With a heritability of 50 percent, schizophrenia is very clearly a disease of disturbed biology, but to dissect the biological contribution to its etiology, researchers need relevant, patient-derived cell models. Ideally, we need cell models that can tell us how schizophrenia cell biology leads to an altered brain. Induced pluripotent stem (iPS) cells are genetically engineered cells, from a patient's cells (e.g., fibroblasts), that resemble embryonic stem cells, that can be used to generate neurons. There is much excitement that they will be useful as models for many brain disorders and diseases. Two new papers in Molecular Psychiatry and Nature report on applying iPS cell technology to schizophrenia by generating iPS cells from patients with a DISC1 mutation (Chiang et al., 2011) and from patients selected with a high likelihood of a genetic component to disease (Brennand et al., 2011).

When specific genes are implicated, then animal models can provide breakthroughs by determining the cellular functions of the implicated genes and their mutations. Although...  Read more

With a heritability of 50 percent, schizophrenia is very clearly a disease of disturbed biology, but to dissect the biological contribution to its etiology, researchers need relevant, patient-derived cell models. Ideally, we need cell models that can tell us how schizophrenia cell biology leads to an altered brain. Induced pluripotent stem (iPS) cells are genetically engineered cells, from a patient's cells (e.g., fibroblasts), that resemble embryonic stem cells, that can be used to generate neurons. There is much excitement that they will be useful as models for many brain disorders and diseases. Two new papers in Molecular Psychiatry and Nature report on applying iPS cell technology to schizophrenia by generating iPS cells from patients with a DISC1 mutation (Chiang et al., 2011) and from patients selected with a high likelihood of a genetic component to disease (Brennand et al., 2011).

When specific genes are implicated, then animal models can provide breakthroughs by determining the cellular functions of the implicated genes and their mutations. Although schizophrenia lacks single commonly mutated genes of large effect, some candidate genes, such as DISC1, are being identified in some families. This is now a very hot area for research that is identifying the role of this gene at the cellular level and in animal models. As such candidate genes are identified and their functions are ascertained, it will be essential to demonstrate their direct relevance in schizophrenia through patient-derived cellular models. In this regard, a new tool has emerged in the recent letter to Molecular Psychiatry reporting the generation of induced pluripotent cells from two patients with DISC1 mutation (Chiang et al., 2011). This preliminary study did not report a disease-associated phenotype in these iPS cells.

A disease-associated phenotype is best identified by comparing iPS cells from patients and controls, as now demonstrated by Brennand et al. (2011). This work is a significant new contribution to the field because it has demonstrated differences in the biology of neurons derived from patients and controls. As proof of principle, they have identified differences in the way patient neurons branch (they have fewer branches) and connect with each other (they connect to fewer other neurons). Most importantly, the patient neurons had normal physiological properties. That is to say, their physiology was not different from controls. These are interesting and important distinctions that are a reassuring proof of principle for this model, suggesting that the etiology of schizophrenia derives from altered connectivity of neuronal circuits and not from basic neuronal functions. This fits with the postulated “neurodevelopmental hypothesis” of schizophrenia. Patient neurons also had decreased levels of synaptic proteins (PSD95, glutamate receptor), which is consistent with “synaptic hypotheses” of schizophrenia. These are early days yet, but this cell model already demonstrates how a relevant cell model can provide a path for unifying etiological hypotheses.

Another aim for developing cell models of schizophrenia is to use them for drug discovery. Patient-control differences in cell functions can be the basis for screening chemical compounds that ameliorate this difference. Here, too, Brennand et al. (2011) demonstrate proof of principle by showing that loxapine treatment of the patient neurons increased their connectivity towards control levels. Only loxapine, of five antipsychotic drugs tested, had this effect, but the results are a clear sign of the utility of such cells for drug screening to find new potential drug candidates.

These two papers are a great start to using iPS cells as models of schizophrenia.

References:

Chiang CH, Su1Y, Wen Z, Yoritomo N, Ross CA, Margolis RL, Song H, Ming G-I. (2011) Integration-free induced pluripotent stem cells derived from schizophrenia patients with a DISC1 mutation. Molecular Psychiatry advance online publication, 22 February 2011. Abstract

Brennand KJ, Simone A, Jou1 J, Gelboin-Burkhart C, Tran N, Sangar S, Li Y, Mu Y, Chen G, Yu D, McCarthy S, Sebat J, Gage FH (2011). Modeling schizophrenia using human induced pluripotent stem cells. Nature.

I fully appreciate the efforts of Brennand and colleagues as pioneers. Indeed, this is great work. Like any pioneering work, this paper will be both applauded and criticized. The strength of the paper is in providing ways for us to analyze iPS cells and derived neurons. The multifaceted approach taken in this study will be a great platform for many investigators.

Schizophrenia is, clinically, a very heterogeneous condition, but for the past several years, basic scientists have tended to oversimplify the disorder. It is also true that this trend makes the neurobiology of schizophrenia move productively forward in some ways. I believe that the new tools for studying the biology of schizophrenia, such as iPSC-derived neurons, will teach us how difficult it is to draw simplified pathways for the disorder. Nonetheless, some common pathway(s) may be identified in the future, I optimistically hope.

Based on the great experimental procedures that this paper provides, many other groups may need to address whether or not these data are reproducible or not in “general” cases of...  Read more

I fully appreciate the efforts of Brennand and colleagues as pioneers. Indeed, this is great work. Like any pioneering work, this paper will be both applauded and criticized. The strength of the paper is in providing ways for us to analyze iPS cells and derived neurons. The multifaceted approach taken in this study will be a great platform for many investigators.

Schizophrenia is, clinically, a very heterogeneous condition, but for the past several years, basic scientists have tended to oversimplify the disorder. It is also true that this trend makes the neurobiology of schizophrenia move productively forward in some ways. I believe that the new tools for studying the biology of schizophrenia, such as iPSC-derived neurons, will teach us how difficult it is to draw simplified pathways for the disorder. Nonetheless, some common pathway(s) may be identified in the future, I optimistically hope.

Based on the great experimental procedures that this paper provides, many other groups may need to address whether or not these data are reproducible or not in “general” cases of schizophrenia. In such studies, the most important issue is to examine detailed clinical information of the subjects in comparison with this study.

The gene Neuregulin 1 (NRG1) on chromosome 8p has been identified as one of the risk genes for schizophrenia. It is unclear how the DNA sequence variation linked to schizophrenia leads to abnormalities of mRNA expression. This would be important to know, in order to understand the downstream effects of the neuregulin gene on neuronal functioning in schizophrenia.

Law and colleagues explored this question in post-mortem specimens of the hippocampus of control subjects and patients with schizophrenia. This elegant study of the expression of four types of NRG1 mRNA (types I-IV) is exactly what we need to translate findings from the field of human genetics into the field of schizophrenia neuropathology. The findings are complex and cannot be translated easily into a model of neuregulin dysfunction in schizophrenia. I would like to highlight two findings.

First, the level of NRG1 type I mRNA expression was increased in the hippocampus of schizophrenia patients. This confirms an earlier study of NRG1 mRNA expression in schizophrenia. It remains to be seen how this change in...  Read more

The gene Neuregulin 1 (NRG1) on chromosome 8p has been identified as one of the risk genes for schizophrenia. It is unclear how the DNA sequence variation linked to schizophrenia leads to abnormalities of mRNA expression. This would be important to know, in order to understand the downstream effects of the neuregulin gene on neuronal functioning in schizophrenia.

Law and colleagues explored this question in post-mortem specimens of the hippocampus of control subjects and patients with schizophrenia. This elegant study of the expression of four types of NRG1 mRNA (types I-IV) is exactly what we need to translate findings from the field of human genetics into the field of schizophrenia neuropathology. The findings are complex and cannot be translated easily into a model of neuregulin dysfunction in schizophrenia. I would like to highlight two findings.

First, the level of NRG1 type I mRNA expression was increased in the hippocampus of schizophrenia patients. This confirms an earlier study of NRG1 mRNA expression in schizophrenia. It remains to be seen how this change in NRG1 type I mRNA expression relates to the finer details of neuregulin dysfunction in schizophrenia.

Second, one single nucleotide polymorphism (SNP8NRG243177) of the risk haplotype linked to schizophrenia in earlier studies predicts NRG1 type IV mRNA expression. The SNP determines a binding site for transcription factors, providing clues for how DNA sequence variation may lead, via modulation of mRNA expression, to neuronal dysfunction in schizophrenia. It is exciting to see that we can now test specific hypotheses of molecular mechanisms in the brains of patients who have suffered from schizophrenia. The study by Law et al. is an encouraging step in the right direction.

I think this is a very interesting and potentially significant paper. It is important to point out, however, that it deals with changes in mRNA abundance rather than alterations in neuregulin protein expression. No measures of isoform protein expression were performed, and it is conceivable that neuregulin isoform protein expression could be increased, decreased, or not changed. A second point is that although statistically significant changes in mRNA were measured, they are modest.

Finally, although multiple comparisons were performed, the authors chose not to perform Bonferroni corrections, noting in the primary paper that, "Correction for random effects, such as Bonferroni correction, would be an excessively conservative approach, particularly given that we have restricted our primary analyses to planned comparisons (based on strong prior clinical association and physical location of the SNPs) of four SNPs and a single haplotype comprised of these SNPs. Because the SNPs are in moderate LD, the degree of independence between markers is low and, therefore, correcting for...  Read more

I think this is a very interesting and potentially significant paper. It is important to point out, however, that it deals with changes in mRNA abundance rather than alterations in neuregulin protein expression. No measures of isoform protein expression were performed, and it is conceivable that neuregulin isoform protein expression could be increased, decreased, or not changed. A second point is that although statistically significant changes in mRNA were measured, they are modest.

Finally, although multiple comparisons were performed, the authors chose not to perform Bonferroni corrections, noting in the primary paper that, "Correction for random effects, such as Bonferroni correction, would be an excessively conservative approach, particularly given that we have restricted our primary analyses to planned comparisons (based on strong prior clinical association and physical location of the SNPs) of four SNPs and a single haplotype comprised of these SNPs. Because the SNPs are in moderate LD, the degree of independence between markers is low and, therefore, correcting for multiple testing would result in a high type II error rate. The prior probability and the predictable association between the deCODE haplotype and expression of NRG1 isoforms (especially type IV, which is its immediate physical neighbor) combined with the LD between SNPs in this haplotype makes statistical correction for these comparisons inappropriate. Nevertheless, our finding regarding type IV expression and the deCODE haplotype and SNP8NRG243177 requires independent replication."

It will thus be important to determine if these changes in neuregulin mRNA isoform abundance are mirrored by significant changes in neuregulin isoform protein expression and if the findings can be independently replicated with other cohorts.

Gilman et al. pose exceptionally important and salient questions: given that increasingly detailed genomic data have established that many genes are now strongly implicated in the etiology of schizophrenia, how do we understand this? How can these different components of the “parts list” for schizophrenia be pieced together to derive a cogent etiological hypothesis for further testing?

The authors use a new computational approach to address these questions, and derive lists related to axon guidance, neuronal cell mobility, synaptic function, and chromosomal remodeling. Additional analyses suggest the coherence of their lists. These are good clues that deserve further evaluation.

It was intriguing that the authors included multiple types of genetic variation—rare but potent copy number variants (e.g., Kirov et al., 2012), rare exonic mutations (Xu et al., 2012), and common variations from genomewide association studies (  Read more

Gilman et al. pose exceptionally important and salient questions: given that increasingly detailed genomic data have established that many genes are now strongly implicated in the etiology of schizophrenia, how do we understand this? How can these different components of the “parts list” for schizophrenia be pieced together to derive a cogent etiological hypothesis for further testing?

The authors use a new computational approach to address these questions, and derive lists related to axon guidance, neuronal cell mobility, synaptic function, and chromosomal remodeling. Additional analyses suggest the coherence of their lists. These are good clues that deserve further evaluation.

It was intriguing that the authors included multiple types of genetic variation—rare but potent copy number variants (e.g., Kirov et al., 2012), rare exonic mutations (Xu et al., 2012), and common variations from genomewide association studies (Ripke et al., 2011)—as most authors have tended to conduct these analyses separately.

In sum, a nice contribution to the literature and initial steps towards tackling a tough problem in human genetics. But, there are four issues for readers to bear in mind in evaluating the results.

First, we hope that the authors make their program freely available. This is the standard in the field. Many of us are interested in evaluating the capacities of their program. To our knowledge, it is not now available, although it has been used in multiple published papers. We could find no link in the paper or on the senior author’s lab page.

Second, readers need to remember that this was an in-silico analysis. It produces hypotheses but does not (and cannot) provide proof. The methods are subject to multiple biases, and it was not clear how well these were controlled (see point 4 as well). We wondered whether known biases like gene size and LD patterns were well controlled.

Third, we would have liked to see greater scholarship. There is an unfortunate trend for computational biologists to produce tools without benchmarking them against existing tools or rigorously determining power and error rates. The lack of finding significant clusters in control sets is insufficient in showing the validity of their program. Are the authors’ claims that their new tool represents superiority truly justified?

Moreover, there are a lot of tools for performing analyses of these sorts (e.g., INRICH, FORGE, MAGENTA, Ingenuity, ALIGATOR, among many others). Indeed, these sorts of analyses are in the toolkits of most psychiatric genetics groups and are routinely applied. Given that there are many papers reporting results, a scholarly treatment of how their results compare to those of others and what the added value of their program is would have been useful.

Fourth, and most importantly, pathway analysis is completely dependent on the input—the genetic findings and the pathways. The findings that the authors used had issues. The CNV list is likely to change soon as the PGC CNV group completes its integrated analyses of tens of thousands of subjects. The exome list was based on a small and atypical sample, and much larger studies are in preparation (see SRF comment). The authors did not seem to confront the issue that all humans contain a lot of deleterious exonic variation. And (spoiler alert), the GWAS list is soon to increase markedly. More and more precise findings are sure to alter the results.

The pathways used were pretty standard—GO, KEGG, protein-protein interaction databases. Unfortunately, although widely used, these pathways have multiple issues. The content of many GO annotations and KEGG pathways have not been constructed by experts in the area. As one salient example, synaptic gene lists in standard pathway databases were quite imperfectly related to lists created by experts (Ruano et al., 2010). The authors also relied somewhat uncritically on the PPI databases. These have multiple issues, and some (unpublished) data suggest substantial error (i.e., large fractions of the predicted interactions are not, in fact, real or biologically meaningful). The fraction of the proteome screened adequately by these methods is small. Some interactions in these databases are non-specific, or occur between molecules that are never in the same place at the same time.

Indeed, the genes overrepresented in PPI databases were selected due to disease relevance or biological importance (e.g., there is a lot of work on P53). In general, the more a gene is investigated, the more interactions are found.

Still, this is a key paper, albeit a snapshot based on imperfect input data, and we look forward to seeing whether additional analyses confirm a role in schizophrenia of the networks identified currently with their program.

References:

Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, Moran J, Chambert K, Toncheva D, Georgieva L, Grozeva D, Fjodorova M, Wollerton R, Rees E, Nikolov I, van de Lagemaat LN, Bayés A, Fernandez E, Olason PI, Böttcher Y, Komiyama NH, Collins MO, Choudhary J, Stefansson K, Stefansson H, Grant SG, Purcell S, Sklar P, O'Donovan MC, Owen MJ. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry. 2012 Feb; 17(2):142-53. Abstract

Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, Levy S, Gogos JA, Karayiorgou M. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet. 2012 Oct 3. Abstract

Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA, Scolnick E, Cichon S, St Clair D, Corvin A, Gurling H, Werge T, Rujescu D, Blackwood DH, Pato CN, Malhotra AK, Purcell S, Dudbridge F, Neale BM, Rossin L, Visscher PM, Posthuma D, Ruderfer DM, Fanous A, Stefansson H, Steinberg S, Mowry BJ, Golimbet V, de Hert M, Jönsson EG, Bitter I, Pietiläinen OP, Collier DA, Tosato S, Agartz I, Albus M, Alexander M, Amdur RL, Amin F, Bass N, Bergen SE, Black DW, Børglum AD, Brown MA, Bruggeman R, Buccola NG, Byerley WF, Cahn W, Cantor RM, Carr VJ, Catts SV, Choudhury K, Cloninger CR, Cormican P, Craddock N, Danoy PA, Datta S, de Haan L, Demontis D, Dikeos D, Djurovic S, Donnelly P, Donohoe G, Duong L, Dwyer S, Fink-Jensen A, Freedman R, Freimer NB, Friedl M, Georgieva L, Giegling I, Gill M, Glenthøj B, Godard S, Hamshere M, Hansen M, Hansen T, Hartmann AM, Henskens FA, Hougaard DM, Hultman CM, Ingason A, Jablensky AV, Jakobsen KD, Jay M, Jürgens G, Kahn RS, Keller MC, Kenis G, Kenny E, Kim Y, Kirov GK, Konnerth H, Konte B, Krabbendam L, Krasucki R, Lasseter VK, Laurent C, Lawrence J, Lencz T, Lerer FB, Liang KY, Lichtenstein P, Lieberman JA, Linszen DH, Lönnqvist J, Loughland CM, Maclean AW, Maher BS, Maier W, Mallet J, Malloy P, Mattheisen M, Mattingsdal M, McGhee KA, McGrath JJ, McIntosh A, McLean DE, McQuillin A, Melle I, Michie PT, Milanova V, Morris DW, Mors O, Mortensen PB, Moskvina V, Muglia P, Myin-Germeys I, Nertney DA, Nestadt G, Nielsen J, Nikolov I, Nordentoft M, Norton N, Nöthen MM, O'Dushlaine CT, Olincy A, Olsen L, O'Neill FA, Orntoft TF, Owen MJ, Pantelis C, Papadimitriou G, Pato MT, Peltonen L, Petursson H, Pickard B, Pimm J, Pulver AE, Puri V, Quested D, Quinn EM, Rasmussen HB, Réthelyi JM, Ribble R, Rietschel M, Riley BP, Ruggeri M, Schall U, Schulze TG, Schwab SG, Scott RJ, Shi J, Sigurdsson E, Silverman JM, Spencer CC, Stefansson K, Strange A, Strengman E, Stroup TS, Suvisaari J, Terenius L, Thirumalai S, Thygesen JH, Timm S, Toncheva D, van den Oord E, van Os J, van Winkel R, Veldink J, Walsh D, Wang AG, Wiersma D, Wildenauer DB, Williams HJ, Williams NM, Wormley B, Zammit S, Sullivan PF, O'Donovan MC, Daly MJ, Gejman PV. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011 Oct ; 43(10):969-76. Abstract

Ruano D, Abecasis GR, Glaser B, Lips ES, Cornelisse LN, de Jong AP, Evans DM, Davey Smith G, Timpson NJ, Smit AB, Heutink P, Verhage M, Posthuma D. Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. Am J Hum Genet. 2010 Feb 12;86(2):113-25. Abstract