Researchers Model Susceptibility to Schizophrenia in a Petri Dish
13 April 2011. Neurons have been successfully grown from induced pluripotent stem cells (iPSCs) derived from people with schizophrenia, according to a study in Nature appearing online 13 April 2011. Fred Gage of the Salk Institute in La Jolla, California, and colleagues report that, though these neurons resembled those from people without schizophrenia in many ways, they had deficits in forming connections with other neurons, and exhibited differences in gene expression—the researchers highlighted cAMP and Wnt pathway genes—when compared to control neurons. The connectivity and some gene expression aberrations could be "normalized" by treating the neurons with the antipsychotic loxapine.
The study adds schizophrenia to the few diseases that have so far been modeled using iPSCs derived from actual patients. With techniques to reprogram adult, readily obtainable tissue like skin cells into iPSCs, researchers can now try to generate cell types of interest from patient populations to better simulate at a cellular level what may be happening in a particular disease, even in a specific person. While recent studies of patient-specific iPSCs have focused on single gene disorders such as Rett’s syndrome (e.g., Marchetto et al., 2010), the new study marks one of the first attempts to study a disorder with heterogeneous genetic origins.
A brief report in Molecular Psychiatry in February described iPSCs derived from schizophrenia patients (Chiang et al., 2011), but the new study starts to get a handle on features potentially related to disease, comparing the connectivity patterns, synaptic markers, physiology, and gene expression profiles of neurons grown from iPSCs derived from control and schizophrenia subjects.
First author Kristen Brennand and colleagues started with fibroblast samples from four schizophrenia patients: one with childhood onset of the disorder, and three others with an affected parent. Control samples came from age- and ancestry-matched individuals with normal psychiatric evaluations. The fibroblasts were transformed into iPSCs using a lentivirus to introduce genes that reprogrammed the cells into a pluripotent state. The iPSCs were then differentiated into neural precursor cells, and then neurons, over the course of three months. Most turned out to express VGLUT, a marker of glutamatergic cells, about 30 percent expressed GABAergic neuron markers, and less than 10 percent were positive for tyrosine hydroxylase, an enzyme required to make dopamine.
Interconnected neurons derived from induced pluripotent stem cells (iPSCs) from schizophrenia patients. hiPSC neurons are shown expressing the neuronal proteins Beta-III-tubulin (red) and MAP2AB (green). Nuclei are stained with DAPI (blue). Magnification is 20x. Image credit: Kristen Brennand, Salk Institute for Biological Studies
When grown with astrocytes in the dish, the neurons formed connections with each other. Using a modified rabies virus to trace the number of direct inputs received by a given neuron (Wickersham et al., 2007), the researchers measured a decrease in connectivity, with schizophrenia-derived neurons receiving inputs from about half the number of neurons as controls did. Treatment with a variety of antipsychotic agents with affinity for both dopamine and serotonin receptors did not affect connectivity, with one exception: adding loxapine, which targets both dopamine and serotonin receptors about equally (Kapur et al., 1997), to the dish for three weeks boosted connectivity in the schizophrenia neurons.
The researchers also measured slightly fewer neurites, the processes destined to become dendrites or axons, in the schizophrenia hiPSC neurons compared to controls—something the authors compare to the reduced dendritic arborizations found in postmortem brain. The neurons from individuals with schizophrenia also had less staining for PSD95—a protein involved in anchoring proteins at glutamatergic synapses—than control neurons did.
These changes did not seem to compromise synaptic function, however. The researchers report that the schizophrenia hiPSC neurons exhibited normal action potentials, spontaneous excitatory and inhibitory synaptic activity, and spontaneous calcium signals. This overall picture of decreased connectivity with normal synaptic function runs counter to the synaptopathic view of schizophrenia and other disorders (Südhof, 2008) in which the number of synapses is postulated to remain normal, but synaptic function is compromised. The authors suggest that further analysis may, in fact, reveal some functional differences in the neurons derived from individuals with schizophrenia.
With gene expression microarrays, the researchers detected deviations in expression of 596 genes in the schizophrenia neurons that were at least 1.3 times greater or less than the level found in controls. Of these genes, 25 percent had been previously linked to schizophrenia, either through genetic association or postmortem studies. The authors write that gene ontology analysis of the altered expression highlighted glutamate receptor genes, and cAMP and Wnt pathway genes. Other schizophrenia-related genes, including NRG1 and ANK3, had significantly elevated expression in schizophrenia-derived neurons compared to controls. Interestingly, the NRG1 increase was detected only in neurons, and not in fibroblasts or iPSCs from the schizophrenia patients, which argues that it is critical to look at the cell type relevant to a disease. Further study with qPCR verified patterns of altered expression for these and other schizophrenia suspects, and loxapine treatment usually boosted expression of these genes.
However, patients varied in their patterns of gene expression, which may reflect differences in the underlying genetic component contributing to each individual's schizophrenia. To address this, the researchers analyzed copy number variations (CNVs)—losses or gains of segments of DNA—which have been reported to substantially increase risk of schizophrenia (Walsh et al., 2008). They found 42 genes affected by CNVs among their four patients, none of which occurred at regions where CNVs have been previously associated with schizophrenia. Strikingly, only 12 of the genes affected by CNVs showed changes in neuronal expression that correlated with whether a copy of a gene was lost or gained. This suggests that compensatory mechanisms could be at work in these neurons, and indicates that neurons grown from iPSCs may deliver a reality check for ideas gleaned from human CNV studies, which often spur animal models based on an observed deletion or duplication of a particular gene.
Some of the results echo reported pathophysiology in schizophrenia; for example, NRG1 expression was elevated in the neurons grown from iPSCs derived from schizophrenia patients, similar to the increased levels found in postmortem brain tissue (see SRF related news story). Other results suggest new avenues of research, such as finding altered expression in genes related to axon guidance and NOTCH signaling (interestingly, NOTCH4 currently has a positive meta-analysis in SZGene).
A stem cell watershed
Despite the heterogeneous genetic risk factors likely at work in this small patient sample, it is interesting that some consistent results—such as the decrease in connectivity—were obtained. In fact, the authors predict that a narrower, more consistent pattern of expression changes affecting a smaller number of genes will emerge as the number of individuals with schizophrenia studied with iPSCs increases. This is consistent with a "watershed" model that proposes that a vast variety of gene malfunctions could contribute to schizophrenia by converging on the same key biological pathways.
The study marks the beginning of an era of stem cell research of schizophrenia. Future work will refine the description of these neurons and delineate how drugs may change them, and researchers will have to grapple with the interpretation of any results coming from the schizophrenia-derived neurons that happen to resemble, or diverge from, alterations noted in the brains of people with schizophrenia.—Michele Solis.
Brennand KJ, Simone A, Jou J, Gelboin-Burkhart C, Tran N, Sangar S, Li Y, Mu Y, Chen G, Yu D, McCarthy S, Sebat J, Gage FH. Modelling schizophrenia using human induced pluripotent stem cells. Nature. 2011 April 13.
Comments on News and Primary Papers
Comment by: Alan Mackay-Sim
Submitted 13 April 2011
Posted 13 April 2011
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
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.
View all comments by Alan Mackay-SimComment by: Akira Sawa, SRF Advisor
Submitted 13 April 2011
Posted 13 April 2011
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.
View all comments by Akira Sawa
Comments on Related News
Related News: Polymorphisms and Schizophrenia—The Ups and Downs of Neuregulin ExpressionComment by: William Carpenter, SRF Advisor
Submitted 22 April 2006
Posted 22 April 2006
I recommend the Primary PapersRelated News: Polymorphisms and Schizophrenia—The Ups and Downs of Neuregulin ExpressionComment by: Stephan Heckers, SRF Advisor
Submitted 29 April 2006
Posted 29 April 2006
I recommend the Primary Papers
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.
View all comments by Stephan Heckers
Related News: Polymorphisms and Schizophrenia—The Ups and Downs of Neuregulin Expression
Comment by: Bryan Roth, SRF Advisor
Submitted 5 May 2006
Posted 5 May 2006
I recommend the Primary Papers
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.
View all comments by Bryan Roth
Related News: Polymorphisms and Schizophrenia—The Ups and Downs of Neuregulin Expression
Comment by: Patricia Estani
Submitted 9 June 2007
Posted 10 June 2007
I recommend the Primary Papers
Related News: Deciphering Themes for Schizophrenia’s Genetic Variation
Comment by: Patrick Sullivan, SRF Advisor, Danielle Posthuma
Submitted 16 November 2012
Posted 16 November 2012
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.
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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
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