Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion
Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a u...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S105381192100793X |
| id |
doaj-a3cfe72b0eaf4f28926de64b968c9513 |
|---|---|
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Article |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Natalia Gass Zeru Peterson Jonathan Reinwald Alexander Sartorius Wolfgang Weber-Fahr Markus Sack Junfang Chen Han Cao Michael Didriksen Tine Bryan Stensbøl Gabrielle Klemme Adam J. Schwarz Emanuel Schwarz Andreas Meyer-Lindenberg Thomas Nickl-Jockschat |
| spellingShingle |
Natalia Gass Zeru Peterson Jonathan Reinwald Alexander Sartorius Wolfgang Weber-Fahr Markus Sack Junfang Chen Han Cao Michael Didriksen Tine Bryan Stensbøl Gabrielle Klemme Adam J. Schwarz Emanuel Schwarz Andreas Meyer-Lindenberg Thomas Nickl-Jockschat Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion NeuroImage 22q11.2 deletion Schizophrenia Comt Trmt2a Functional connectivity Mouse |
| author_facet |
Natalia Gass Zeru Peterson Jonathan Reinwald Alexander Sartorius Wolfgang Weber-Fahr Markus Sack Junfang Chen Han Cao Michael Didriksen Tine Bryan Stensbøl Gabrielle Klemme Adam J. Schwarz Emanuel Schwarz Andreas Meyer-Lindenberg Thomas Nickl-Jockschat |
| author_sort |
Natalia Gass |
| title |
Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion |
| title_short |
Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion |
| title_full |
Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion |
| title_fullStr |
Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion |
| title_full_unstemmed |
Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion |
| title_sort |
differential resting-state patterns across networks are spatially associated with comt and trmt2a gene expression patterns in a mouse model of 22q11.2 deletion |
| publisher |
Elsevier |
| series |
NeuroImage |
| issn |
1095-9572 |
| publishDate |
2021-11-01 |
| description |
Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a, were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level. |
| topic |
22q11.2 deletion Schizophrenia Comt Trmt2a Functional connectivity Mouse |
| url |
http://www.sciencedirect.com/science/article/pii/S105381192100793X |
| work_keys_str_mv |
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doaj-a3cfe72b0eaf4f28926de64b968c95132021-10-05T04:18:50ZengElsevierNeuroImage1095-95722021-11-01243118520Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletionNatalia Gass0Zeru Peterson1Jonathan Reinwald2Alexander Sartorius3Wolfgang Weber-Fahr4Markus Sack5Junfang Chen6Han Cao7Michael Didriksen8Tine Bryan Stensbøl9Gabrielle Klemme10Adam J. Schwarz11Emanuel Schwarz12Andreas Meyer-Lindenberg13Thomas Nickl-Jockschat14Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USADepartment of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyLundbeck A/S, Copenhagen, DenmarkLundbeck A/S, Copenhagen, DenmarkDepartment of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USATakeda Pharmaceuticals, Cambridge, MA, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USADepartment of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, GermanyDepartment of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Corresponding author.Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a, were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level.http://www.sciencedirect.com/science/article/pii/S105381192100793X22q11.2 deletionSchizophreniaComtTrmt2aFunctional connectivityMouse |