- Email: [email protected]
Connecting the Dots
Commentary
Biological Psychiatry
Connecting the Dots Jyothi Arikkath and Károly Mirnics One of the most widely observed and robust findings in schizophrenia (SCZ) is altered connectivity between brain regions involved in cognition and emotion. In this issue, Romme et al. (1) provide a new look at the interaction between genes playing a role in SCZ pathophysiology and cortical disconnectivity observed in patients. Candidate genes and genome-wide association studies (GWASs) have identified .100 common genetic variants conferring increased risk for developing SCZ (2). To date, many studies have examined the pathological effects of individual genes and variants on SCZ-related phenotypes. However, our understanding of the mechanisms by which the various gene products of these diverse loci converge onto disrupted brain connectivity, information processing, and behavioral phenotypes remains limited. Romme et al. took advantage of the known expression levels of SCZ risk genes in the human brain and data obtained from diffusion-weighted magnetic resonance imaging in patients with SCZ to perform a cross-correlation study. Using this approach, they provide evidence that the cortical transcriptional profiles of SCZ-predisposing genes correlate with patterns of white matter (WM) disconnectivity observed in patients with SCZ (i.e., regions of the cortex with the highest expression levels of SCZ risk genes are similar to the areas that show the largest reductions in WM connectivity). This correlation was particularly strong for WM volume, but not WM microstructure. In addition, there was no correlation between the expression of the genes conferring risk to SCZ and cortical thinning, suggesting that the correlation between gene expression and WM disconnectivity is specific and not a secondary consequence of structural alterations caused by progression of the disease. The experimental design was clever and multifaceted. Romme et al. analyzed 3T magnetic resonance imaging datasets of 48 patients with SCZ and 43 matched healthy controls, and used streamline tractography to trace WM pathways. Connection strength of corticocortical pathways was identified in terms of number of interconnecting streamlines. In parallel, they took advantage of the open database Allen Human Brain Atlas resource (3), and computed the gene expression profile for systematically segmented, topographically distinct brain regions. Next, they calculated the SCZ risk gene expression for various functional groups of genes, defined by SCZ risk loci reported by GWAS (2) and a recent meta-analysis (4). These distinct datasets were cross-correlated and analyzed in multiple ways. They found a positive association between default SCZ risk gene expression (defined by multiple gene grouping approaches) and between-group differences in macroscale regional disconnectivity. Next, the authors examined a subset of 35 GWAS-derived SCZ risk genes that have
been classified into six functional gene classes. This analysis revealed that genes involved in calcium signaling showed the strongest correlation with the pattern of disconnectivity seen in the patient imaging studies. At this point, the researchers changed their approach and used the 20,737 Allen Human Brain Atlas–derived expression data in a different way: of the 20,737 genes, they defined and grouped the 100 and 200 most correlated transcripts. Analyzing the existing PubMed literature, they found that these most correlated genes were significantly enriched for genes involved in SCZ (and other psychiatric disorder) pathophysiology. Additional analyses of the 100 strongest correlating genes revealed significant overrepresentation in the biological processes of synaptic transmission, cell–cell signaling, and regulation of cell and protein localization. These studies support and extend data from other laboratories that implicate genes regulating neuronal migration, synaptic transmission, transport, transcription, and signaling in the pathophysiological cascade of SCZ (5,6). To confirm that their findings are specific to SCZ, the authors examined the expression profiles of the SCZ risk conferring genes and functional connectivity in individuals with bipolar disorder. This analysis revealed no significant correlation between the gene expression patterns and connectivity, suggesting at least some disease-specificity of the findings. Additional analyses also revealed there was an inverse association between the expression of bipolar risk genes and regional disconnectivity in individuals with bipolar disorder. The major strength of the Romme et al. study is that they succeeded in integrating data from different sources to generate a cohesive, minable data set that allowed identification of previously unknown relationships. This is clearly a good example of how data integration across different fields can result in a new level of understanding of the disorder. As with any interesting study, these new insights raise additional questions and identify new avenues for further investigation. First, the causality of the identified relationship between the altered WM connectivity and groups of genes remains somewhat unclear. It should be noted that this study shows WM disconnectivity associated with many genes primarily expressed by neurons. Thus, it is important to not overinterpret the findings. This research project does dissect the role of glial versus neuronal mechanisms in the causality of the disconnectivity: it is very likely that the WM disconnectivity (at least to a significant extent) is a neuron-driven (mal)developmental process. Second, the Romme et al. study nicely identifies a relationship between the disconnectivity seen in disease and molecular-genetic patterns relevant for developing SCZ, but their link to the developmental timeline of the pathophysiology remains unclear (7). Does the cumulative genetic liability lead
SEE CORRESPONDING ARTICLE ON PAGE 495
http://dx.doi.org/10.1016/j.biopsych.2016.12.013 ISSN: 0006-3223
463 & 2016 Society of Biological Psychiatry. Biological Psychiatry March 15, 2017; 81:463–464 www.sobp.org/journal
Biological Psychiatry
Commentary
to altered gene expression and disrupted early brain development, which in turn results in disconnectivity? Do individuals with the same genetic liability who do not get SCZ have the same disconnectivity as observed in patients with SCZ? If so, what is the difference that ultimately defines disease outcome? Do environmental insults occurring in critical time windows promote detrimental outcomes and lead to disease? Third, similar studies will also have to be performed using other gene expression data sources. Gene expression is not static: it continuously changes from conception to death. One could hypothesize that the human developmental gene expression in periods of particular vulnerability would show an even a higher correlation with cortical disconnectivity seen in SCZ. As a result, it seems essential to perform similar correlational studies using new and already existing gene expression datasets from human fetal tissue (8,9). Fourth, it is important to note the observed correlations are observed between WM disconnectivity of patients with SCZ and gene expression patterns of nonaffected individuals. An argument can be made that the correlation between connectivity and gene expression for the same genes will be even stronger if both datasets originate from the same diagnostic group of patient/subject population. Thus, performing the assessments between the cortical disconnectivity in patients and gene expression datasets that originate from actual subjects with SCZ seems to be a critical next step. Related studies using quite different approaches and methodologies have been already performed to date, and it will be informative to see how these datasets replicate each other. Fifth, it should be considered that the genes conferring genetic predisposition to SCZ and genes reporting expression changes observed in the brain of subjects with SCZ are only partially overlapping, and describe two different aspects of the disease process. Not all genes conferring SCZ susceptibility have altered gene expression patterns in the diseased brain of adults, and not all transcripts changed in the brain of subjects with SCZ are a result of genetic susceptibility. For example, reduction in GAD1/GAD67 in the postmortem brain is a consistent, well-replicated, and perhaps critical feature in SCZ (10); however, there is little evidence that GAD1 genetic variants alter risk to this disorder. This could have been perhaps better addressed in the study of Romme et al., which does not discuss this important distinction (genetic predisposition vs. gene expression). In summary, the glass is still both half-empty and half-full: we are becoming good at gathering data, but connecting and interpreting findings across disciplines remains a monumental
464
challenge. Studies are getting better at linking these “data islands,” but the number of unconnected dots is still staggering, and potentially endless. Perhaps we should choose the view that the glass is half-full, and enjoy reading this nice research study by Romme et al.
Acknowledgments and Disclosures The authors report no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Developmental Neuroscience (JA, KM), MunroeMeyer Institute for Genetics and Rehabilitation, Department of Psychiatry (KM), and the Department of Biochemistry and Molecular Biology (KM), College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska. Address correspondence to Károly Mirnics, M.D., Ph.D., University of Nebraska Medical Center, Munroe-Meyer Institute for Genetics and Rehabilitation, 444 South 44th Street, Room 2005 985450, Omaha, NE 68198; E-mail: [email protected]. Received Dec 14, 2016; accepted Dec 15, 2016.
References 1.
2.
3.
4. 5. 6.
7.
8.
9.
10.
Romme IAC, de Reus MA, Ophoff RA, Kahn RS, van den Heuvel MP (2017): Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biol Psychiatry 81:495–502. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014): Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427. Hawrylycz M, Lein E, Guillozet-Bongaarts A, Shen E, Ng L, Miller J, et al. (2012): An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489:391–399. Chen J, Cao F, Liu L, Wang L, Chen X (2015): Genetic studies of schizophrenia: An update. Neurosci Bull 31:87–98. Horváth S, Mirnics K (2015): Schizophrenia as a disorder of molecular pathways. Biol Psychiatry 77:22–28. Mirnics K, Middleton FA, Lewis DA, Levitt P (2001): Analysis of complex brain disorders with gene expression microarrays: Schizophrenia as a disease of the synapse. Trends Neurosci 24:479–486. Jaffe AE, Shin J, Collado-Torres L, Leek JT, Tao R, Li C, et al. (2015): Developmental regulation of human cortex transcription and its clinical relevance at single base resolution. Nat Neurosci 18:154–161. Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, et al. (2011): Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478:519–523. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. (2011): Spatio-temporal transcriptome of the human brain. Nature 478: 483–489. Schmidt MJ, Mirnics K (2015): Neurodevelopment, GABA system dysfunction, and schizophrenia. Neuropsychopharmacology 40: 190–206.
Biological Psychiatry March 15, 2017; 81:463–464 www.sobp.org/journal
Biological Psychiatry
Connecting the Dots Jyothi Arikkath and Károly Mirnics One of the most widely observed and robust findings in schizophrenia (SCZ) is altered connectivity between brain regions involved in cognition and emotion. In this issue, Romme et al. (1) provide a new look at the interaction between genes playing a role in SCZ pathophysiology and cortical disconnectivity observed in patients. Candidate genes and genome-wide association studies (GWASs) have identified .100 common genetic variants conferring increased risk for developing SCZ (2). To date, many studies have examined the pathological effects of individual genes and variants on SCZ-related phenotypes. However, our understanding of the mechanisms by which the various gene products of these diverse loci converge onto disrupted brain connectivity, information processing, and behavioral phenotypes remains limited. Romme et al. took advantage of the known expression levels of SCZ risk genes in the human brain and data obtained from diffusion-weighted magnetic resonance imaging in patients with SCZ to perform a cross-correlation study. Using this approach, they provide evidence that the cortical transcriptional profiles of SCZ-predisposing genes correlate with patterns of white matter (WM) disconnectivity observed in patients with SCZ (i.e., regions of the cortex with the highest expression levels of SCZ risk genes are similar to the areas that show the largest reductions in WM connectivity). This correlation was particularly strong for WM volume, but not WM microstructure. In addition, there was no correlation between the expression of the genes conferring risk to SCZ and cortical thinning, suggesting that the correlation between gene expression and WM disconnectivity is specific and not a secondary consequence of structural alterations caused by progression of the disease. The experimental design was clever and multifaceted. Romme et al. analyzed 3T magnetic resonance imaging datasets of 48 patients with SCZ and 43 matched healthy controls, and used streamline tractography to trace WM pathways. Connection strength of corticocortical pathways was identified in terms of number of interconnecting streamlines. In parallel, they took advantage of the open database Allen Human Brain Atlas resource (3), and computed the gene expression profile for systematically segmented, topographically distinct brain regions. Next, they calculated the SCZ risk gene expression for various functional groups of genes, defined by SCZ risk loci reported by GWAS (2) and a recent meta-analysis (4). These distinct datasets were cross-correlated and analyzed in multiple ways. They found a positive association between default SCZ risk gene expression (defined by multiple gene grouping approaches) and between-group differences in macroscale regional disconnectivity. Next, the authors examined a subset of 35 GWAS-derived SCZ risk genes that have
been classified into six functional gene classes. This analysis revealed that genes involved in calcium signaling showed the strongest correlation with the pattern of disconnectivity seen in the patient imaging studies. At this point, the researchers changed their approach and used the 20,737 Allen Human Brain Atlas–derived expression data in a different way: of the 20,737 genes, they defined and grouped the 100 and 200 most correlated transcripts. Analyzing the existing PubMed literature, they found that these most correlated genes were significantly enriched for genes involved in SCZ (and other psychiatric disorder) pathophysiology. Additional analyses of the 100 strongest correlating genes revealed significant overrepresentation in the biological processes of synaptic transmission, cell–cell signaling, and regulation of cell and protein localization. These studies support and extend data from other laboratories that implicate genes regulating neuronal migration, synaptic transmission, transport, transcription, and signaling in the pathophysiological cascade of SCZ (5,6). To confirm that their findings are specific to SCZ, the authors examined the expression profiles of the SCZ risk conferring genes and functional connectivity in individuals with bipolar disorder. This analysis revealed no significant correlation between the gene expression patterns and connectivity, suggesting at least some disease-specificity of the findings. Additional analyses also revealed there was an inverse association between the expression of bipolar risk genes and regional disconnectivity in individuals with bipolar disorder. The major strength of the Romme et al. study is that they succeeded in integrating data from different sources to generate a cohesive, minable data set that allowed identification of previously unknown relationships. This is clearly a good example of how data integration across different fields can result in a new level of understanding of the disorder. As with any interesting study, these new insights raise additional questions and identify new avenues for further investigation. First, the causality of the identified relationship between the altered WM connectivity and groups of genes remains somewhat unclear. It should be noted that this study shows WM disconnectivity associated with many genes primarily expressed by neurons. Thus, it is important to not overinterpret the findings. This research project does dissect the role of glial versus neuronal mechanisms in the causality of the disconnectivity: it is very likely that the WM disconnectivity (at least to a significant extent) is a neuron-driven (mal)developmental process. Second, the Romme et al. study nicely identifies a relationship between the disconnectivity seen in disease and molecular-genetic patterns relevant for developing SCZ, but their link to the developmental timeline of the pathophysiology remains unclear (7). Does the cumulative genetic liability lead
SEE CORRESPONDING ARTICLE ON PAGE 495
http://dx.doi.org/10.1016/j.biopsych.2016.12.013 ISSN: 0006-3223
463 & 2016 Society of Biological Psychiatry. Biological Psychiatry March 15, 2017; 81:463–464 www.sobp.org/journal
Biological Psychiatry
Commentary
to altered gene expression and disrupted early brain development, which in turn results in disconnectivity? Do individuals with the same genetic liability who do not get SCZ have the same disconnectivity as observed in patients with SCZ? If so, what is the difference that ultimately defines disease outcome? Do environmental insults occurring in critical time windows promote detrimental outcomes and lead to disease? Third, similar studies will also have to be performed using other gene expression data sources. Gene expression is not static: it continuously changes from conception to death. One could hypothesize that the human developmental gene expression in periods of particular vulnerability would show an even a higher correlation with cortical disconnectivity seen in SCZ. As a result, it seems essential to perform similar correlational studies using new and already existing gene expression datasets from human fetal tissue (8,9). Fourth, it is important to note the observed correlations are observed between WM disconnectivity of patients with SCZ and gene expression patterns of nonaffected individuals. An argument can be made that the correlation between connectivity and gene expression for the same genes will be even stronger if both datasets originate from the same diagnostic group of patient/subject population. Thus, performing the assessments between the cortical disconnectivity in patients and gene expression datasets that originate from actual subjects with SCZ seems to be a critical next step. Related studies using quite different approaches and methodologies have been already performed to date, and it will be informative to see how these datasets replicate each other. Fifth, it should be considered that the genes conferring genetic predisposition to SCZ and genes reporting expression changes observed in the brain of subjects with SCZ are only partially overlapping, and describe two different aspects of the disease process. Not all genes conferring SCZ susceptibility have altered gene expression patterns in the diseased brain of adults, and not all transcripts changed in the brain of subjects with SCZ are a result of genetic susceptibility. For example, reduction in GAD1/GAD67 in the postmortem brain is a consistent, well-replicated, and perhaps critical feature in SCZ (10); however, there is little evidence that GAD1 genetic variants alter risk to this disorder. This could have been perhaps better addressed in the study of Romme et al., which does not discuss this important distinction (genetic predisposition vs. gene expression). In summary, the glass is still both half-empty and half-full: we are becoming good at gathering data, but connecting and interpreting findings across disciplines remains a monumental
464
challenge. Studies are getting better at linking these “data islands,” but the number of unconnected dots is still staggering, and potentially endless. Perhaps we should choose the view that the glass is half-full, and enjoy reading this nice research study by Romme et al.
Acknowledgments and Disclosures The authors report no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Developmental Neuroscience (JA, KM), MunroeMeyer Institute for Genetics and Rehabilitation, Department of Psychiatry (KM), and the Department of Biochemistry and Molecular Biology (KM), College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska. Address correspondence to Károly Mirnics, M.D., Ph.D., University of Nebraska Medical Center, Munroe-Meyer Institute for Genetics and Rehabilitation, 444 South 44th Street, Room 2005 985450, Omaha, NE 68198; E-mail: [email protected]. Received Dec 14, 2016; accepted Dec 15, 2016.
References 1.
2.
3.
4. 5. 6.
7.
8.
9.
10.
Romme IAC, de Reus MA, Ophoff RA, Kahn RS, van den Heuvel MP (2017): Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biol Psychiatry 81:495–502. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014): Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427. Hawrylycz M, Lein E, Guillozet-Bongaarts A, Shen E, Ng L, Miller J, et al. (2012): An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489:391–399. Chen J, Cao F, Liu L, Wang L, Chen X (2015): Genetic studies of schizophrenia: An update. Neurosci Bull 31:87–98. Horváth S, Mirnics K (2015): Schizophrenia as a disorder of molecular pathways. Biol Psychiatry 77:22–28. Mirnics K, Middleton FA, Lewis DA, Levitt P (2001): Analysis of complex brain disorders with gene expression microarrays: Schizophrenia as a disease of the synapse. Trends Neurosci 24:479–486. Jaffe AE, Shin J, Collado-Torres L, Leek JT, Tao R, Li C, et al. (2015): Developmental regulation of human cortex transcription and its clinical relevance at single base resolution. Nat Neurosci 18:154–161. Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, et al. (2011): Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478:519–523. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. (2011): Spatio-temporal transcriptome of the human brain. Nature 478: 483–489. Schmidt MJ, Mirnics K (2015): Neurodevelopment, GABA system dysfunction, and schizophrenia. Neuropsychopharmacology 40: 190–206.
Biological Psychiatry March 15, 2017; 81:463–464 www.sobp.org/journal