxicity as measured by
future science group
Systems biology in nanosafety research
LDH leakage from these cells caused extensive transcriptional reprogramming in response to bacterial lipopolysaccharide challenge. Notably, macrophages exposed to SPIONs displayed diminished phagocytic activity toward the lung pathogen Streptococcus pneumoniae. The authors concluded that biological effects of engineered nanomaterials may not only occur through direct cytotoxicity, but may be indirectly manifested after challenging normal (immune) cell function [10] . Furthermore, we recently performed whole transcriptome sequencing of primary human bronchial epithelial cells exposed to poly(amidoamine) dendrimers, under conditions when these nanoparticles did not trigger cell death using conventional cell viability assays [11] . Importantly, the global geneexpression profiling approach coupled with detailed bioinformatics assessment revealed that all of the most significantly differentially expressed gene categories were related to cell cycle and cell division. In other words, we were able to define the predominant (transcriptional) effects of these nanoparticles and subsequent biological experiments validated these findings. In addition, using pathway analysis software, we identified NF-κB as a potential upstream regulator of gene expression, and this in silico-based prediction was borne out in cell-based assays [11] . Thus, we could validate hypotheses based on computational analysis of the transcriptomics data. References 1
Lazebnik Y. Can a biologist fix a radio? Or, what I learned while studying apoptosis. Cancer Cell 2(3), 179–182 (2002).
2
Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu. Rev. Genom. Hum. Genet. 2, 343–372 (2001).
3
Sturla SJ, Boobis AR, FitzGerald RE et al. Systems toxicology: from basic research to risk assessment. Chem. Res. Toxicol. 27(3), 314–329 (2014).
4
Kitano H. Computational systems biology. Nature 420(6912), 206–210 (2002).
5
Krug HF. Nanosafety research – are we on the right track? Angew. Chem. Int. Ed. Engl. 53(46), 12304–12319 (2014).
6
Stark WJ. Nanoparticles in biological systems. Angew. Chem. Int. Ed. Engl. 50(6), 1242–1258 (2011).
7
Feliu N, Fadeel B. Nanotoxicology: no small matter. Nanoscale 2(12), 2514–2520 (2010).
future science group
Editorial
It has been argued [12] that systems biology is destined to fail because one cannot solve the inverse problem of physiology, that is, deducing models of function from the behavior of a complex system (or, to put it another way: to build a radio that works from a detailed knowledge of all the individual components of the radio, and their interactions). However, while the fulfillment of the claims of ‘radical systems biology’ may still be beyond reach, developing predictive (and testable) models of toxicological effects of chemicals and nanomaterials will nonetheless represent a significant step forward. This, therefore, represents a grand challenge in nanosafety research. Acknowledgements The author would like to thank the members of the working group on systems biology in the EU nanosafety cluster (www. nanosafetycluster.eu) for useful discussions.
Financial & competing interests disclosure The author would like to thank the European Commission for funding (FP7-NANOSOLUTIONS; grant agreement number 309329). The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. 8
Pillai S, Behra R, Nestler H et al. Linking toxicity and adaptive responses across the transcriptome, proteome, and phenotype of Chlamydomonas reinhardtii exposed to silver. Proc. Natl Acad. Sci. USA 111(9), 3490–3495 (2014).
9
Donaldson K, Poland CA. Nanotoxicity: challenging the myth of nano-specific toxicity. Curr. Opin. Biotechnol. 24(4), 724–734 (2013).
10
Kodali V, Littke MH, Tilton SC et al. Dysregulation of macrophage activation profiles by engineered nanoparticles. ACS Nano 7(8), 6997–7010 (2013).
11
Feliu N, Kohonen P, Ji J et al. Next-generation sequencing reveals low-dose effects of cationic dendrimers in primary human bronchial epithelial cells. ACS Nano 9 (1), 146–163 (2015).
12
Brenner S. Sequences and consequences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365(1537), 207–212 (2010).
www.futuremedicine.com
1041
Systems biology in nanosafety research
“...a majority of studies published to date fall short of achieving a systems biology view (or model) of the perturbations inflicted by exposure to nanomaterials...” Keywords: computational biology • engineered nanomaterials • gene-expression profiling • nanosafety • systems biology
In his famous essay, Lazebnik [1] discussed whether a biologist can fix a broken radio and suggested that applying the ‘biologist approach’ to a broken radio will likely not lead to a successful end, while any engineer or even a trained repairman evidently could fix the radio. In biology, the study of molecules one by one is fine for gathering information on how molecules work, but deciphering biological mechanisms, or the function of biological systems, is a different story. One difference between an engineer and a biologist, according to Lazebnik, lies in the language: in biology, language is oftentimes vague and nonquantitative which limits the possibility of making predictions [1] . In systems biology, a quantitative and predictive language is adopted to describe biological knowledge in order to understand how molecules act together within the network of interactions that makes up a living system. Systems biology, in other words, is the holistic analysis of complex systems. Indeed, systems biology can be defined as the computational and mathematical modeling of complex biological systems, in contrast to the traditional, reductionist approach to biology. Common technology platforms that are deployed to obtain complex datasets are transcriptomics, proteomics, metabolomics and epigenomics. However, systems biology should not be seen merely as the generation of lists of genes, proteins or metabolites using such omics platforms; the objective is to exploit
10.2217/NNM.15.17 © 2015 Future Medicine Ltd
these data and to develop quantitative models that describe the biological system and its response to individual perturbations (see [2] for an excellent introduction to this subject). Furthermore, systems biology can be seen as an iterative interplay between discovery- and hypothesis-driven science: “global observations (discoveries) are matched against model predictions (hypotheses) in an iterative manner, leading to the formation of new models, new predictions, and new experiments to test them” [2] . Systems toxicology, in turn, has been described as “the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization”, and it has been suggested that its application could be part of a new and improved risk assessment [3] . This is nothing less than a paradigm shift in the way that toxicology is conducted. Importantly, systems toxicology – and systems biology in general – relies heavily on computational approaches to manage, analyze and interpret the data generated by large-scale experiments, and computer databases are thus an integral feature of this approach [2] . To this end, commonly accepted repositories and software environments are crucially important [4] . This does not mean that the introduction of systems toxicology approaches is breaking the rice bowl of the classically trained toxicologist. Instead, the ‘new’ toxicology is
Nanomedicine (Lond.) (2015) 10(7), 1039–1041
Bengt Fadeel Nanosafety & Nanomedicine Laboratory, Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, 171 77 Stockholm, Sweden bengt.fadeel@ ki.se
part of
ISSN 1743-5889
1039
Editorial Fadeel multidisciplinary, combining the expertise and principles of computer science, engineering, mathematics, physics and chemistry, with those of biology and toxicology [3] . Detailed knowledge of biology will always be required to make sense of the data. The purpose of this editorial is to give examples of emerging systems biology approaches for the assessment of nanomaterial effects in living organisms and to highlight some of the promises and challenges.
“
...systems biology can be defined as the computational and mathematical modeling of complex biological systems...
”
First, as discussed by Sturla et al., experimental transparency is one of the major requirements for omics experiments [3] . Indeed, standards have been developed for the ‘minimum information about a microarray experiment’ (MIAME) and the ‘minimum information about a proteomics experiment’ (MIAPE) and this has also become the standard for datasets deposited in public databases [3] . Do we need to invent new standards for omics experiments specifically in nanosafety research? The answer to this question is ‘yes’ and ‘no’. No, because it makes far more sense to adopt existing standards for data generation and data sharing in the omics and systems biology community. Yes, because there are still major deficiencies in the way that nanotoxicological experiments are designed [5] , and if the experiment is poorly designed or controlled, and/or if the nanomaterials that are under study are poorly characterized in terms of their physico-chemical properties, or the dispersions are poorly controlled, then the omics data that are generated will, naturally, be of poor quality too, regardless of the degree of sophistication of the omics technologies applied. Hence, it would seem appropriate for the global community of nanosafety researchers to devise a ‘minimum information about a nanotoxicology experiment’ (MIANE) standard, including guidelines on how to report the dose metric used, and how to record physico-chemical properties of the materials studied (and reference materials), that can be applied across different laboratories. It deserves to be noted that while nanomaterials may be categorized and regulated as chemicals, their behavior in, and interactions with, biological systems differs from that of chemicals; in fact, it has been argued that chemically well-defined nanoparticles are “an extension of the classical concept of the molecule” insofar as they combine the properties of solids – such as magnetism, or fluorescence – with mobility, a property of molecules [6] . When we surveyed the literature 5 years ago [7] , there were only scattered examples of gene or protein expres-
1040
Nanomedicine (Lond.) (2015) 10(7)
sion studies to address the toxicities of nanomaterials. Now, more and more studies are being published in which different omics technologies have been applied to study responses to nanoparticles, with a predominance of gene-expression profiling studies, but studies employing proteomics, epigenomics and metabolomics methodologies are also emerging; in some, but not all cases, phenotypic hypotheses generated through computational analysis of the data were tested. In fact, a majority of studies published to date fall short of achieving a systems biology view (or model) of the perturbations inflicted by exposure to nanomaterials, and appear mainly to catalog changes in gene or protein expression. For comparison, Pillai et al. have provided an illustrative example of a systems-based approach in toxicology in which transcriptome and proteome data are paired with physiological responses to silver, using the green algae Chlamydomonas reinhardtii as a model system [8] . The authors achieved a mechanistic understanding not only of the toxicity pathways elicited by exposure to Ag+, but also explored the adaptive response pathways, which may be equally as important for understanding the outcome of exposure to a toxicant. One may ask whether there are any ‘novel’ (i.e., unexpected) toxicities associated with nanomaterial exposure, and whether systems toxicology approaches can aid in unveiling such toxicities. Some researchers have argued that “the focus on the search for ‘nano-specific’ [toxicity] may have the effect of ‘re-inventing the wheel’ of what is already known for conventional particles” [9] , and suggested that “the final common pathways for pathological effects are entirely shared by both nano particles and conventional particles and no novel pathogenic pathways are anticipated” [9] . However, while the final outcome at the tissue or organ level (for instance, inflammation or cancer) may be shared with other toxicants, simply due to the fact that conserved signaling pathways are activated in response to diverse stimuli, it remains possible that the proximal events, such as translocation of nanoparticles across biological barriers, may be unusual or unanticipated or novel; indeed, as discussed before, nanoparticles, by virtue of their small size, may combine the properties of classical solids with those of molecules [6] . Furthermore, systems toxicology will surely have a place in nanosafety research insofar as omics-based approaches – combined with appropriate computational analysis of the data – may be applied to study more subtle effects not readily detected using conventional toxicological assays. Kodali et al. [10] reported that preincubation of primary murine bone marrow-derived macrophages with superparamagnetic iron oxide nanoparticles (SPIONs) that did not elicit acute cytotoxicity as measured by
future science group
Systems biology in nanosafety research
LDH leakage from these cells caused extensive transcriptional reprogramming in response to bacterial lipopolysaccharide challenge. Notably, macrophages exposed to SPIONs displayed diminished phagocytic activity toward the lung pathogen Streptococcus pneumoniae. The authors concluded that biological effects of engineered nanomaterials may not only occur through direct cytotoxicity, but may be indirectly manifested after challenging normal (immune) cell function [10] . Furthermore, we recently performed whole transcriptome sequencing of primary human bronchial epithelial cells exposed to poly(amidoamine) dendrimers, under conditions when these nanoparticles did not trigger cell death using conventional cell viability assays [11] . Importantly, the global geneexpression profiling approach coupled with detailed bioinformatics assessment revealed that all of the most significantly differentially expressed gene categories were related to cell cycle and cell division. In other words, we were able to define the predominant (transcriptional) effects of these nanoparticles and subsequent biological experiments validated these findings. In addition, using pathway analysis software, we identified NF-κB as a potential upstream regulator of gene expression, and this in silico-based prediction was borne out in cell-based assays [11] . Thus, we could validate hypotheses based on computational analysis of the transcriptomics data. References 1
Lazebnik Y. Can a biologist fix a radio? Or, what I learned while studying apoptosis. Cancer Cell 2(3), 179–182 (2002).
2
Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu. Rev. Genom. Hum. Genet. 2, 343–372 (2001).
3
Sturla SJ, Boobis AR, FitzGerald RE et al. Systems toxicology: from basic research to risk assessment. Chem. Res. Toxicol. 27(3), 314–329 (2014).
4
Kitano H. Computational systems biology. Nature 420(6912), 206–210 (2002).
5
Krug HF. Nanosafety research – are we on the right track? Angew. Chem. Int. Ed. Engl. 53(46), 12304–12319 (2014).
6
Stark WJ. Nanoparticles in biological systems. Angew. Chem. Int. Ed. Engl. 50(6), 1242–1258 (2011).
7
Feliu N, Fadeel B. Nanotoxicology: no small matter. Nanoscale 2(12), 2514–2520 (2010).
future science group
Editorial
It has been argued [12] that systems biology is destined to fail because one cannot solve the inverse problem of physiology, that is, deducing models of function from the behavior of a complex system (or, to put it another way: to build a radio that works from a detailed knowledge of all the individual components of the radio, and their interactions). However, while the fulfillment of the claims of ‘radical systems biology’ may still be beyond reach, developing predictive (and testable) models of toxicological effects of chemicals and nanomaterials will nonetheless represent a significant step forward. This, therefore, represents a grand challenge in nanosafety research. Acknowledgements The author would like to thank the members of the working group on systems biology in the EU nanosafety cluster (www. nanosafetycluster.eu) for useful discussions.
Financial & competing interests disclosure The author would like to thank the European Commission for funding (FP7-NANOSOLUTIONS; grant agreement number 309329). The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. 8
Pillai S, Behra R, Nestler H et al. Linking toxicity and adaptive responses across the transcriptome, proteome, and phenotype of Chlamydomonas reinhardtii exposed to silver. Proc. Natl Acad. Sci. USA 111(9), 3490–3495 (2014).
9
Donaldson K, Poland CA. Nanotoxicity: challenging the myth of nano-specific toxicity. Curr. Opin. Biotechnol. 24(4), 724–734 (2013).
10
Kodali V, Littke MH, Tilton SC et al. Dysregulation of macrophage activation profiles by engineered nanoparticles. ACS Nano 7(8), 6997–7010 (2013).
11
Feliu N, Kohonen P, Ji J et al. Next-generation sequencing reveals low-dose effects of cationic dendrimers in primary human bronchial epithelial cells. ACS Nano 9 (1), 146–163 (2015).
12
Brenner S. Sequences and consequences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365(1537), 207–212 (2010).
www.futuremedicine.com
1041
