ARTHRITIS & RHEUMATOLOGY Vol. 66, No. 6, June 2014, pp 1418–1420 DOI 10.1002/art.38629 © 2014, American College of Rheumatology
EDITORIAL
The Power of a Modular Approach to Transcriptional Analysis Peter K. Gregersen and Michaela Oswald In this issue of Arthritis & Rheumatology, Chiche and colleagues provide additional insight into the complexity of interferon (IFN) signatures in the peripheral blood of lupus patients (1). Their data illustrate the power of combining correlated gene expression information with biologic associations, in this case based on empirically derived modules of gene expression and their post hoc annotations. These methods are beginning to provide more user-friendly approaches to analyzing and communicating results for monitoring immune function in the context of disease, vaccination, and therapy. Over the last decade, the development of increasingly comprehensive platforms for global analysis of messenger RNA (mRNA) expression at reasonable cost has spawned a huge number of studies that utilize gene expression profiling to understand human disease. A PubMed search for studies using gene expression profiling in humans yields tens of thousands of results, including more than 300 publications on systemic lupus erythematosus (SLE) and 500 publications on rheumatoid arthritis. A substantial fraction of these studies were carried out on mixed cell populations, most commonly peripheral blood mononuclear cells (PBMCs) or whole blood. Early studies documented the importance of fresh preparation of RNA, since thousands of genes can change expression in PBMCs after just a few hours and even more so after overnight shipping (2). This problem has been solved by the use of blood collections using PAXgene or Tempus tubes that immediately stabilize RNA. Nevertheless, the computational challenges of making sense of large-scale analog data related to mRNA transcript levels are substantial. A variety of exploratory statistical approaches to large-scale data have been employed, which are gener-
ally agnostic with regard to the biologic functions of the genes being analyzed. As a result, one of the biggest problems in the field has been the lack of validated results. Small sample sizes, together with statistical noise and an inability to combine data across experiments or laboratories because of standardization problems (batch effects), have led to isolated reports of results which could not later be reproduced. Studies of rheumatic diseases in particular have been plagued by this problem. However, an exception to this is the peripheral blood IFN signature in lupus, which has been well replicated in numerous studies since the first observations more than a decade ago (3–5). In 2008, Chaussabel et al reported on an approach to gene expression analysis that was based on an empirical examination of how groups of genes behave across a variety of disease conditions in PBMCs (6). Expression profiling was carried out on 239 patients with 1 of 8 disorders: juvenile rheumatoid arthritis, systemic lupus, type 1 diabetes mellitus, metastatic melanoma, Escherichia coli infection, Staphylococcus aureus infection, influenza A infection, and acute liver transplant rejection. These disorders all involve some degree of inflammation or immune stimulation, and thus, it was reasonable to expect some reflection of these states in the circulating peripheral blood cells. Remarkably, the authors observed clear “modules” of gene expression, most commonly involving hundreds of transcripts moving in a common pattern across samples from the 8 data sets. Upon inspection of the genes in these modular groupings, many modules could be reasonably designated as belonging to known cell types, such as B cells, T cells, myeloid lineage, etc., as well as to known pathways, such as IFN-inducible genes or ribosome- or major histocompatibility complex–related genes. Many modules did not lend themselves to easy categorization and were thus designated “undetermined”; a total of 28 distinct modules were identified. Not surprisingly, the IFN-associated module was strongly up-regulated in the pediatric lupus patients in that study (6). The study by Chiche et al takes advantage of an
Peter K. Gregersen, MD, Michaela Oswald, PhD: Feinstein Institute for Medical Research, Manhasset, New York. Address correspondence to Peter K. Gregersen, MD, Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY 11030. E-mail: [email protected]. Submitted for publication February 24, 2014; accepted in revised form March 11, 2014. 1418
EDITORIAL
extension of the original modular approach, again based on empirical analysis of groups of genes across multiple inflammatory conditions, leading to the identification of 260 expression modules, as previously described (7). Three distinct IFN-related modules were identified, designated M1.2, M3.4, and M5.12. By further investigation of published data, these modules could be broadly related to the effects of type I (either IFN␣ or IFN) or type II (IFN␥) IFNs. This was made possible in part by detailed information on gene expression in the context of treatment of hepatitis C infection with IFN␣ (8) and treatment of multiple sclerosis with IFN (9), from which data are publicly available. This was supplemented with information from in vitro studies, also in the public domain and compiled in an online annotated database of IFN-regulated genes (10): http://interferome. its.monash.edu.au/interferome/home.jspx. Of the 3 IFN modules, module M1.2 predominantly reflects the effects of type I IFN, while modules M3.4 and M5.12 can be induced by both type I and type II IFNs. IFN was generally more effective than IFN␣ for the induction of M1.2 and M3.4, while module M5.12 was dependent on IFN␥ in addition to type I IFNs. Importantly, the full-blown SLE IFN signature involving all 3 modules could not be reproduced by exposure to type I IFNs (IFN␣ and/or IFN) alone, implying that elevated IFN␥ is a significant contributor to the “interferon signature” in lupus. Even more strikingly, modules M1.2, M3.4, and M5.12 exhibited an ordered appearance, depending on the extent and intensity of the IFN expression phenotype. Even within patients with quiescent disease, ⬎85% exhibited an elevation of IFN modules, with all of these subjects being positive for M1.2, and this was relatively stable over time regardless of disease activity. M3.4 was not observed in the absence of M1.2, while M5.12 was only observed when both M1.2 and M3.4 were also present, occurring in 18% of these patients with quiescent disease. Interestingly, the presence of M5.12 (meaning that all modules are present) was modestly correlated with renal disease and flare. This might imply contribution by tissue-infiltrating T cells, the major cellular source of IFN␥, which appears to play a role in the M5.12 signature. Despite early promise, the various approaches to measuring the IFN signature have not provided a clinically useful biomarker for disease management (11,12). The current data suggest that with a more granular and focused approach, the different types of IFN signature may in fact have some clinical utility. In addition to the study of lupus, this modular approach to peripheral blood gene expression analysis
1419
is increasingly being used to examine the state of the immune system in other clinical contexts. Modular analysis of transcription appears to have utility in the diagnosis and management of tuberculosis and sarcoidosis, which have patterns that distinguish them from other pulmonary disorders (13). We have recently applied these modules to examine the response to TNF blockade in rheumatoid arthritis, with highly consistent changes over time in clinical responders but not in nonresponders (14). A number of studies of vaccine response suggest particular utility for understanding the human immune response at an individual level. For example, a longitudinal analysis of these modules by Obermoser et al has demonstrated distinct patterns of immune response in the context of pneumococcal and influenza vaccines, with early appearance of IFN signatures reflecting stimulation of the innate system, followed by a clear signature of plasmablasts at 7 days in the case of immunization using pneumococcal polysaccharides (7). In the case of influenza immunization, other investigators have demonstrated that early patterns of gene expression can predict the humoral response (15). Online resources for modular vaccine analysis are being developed (16) and may drive a more sophisticated approach to vaccine utilization and development (17). Compared to the early days of peripheral gene expression analysis, this is impressive progress. This has been dependent on the creativity and commitment of the investigators involved, but it is also important to emphasize the role of open data sharing in this process. Without the data resources that have been developed and shared over the years, progress would not be so rapid. Indeed, it might not even occur at all, since the relevant data sets are often large and expensive to generate. Another aspect of this is the commitment of the authors of this article and others to making data available in a format that is flexible and easy to understand by immunologists and biologists who do not have advanced computational skills. Many other sophisticated approaches to defining biologic systems are emerging, from single-cell transcriptional analysis to high-dimensional tracking of transcription, phenotype, and function in single cells (18). A major stimulus to future progress will be the ability of clinical investigators to understand and explore these types of data and to think of new ways in which these resources can be applied to their clinical problem. Analysis of data in a way that takes advantage of biologic information has been the driver of this progress, but creative visualization and flexible exploration of results are equally im-
1420
GREGERSEN AND OSWALD
portant to ensure that the full promise of systems biology is achieved. 9.
AUTHOR CONTRIBUTIONS Drs. Gregersen and Oswald drafted the article, revised it critically for important intellectual content, and approved the final version to be published.
REFERENCES 1. Chiche L, Jourde-Chiche N, Whalen E, Presnell S, Gersuk V, Dang K, et al. Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis Rheumatol 2014;66: 1583–95. 2. Gregersen PK, Behrens TW. Fine mapping the phenotype in autoimmune disease: the promise and pitfalls of DNA microarray technologies. Genes Immun 2003;4:175–6. 3. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 2003;197:711–23. 4. Crow MK, Kirou KA, Wohlgemuth J. Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 2003;36:481–90. 5. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003;100:2610–5. 6. Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 2008;29: 150–64. 7. Obermoser G, Presnell S, Domico K, Xu H, Wang Y, Anguiano E, et al. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity 2013;38:831–44. 8. Taylor MW, Tsukahara T, McClintick JN, Edenberg HJ, Kwo P. Cyclic changes in gene expression induced by Peg-interferon
10.
11.
12.
13.
14.
15.
16.
17. 18.
alfa-2b plus ribavirin in peripheral blood monocytes (PBMC) of hepatitis C patients during the first 10 weeks of treatment. J Transl Med 2008;6:66. Malhotra S, Bustamante MF, Perez-Miralles F, Rio J, Ruiz de Villa MC, Vegas E, et al. Search for specific biomarkers of IFN bioactivity in patients with multiple sclerosis. PLoS One 2011;6: e23634. Rusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res 2013;41:D1040–6. Petri M, Singh S, Tesfasyone H, Dedrick R, Fry K, Lal P, et al. Longitudinal expression of type I interferon responsive genes in systemic lupus erythematosus. Lupus 2009;18:980–9. Landolt-Marticorena C, Bonventi G, Lubovich A, Ferguson C, Unnithan T, Su J, et al. Lack of association between the interferon-␣ signature and longitudinal changes in disease activity in systemic lupus erythematosus. Ann Rheum Dis 2009;68:1440–6. Bloom CI, Graham CM, Berry MP, Rozakeas F, Redford PS, Wang Y, et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PLoS One 2013;8:e70630. Curran M, Oswald M, Lamberth S, Brodmerkel C, Cherkas Z, Gregersen PK. Modular analysis of in peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in TNF responders [abstract]. Arthritis Rheum 2013;65 Suppl:S58. Bucasas KL, Franco LM, Shaw CA, Bray MS, Wells JM, Nino D, et al. Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J Infect Dis 2011;203:921–9. Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol 2014;15:195–204. Haining WN. Strength in numbers: comparing vaccine signatures the modular way. Nat Immunol 2014;15:139–41. Chattopadhyay PK, Gierahn TM, Roederer M, Love JC. Singlecell technologies for monitoring immune systems. Nat Immunol 2014;15:128–35.
EDITORIAL
The Power of a Modular Approach to Transcriptional Analysis Peter K. Gregersen and Michaela Oswald In this issue of Arthritis & Rheumatology, Chiche and colleagues provide additional insight into the complexity of interferon (IFN) signatures in the peripheral blood of lupus patients (1). Their data illustrate the power of combining correlated gene expression information with biologic associations, in this case based on empirically derived modules of gene expression and their post hoc annotations. These methods are beginning to provide more user-friendly approaches to analyzing and communicating results for monitoring immune function in the context of disease, vaccination, and therapy. Over the last decade, the development of increasingly comprehensive platforms for global analysis of messenger RNA (mRNA) expression at reasonable cost has spawned a huge number of studies that utilize gene expression profiling to understand human disease. A PubMed search for studies using gene expression profiling in humans yields tens of thousands of results, including more than 300 publications on systemic lupus erythematosus (SLE) and 500 publications on rheumatoid arthritis. A substantial fraction of these studies were carried out on mixed cell populations, most commonly peripheral blood mononuclear cells (PBMCs) or whole blood. Early studies documented the importance of fresh preparation of RNA, since thousands of genes can change expression in PBMCs after just a few hours and even more so after overnight shipping (2). This problem has been solved by the use of blood collections using PAXgene or Tempus tubes that immediately stabilize RNA. Nevertheless, the computational challenges of making sense of large-scale analog data related to mRNA transcript levels are substantial. A variety of exploratory statistical approaches to large-scale data have been employed, which are gener-
ally agnostic with regard to the biologic functions of the genes being analyzed. As a result, one of the biggest problems in the field has been the lack of validated results. Small sample sizes, together with statistical noise and an inability to combine data across experiments or laboratories because of standardization problems (batch effects), have led to isolated reports of results which could not later be reproduced. Studies of rheumatic diseases in particular have been plagued by this problem. However, an exception to this is the peripheral blood IFN signature in lupus, which has been well replicated in numerous studies since the first observations more than a decade ago (3–5). In 2008, Chaussabel et al reported on an approach to gene expression analysis that was based on an empirical examination of how groups of genes behave across a variety of disease conditions in PBMCs (6). Expression profiling was carried out on 239 patients with 1 of 8 disorders: juvenile rheumatoid arthritis, systemic lupus, type 1 diabetes mellitus, metastatic melanoma, Escherichia coli infection, Staphylococcus aureus infection, influenza A infection, and acute liver transplant rejection. These disorders all involve some degree of inflammation or immune stimulation, and thus, it was reasonable to expect some reflection of these states in the circulating peripheral blood cells. Remarkably, the authors observed clear “modules” of gene expression, most commonly involving hundreds of transcripts moving in a common pattern across samples from the 8 data sets. Upon inspection of the genes in these modular groupings, many modules could be reasonably designated as belonging to known cell types, such as B cells, T cells, myeloid lineage, etc., as well as to known pathways, such as IFN-inducible genes or ribosome- or major histocompatibility complex–related genes. Many modules did not lend themselves to easy categorization and were thus designated “undetermined”; a total of 28 distinct modules were identified. Not surprisingly, the IFN-associated module was strongly up-regulated in the pediatric lupus patients in that study (6). The study by Chiche et al takes advantage of an
Peter K. Gregersen, MD, Michaela Oswald, PhD: Feinstein Institute for Medical Research, Manhasset, New York. Address correspondence to Peter K. Gregersen, MD, Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY 11030. E-mail: [email protected]. Submitted for publication February 24, 2014; accepted in revised form March 11, 2014. 1418
EDITORIAL
extension of the original modular approach, again based on empirical analysis of groups of genes across multiple inflammatory conditions, leading to the identification of 260 expression modules, as previously described (7). Three distinct IFN-related modules were identified, designated M1.2, M3.4, and M5.12. By further investigation of published data, these modules could be broadly related to the effects of type I (either IFN␣ or IFN) or type II (IFN␥) IFNs. This was made possible in part by detailed information on gene expression in the context of treatment of hepatitis C infection with IFN␣ (8) and treatment of multiple sclerosis with IFN (9), from which data are publicly available. This was supplemented with information from in vitro studies, also in the public domain and compiled in an online annotated database of IFN-regulated genes (10): http://interferome. its.monash.edu.au/interferome/home.jspx. Of the 3 IFN modules, module M1.2 predominantly reflects the effects of type I IFN, while modules M3.4 and M5.12 can be induced by both type I and type II IFNs. IFN was generally more effective than IFN␣ for the induction of M1.2 and M3.4, while module M5.12 was dependent on IFN␥ in addition to type I IFNs. Importantly, the full-blown SLE IFN signature involving all 3 modules could not be reproduced by exposure to type I IFNs (IFN␣ and/or IFN) alone, implying that elevated IFN␥ is a significant contributor to the “interferon signature” in lupus. Even more strikingly, modules M1.2, M3.4, and M5.12 exhibited an ordered appearance, depending on the extent and intensity of the IFN expression phenotype. Even within patients with quiescent disease, ⬎85% exhibited an elevation of IFN modules, with all of these subjects being positive for M1.2, and this was relatively stable over time regardless of disease activity. M3.4 was not observed in the absence of M1.2, while M5.12 was only observed when both M1.2 and M3.4 were also present, occurring in 18% of these patients with quiescent disease. Interestingly, the presence of M5.12 (meaning that all modules are present) was modestly correlated with renal disease and flare. This might imply contribution by tissue-infiltrating T cells, the major cellular source of IFN␥, which appears to play a role in the M5.12 signature. Despite early promise, the various approaches to measuring the IFN signature have not provided a clinically useful biomarker for disease management (11,12). The current data suggest that with a more granular and focused approach, the different types of IFN signature may in fact have some clinical utility. In addition to the study of lupus, this modular approach to peripheral blood gene expression analysis
1419
is increasingly being used to examine the state of the immune system in other clinical contexts. Modular analysis of transcription appears to have utility in the diagnosis and management of tuberculosis and sarcoidosis, which have patterns that distinguish them from other pulmonary disorders (13). We have recently applied these modules to examine the response to TNF blockade in rheumatoid arthritis, with highly consistent changes over time in clinical responders but not in nonresponders (14). A number of studies of vaccine response suggest particular utility for understanding the human immune response at an individual level. For example, a longitudinal analysis of these modules by Obermoser et al has demonstrated distinct patterns of immune response in the context of pneumococcal and influenza vaccines, with early appearance of IFN signatures reflecting stimulation of the innate system, followed by a clear signature of plasmablasts at 7 days in the case of immunization using pneumococcal polysaccharides (7). In the case of influenza immunization, other investigators have demonstrated that early patterns of gene expression can predict the humoral response (15). Online resources for modular vaccine analysis are being developed (16) and may drive a more sophisticated approach to vaccine utilization and development (17). Compared to the early days of peripheral gene expression analysis, this is impressive progress. This has been dependent on the creativity and commitment of the investigators involved, but it is also important to emphasize the role of open data sharing in this process. Without the data resources that have been developed and shared over the years, progress would not be so rapid. Indeed, it might not even occur at all, since the relevant data sets are often large and expensive to generate. Another aspect of this is the commitment of the authors of this article and others to making data available in a format that is flexible and easy to understand by immunologists and biologists who do not have advanced computational skills. Many other sophisticated approaches to defining biologic systems are emerging, from single-cell transcriptional analysis to high-dimensional tracking of transcription, phenotype, and function in single cells (18). A major stimulus to future progress will be the ability of clinical investigators to understand and explore these types of data and to think of new ways in which these resources can be applied to their clinical problem. Analysis of data in a way that takes advantage of biologic information has been the driver of this progress, but creative visualization and flexible exploration of results are equally im-
1420
GREGERSEN AND OSWALD
portant to ensure that the full promise of systems biology is achieved. 9.
AUTHOR CONTRIBUTIONS Drs. Gregersen and Oswald drafted the article, revised it critically for important intellectual content, and approved the final version to be published.
REFERENCES 1. Chiche L, Jourde-Chiche N, Whalen E, Presnell S, Gersuk V, Dang K, et al. Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis Rheumatol 2014;66: 1583–95. 2. Gregersen PK, Behrens TW. Fine mapping the phenotype in autoimmune disease: the promise and pitfalls of DNA microarray technologies. Genes Immun 2003;4:175–6. 3. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 2003;197:711–23. 4. Crow MK, Kirou KA, Wohlgemuth J. Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 2003;36:481–90. 5. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003;100:2610–5. 6. Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 2008;29: 150–64. 7. Obermoser G, Presnell S, Domico K, Xu H, Wang Y, Anguiano E, et al. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity 2013;38:831–44. 8. Taylor MW, Tsukahara T, McClintick JN, Edenberg HJ, Kwo P. Cyclic changes in gene expression induced by Peg-interferon
10.
11.
12.
13.
14.
15.
16.
17. 18.
alfa-2b plus ribavirin in peripheral blood monocytes (PBMC) of hepatitis C patients during the first 10 weeks of treatment. J Transl Med 2008;6:66. Malhotra S, Bustamante MF, Perez-Miralles F, Rio J, Ruiz de Villa MC, Vegas E, et al. Search for specific biomarkers of IFN bioactivity in patients with multiple sclerosis. PLoS One 2011;6: e23634. Rusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res 2013;41:D1040–6. Petri M, Singh S, Tesfasyone H, Dedrick R, Fry K, Lal P, et al. Longitudinal expression of type I interferon responsive genes in systemic lupus erythematosus. Lupus 2009;18:980–9. Landolt-Marticorena C, Bonventi G, Lubovich A, Ferguson C, Unnithan T, Su J, et al. Lack of association between the interferon-␣ signature and longitudinal changes in disease activity in systemic lupus erythematosus. Ann Rheum Dis 2009;68:1440–6. Bloom CI, Graham CM, Berry MP, Rozakeas F, Redford PS, Wang Y, et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PLoS One 2013;8:e70630. Curran M, Oswald M, Lamberth S, Brodmerkel C, Cherkas Z, Gregersen PK. Modular analysis of in peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in TNF responders [abstract]. Arthritis Rheum 2013;65 Suppl:S58. Bucasas KL, Franco LM, Shaw CA, Bray MS, Wells JM, Nino D, et al. Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J Infect Dis 2011;203:921–9. Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol 2014;15:195–204. Haining WN. Strength in numbers: comparing vaccine signatures the modular way. Nat Immunol 2014;15:139–41. Chattopadhyay PK, Gierahn TM, Roederer M, Love JC. Singlecell technologies for monitoring immune systems. Nat Immunol 2014;15:128–35.
