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Sex-specific cortical, hippocampal and thalamic whole genome transcriptome data from controls and a G72 schizophrenia mouse model

Abstract

Objectives

The G72 mouse model of schizophrenia represents a well-known model that was generated to meet the main translational criteria of isomorphism, homology and predictability of schizophrenia to a maximum extent. In order to get a more detailed view of the complex etiopathogenesis of schizophrenia, whole genome transcriptome studies turn out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex, hippocampus and thalamus of G72 transgenic and wild-type control mice. Experimental animals were age-matched and importantly, both sexes were considered separately.

Data description

The isolated RNA from all three brain regions was purified, quantified und quality controlled before initiation of the hybridization procedure with SurePrint G3 Mouse Gene Expression v2 8  ×  60 K microarrays. Following immunofluorescent measurement und preprocessing of image data, raw transcriptome data from G72 mice and control animals were extracted and uploaded in a public database. Our data allow insight into significant alterations in gene transcript levels in G72 mice and enable the reader/user to perform further complex analyses to identify potential age-, sex- and brain-region-specific alterations in transcription profiles and related pathways. The latter could facilitate biomarker identification and drug research and development in schizophrenia research.

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Objective

G72/G30 is a primate specific gene that is localized on human chromosome 13q and turned out to be a susceptibility locus for major psychiatric disorders such as schizophrenia and bipolar disorder [1,2,3]. The dominant gene product of this locus is the largest G72 splice variant, i.e., LG72 protein (here referred to as G72 protein). Notably, the function of the G72 protein in neuropsychiatry remains obscure [4]. Interestingly, increased G72 protein levels were observed in the serum and CNS of schizophrenic patients [5, 6]. In order to investigate the functional implications of the G72 protein in vivo, a humanized transgenic mouse model carrying the G72/G30 locus has been generated that expresses the G72 protein [7]. G72 transgenic mice serve as a schizophrenia model as they display characteristic symptoms, such as motor coordination deficits, altered sensorimotor gating and olfactory discrimination, increased compulsive behavior and spatial memory impairment [7, 8].

Previous complementary proteomics studies from the cerebellum of G72 mice revealed altered protein expression in mitochondria-associated, myelin- and oxidative stress-related processes in G72 compared to control mice [8,9,10,11]. Recently, Filiou et al. (2022) performed multi-omics approaches, i.e., quantitative proteomics and metabolomics, from the hippocampus of male, eight weeks old G72 vs. control mice [10]. However, we are still lacking detailed information about both sex- and brain-region specific differences in transcriptome profiles from schizophrenia mouse models such as G72. To fill this essential gap, we carried out microarray analysis of RNA from the retrosplenial (RS) cortex, hippocampus and thalamus of both female and male G72 mice. It has been shown in the past that the RS cortex is essential for gating information to the medial temporal lobe and displays aberrant connectivity with neuronal networks associated with memory formation and ecphoria, i.e., the limbic system/hippocampal formation in schizophrenia patients [12]. In addition, enhanced neuronal connectivity of the RS cortex with the left superior temporal gyrus was observed in patients with more positive symptoms, e.g. hallucinations [12]. Impaired function of the RS cortex was also related to verbal memory deficits commonly seen in schizophrenia patients [13]. These and other findings suggest that the RS cortex is of central importance in schizophrenia symptomatology [12, 14]. The hippocampus as a memory consolidation and formation system has been related to schizophrenia for long. The etiopathological evolution of schizophrenia via the premorbid, prodromal to syndromal psychotic stages was shown to initiate with dysregulation of glutamate transmission in the CA1 area, further progressing to other hippocampal regions and cortical areas as well [15, 16]. The thalamus as part of the thalamocortical-corticothalamic circuitry serves as another important structure in schizophrenia. It’s critical for the transmission and processing of external information and thereby modulates essential tasks such as wakefulness, sleep and memory. It was shown in both animal models and humans that there are thalamic connectivity deficits in psychiatric disorders, including schizophrenia [17]. There is also evidence of altered thalamic microstructure, e.g., in the mediodorsal nucleus, thalamo-prefrontal and thalamo-somatosensory/parietal connectivity [17]. Multiple studies have proposed an association between psychosis spectrum disorders and thalamic network dysrhythmia and/or dysconnectivity [18, 19]. Our microarray data from these three highly relevant brain regions allow for further characterization of differentially expressed intersectional and signature genes of the individual subgroups, gene ontology and pathway analysis and biophysical studies [20, 21] which are of relevance for future translational studies.

Data description

Experimental animals

G72 transgenic mice carrying the G72/G30 locus and wild-type (WT) mice with a CD1 background were kindly provided by the Institute of Molecular Psychiatry (Life & Brain, Bonn, Germany). Details on breeding and genetics of G72 mice have been described in detail before [7, 8]. In total, eight WT control animals (four ♂, age: 23.14 ± 0.00 wks; four ♀, age: 23.46 ± 0.11 wks) and eight G72 transgenic mice (four ♂, age: 23.14 ± 0.00 wks; four ♀, age: 23.14 ± 0.00 wks) were used for dissection of hippocampus, RS cortex and thalamus for subsequent transcriptome analysis.

Genotyping - DNA preparation from tail biopsies

Every experimental animal was genotyped twice using DNA isolated from tail biopsies. DNA preparation was carried out using peqGOLD DNA Mini Kit (PEQLAB Biotechnologie GmbH, Germany) according to the manufacturer’s instructions. The isolated genomic DNA was stored at + 4 °C until further use.

Hippocampus, RS cortex and thalamus preparation and tissue storage

Experimental animals were anaesthetized using i.p. injection of ketamine (100 mg/kg) / xylazine (10 mg/kg) and immediately decapitated. The brain was removed and placed in a clean RNase-free petri-dish filled with pure RNAlater reagent (Qiagen GmbH, Germany). The RS cortex, hippocampus and thalamus were bluntly dissected and immediately placed in 2 ml RNase free reaction tubes, snap frozen in liquid nitrogen and stored at -80 °C until RNA preparation. This instantaneous and fast processing was performed to eliminate potential effects of anaesthesia on early gene regulation.

Retrosplenial cortex, hippocampal and thalamic RNA isolation

Total RNA from the individual brain regions was extracted using RNeasy Lipid Tissue Mini Kit (Qiagen GmbH, Germany) according to the manufacturer’s protocol. Quantity and quality of the RNA was evaluated using NanoDrop ND-1000 (ThermoFisher Scientific Inc., USA).

Acquisition of transcriptome data and raw data extraction

Transcriptome data were acquired using the One-Color Microarray-Based Gene Expression system (Agilent Technologies Germany GmbH & Co. KG, Germany). In specific, the SurePrint G3 Mouse Gene Expression v2 8 × 60 K Microarray Kit (Agilent Technologies Germany GmbH & Co. KG, Germany) was used for RS cortex, hippocampal and thalamic tissue. All procedures were carried out according to the manufacturer’s instructions.

Raw data are based on fluorescence scanning using the Agilent SureScan Microarray Scanner and raw microarray image file processing using the Feature Extraction Software (both from Agilent Technologies Germany GmbH & Co. KG, Germany). Using GeneSpring Software (Agilent Technologies Germany GmbH & Co. KG, Germany), all information about probe names, fold changes, etc. were extracted and exported into txt.- and csv.-files to allow usage in other transcriptome analysis software in case GeneSpring software is not used and/or not available. These raw data represent unfiltered, non pre-analyzed data.

The raw data (date files 1–47) as well as the two MAGE-TAB format files (data files 48–49) were uploaded to the ArrayExpress repository and are freely accessible with the following accession ID: E-MTAB-13547. The reader might also use the related identifiers.org link, i.e., “https://identifiers.org/arrayexpress:E-MTAB-13547” [12].

The individual subgroups and related data are characterized in Table 1.

Table 1 Overview of data files/data sets (for quality control files of the individual data files see supplementary information)

Limitations

This transcriptome data collection from the RS cortex, hippocampus and thalamus of  6 months old G72 and control mice was carried out using a microarray approach. Following scientific and organizational aspects, resource considerations, and power calculations, this turned out to be a feasible approach for us. We are aware that RNAseq is also a very good alternative strategy to characterize transcriptional alterations in the G72 schizophrenia mouse line.

The G72 mouse model is a unique schizophrenia model to characterize the impact of the G72 protein in neuropsychiatry [7, 8]. This may limit its generalizability compared to other schizophrenia models as regards isomorphism, homology and predictability representing the major translational categories in biomedicine. As for other diseases, numerous schizophrenia mouse models have been generated and none of these models can claim to mimic the etiopathogenesis, symptomatology and drug delivery characteristics of the human disease equivalent to a 100% [22,23,24]. Given these observations, searching a schizophrenia model with common trans-line and trans-species transcriptional profiles is highly challenging. We have thus decided to use the humanized transgenic mouse model G72 carrying the G72/G30 locus which has received much attention in literature, and which can provide novel insight into the crucial role of G72 protein in the etiopathogenesis of schizophrenia.

A unique feature of our data is related to their sex- and brain region-related specificity. Clearly, this results in a high number of experimental subgroups (twelve in total) and microarrays to be used. Note that we focused on animals of “older” age (i.e., 23 wks) in this data collection as stereotypic behavior, characterized by repetitive and unintentional movements typically appears in progressed disease stages [25,26,27,28]. To get an impression of temporal effects on G72 transcriptome profiles, additional studies with G72 and control animals of different ages are necessary.

Data availability

The data described in this Data note (data files 1–47 in total; files #1–3: female G72 cortex (n = 3); files #4–7: female G72 hippocampus (n = 4); files #8–11: female G72 thalamus (n = 4); files # 12–15: female WT cortex (n = 4); files #16–19: female WT hippocampus (n = 4); files #20–23: female WT thalamus (n = 4); files #24–27: male G72 cortex (n = 4); files #28–31: male G72 hippocampus (n = 4); files #32–35: male G72 thalamus (n = 4); files #36–39: male WT cortex (n = 4); files #40–43: male WT hippocampus (n = 4); files #44–47: male WT thalamus (n = 4); and the files 48 and 49 (metadata in the MAGE-TAB format) can be freely and openly accessed in the ArrayExpress repository using the following accession ID: E-MTAB-13547. The reader might also use the related identifiers.org link, i.e., “https://identifiers.org/arrayexpress: E-MTAB-13547” [12].

Abbreviations

RS:

Retrosplenial

WT:

Wild-type

References

  1. Chumakov I, Blumenfeld M, Guerassimenko O, Cavarec L, Palicio M, Abderrahim H, Bougueleret L, Barry C, Tanaka H, La Rosa P, et al. Genetic and physiological data implicating the new human gene G72 and the gene for D-amino acid oxidase in schizophrenia. Proc Natl Acad Sci U S A. 2002;99(21):13675–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Detera-Wadleigh SD, McMahon FJ. G72/G30 in schizophrenia and bipolar disorder: review and meta-analysis. Biol Psychiatry. 2006;60(2):106–14.

    Article  CAS  PubMed  Google Scholar 

  3. Jansen A, Krach S, Krug A, Markov V, Eggermann T, Zerres K, Thimm M, Nothen MM, Treutlein J, Rietschel M, et al. Effect of the G72 (DAOA) putative risk haplotype on cognitive functions in healthy subjects. BMC Psychiatry. 2009;9:60.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sacchi S, Binelli G, Pollegioni L. G72 primate-specific gene: a still enigmatic element in psychiatric disorders. Cell Mol Life Sci. 2016;73(10):2029–39.

    Article  CAS  PubMed  Google Scholar 

  5. Akyol ES, Albayrak Y, Aksoy N, Sahin B, Beyazyuz M, Kuloglu M, Hashimoto K. Increased serum G72 protein levels in patients with schizophrenia: a potential candidate biomarker. Acta Neuropsychiatr. 2017;29(2):80–6.

    Article  PubMed  Google Scholar 

  6. Korostishevsky M, Kaganovich M, Cholostoy A, Ashkenazi M, Ratner Y, Dahary D, Bernstein J, Bening-Abu-Shach U, Ben-Asher E, Lancet D, et al. Is the G72/G30 locus associated with schizophrenia? Single nucleotide polymorphisms, haplotypes, and gene expression analysis. Biol Psychiatry. 2004;56(3):169–76.

    Article  CAS  PubMed  Google Scholar 

  7. Otte DM, Bilkei-Gorzo A, Filiou MD, Turck CW, Yilmaz O, Holst MI, Schilling K, Abou-Jamra R, Schumacher J, Benzel I, et al. Behavioral changes in G72/G30 transgenic mice. Eur Neuropsychopharmacol. 2009;19(5):339–48.

    Article  CAS  PubMed  Google Scholar 

  8. Otte DM, Sommersberg B, Kudin A, Guerrero C, Albayram O, Filiou MD, Frisch P, Yilmaz O, Drews E, Turck CW, et al. N-acetyl cysteine treatment rescues cognitive deficits induced by mitochondrial dysfunction in G72/G30 transgenic mice. Neuropsychopharmacology. 2011;36(11):2233–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Filiou MD, Martins-de-Souza D, Guest PC, Bahn S, Turck CW. To label or not to label: applications of quantitative proteomics in neuroscience research. Proteomics. 2012;12(4–5):736–47.

    Article  CAS  PubMed  Google Scholar 

  10. Filiou MD, Teplytska L, Nussbaumer M, Otte DM, Zimmer A, Turck CW. Multi-omics Analysis reveals myelin, presynaptic and nicotinate alterations in the Hippocampus of G72/G30 transgenic mice. J Pers Med 2022, 12(2).

  11. Filiou MD, Turck CW. Psychiatric Disorder biomarker discovery using quantitative proteomics. Methods Mol Biol. 2012;829:531–9.

    Article  CAS  PubMed  Google Scholar 

  12. Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld RW, Theberge J, Schaefer B, Williamson PC. Retrosplenial cortex connectivity in schizophrenia. Psychiatry Res. 2009;174(1):17–23.

    Article  PubMed  Google Scholar 

  13. Tendolkar I, Weis S, Guddat O, Fernandez G, Brockhaus-Dumke A, Specht K, Klosterkotter J, Reul J, Ruhrmann S. Evidence for a dysfunctional retrosplenial cortex in patients with schizophrenia: a functional magnetic resonance imaging study with a semantic-perceptual contrast. Neurosci Lett. 2004;369(1):4–8.

    Article  CAS  PubMed  Google Scholar 

  14. Rolls ET. Attractor cortical neurodynamics, schizophrenia, and depression. Transl Psychiatry. 2021;11(1):215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lieberman JA, Girgis RR, Brucato G, Moore H, Provenzano F, Kegeles L, Javitt D, Kantrowitz J, Wall MM, Corcoran CM, et al. Hippocampal dysfunction in the pathophysiology of schizophrenia: a selective review and hypothesis for early detection and intervention. Mol Psychiatry. 2018;23(8):1764–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wegrzyn D, Juckel G, Faissner A. Structural and functional deviations of the Hippocampus in Schizophrenia and Schizophrenia Animal models. Int J Mol Sci 2022, 23(10).

  17. Hwang WJ, Kwak YB, Cho KIK, Lee TY, Oh H, Ha M, Kim M, Kwon JS. Thalamic connectivity System Across Psychiatric disorders: current status and clinical implications. Biol Psychiatry Glob Open Sci. 2022;2(4):332–40.

    Article  PubMed  Google Scholar 

  18. Anticevic A, Halassa MM. The thalamus in psychosis spectrum disorder. Front Neurosci. 2023;17:1163600.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Angulo Salavarria MM, Dell’Amico C, D’Agostino A, Conti L, Onorati M. Cortico-thalamic development and disease: from cells, to circuits, to schizophrenia. Front Neuroanat. 2023;17:1130797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Panda D, Saha P, Chaudhuri R, Prasanth T, Ravichandiran V, Dash J. A competitive pull-down assay using G-quadruplex DNA linked magnetic nanoparticles to determine specificity of G-quadruplex ligands. Anal Chem. 2019;91(12):7705–11.

    Article  CAS  PubMed  Google Scholar 

  21. Chaudhuri R, Prasanth T, Dash J. Expanding the Toolbox of Target Directed Bio-orthogonal Synthesis: in situ direct macrocyclization by DNA templates. Angew Chem Int Ed Engl. 2023;62(7):e202215245.

    Article  CAS  PubMed  Google Scholar 

  22. Jones CA, Watson DJ, Fone KC. Animal models of schizophrenia. Br J Pharmacol. 2011;164(4):1162–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Winship IR, Dursun SM, Baker GB, Balista PA, Kandratavicius L, Maia-de-Oliveira JP, Hallak J, Howland JG. An overview of animal models related to Schizophrenia. Can J Psychiatry. 2019;64(1):5–17.

    Article  PubMed  Google Scholar 

  24. Bialon M, Wasik A. Advantages and limitations of Animal Schizophrenia models. Int J Mol Sci 2022, 23(11).

  25. Ridley RM. The psychology of perserverative and stereotyped behaviour. Prog Neurobiol. 1994;44(2):221–31.

    Article  CAS  PubMed  Google Scholar 

  26. Cheng L, Hattori E, Nakajima A, Woehrle NS, Opal MD, Zhang C, Grennan K, Dulawa SC, Tang YP, Gershon ES, et al. Expression of the G72/G30 gene in transgenic mice induces behavioral changes. Mol Psychiatry. 2014;19(2):175–83.

    Article  CAS  PubMed  Google Scholar 

  27. Morrens M, Hulstijn W, Lewi PJ, De Hert M, Sabbe BG. Stereotypy in schizophrenia. Schizophr Res. 2006;84(2–3):397–404.

    Article  PubMed  Google Scholar 

  28. Garner JP, Meehan CL, Mench JA. Stereotypies in caged parrots, schizophrenia and autism: evidence for a common mechanism. Behav Brain Res. 2003;145(1–2):125–34.

    Article  PubMed  Google Scholar 

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Acknowledgments

The authors are grateful to Dr. Christina Kolb (German Center for Neurodegenerative Diseases, DZNE) and Dr. Robert Stark (DZNE) for assistance in animal breeding and animal health care.

Funding

Open Access funding enabled and organized by Projekt DEAL. This work was internally funded by the Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM, Bonn, Germany).

Open Access funding enabled and organized by Projekt DEAL.

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Authors and Affiliations

Authors

Contributions

A.P., C.H., J.D., T.S., A.S, M.W.: Conceptualization, Methodology, Software. M.W.: Data curation, Writing - Original draft preparation. C.H., A.P., M.W.: Visualization, Investigation. M.W.: Supervision. C.H., A.P., J.D., T.S., M.W.: Software, Validation. C.H., A.P., J.D., S.W., K.B., J.H., D.E., C.S., B.H., A.S., M.W.: Writing - Reviewing and Editing.

Corresponding author

Correspondence to Marco Weiergräber.

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Ethics approval and consent to participate

All animal procedures were carried out in accordance with the Guidelines of the German Council on Animal Care and all protocols were approved by the Local Institutional and National Committee on Animal Care (Landesamt für Natur, Umwelt und Verbraucherschutz, LANUV, Germany). The authors further certify that all animal experimentation complied with the ARRIVE guidelines and were carried out in accordance with the U.K. Animals (Scientific Procedures) Act, 1986 and associated guidelines; EU Directive 2010/63/EU for animal experiments; or the National Institutes of Health guide for the care and use of laboratory animals (NIH Publications No. 8023, revised 1978). Maximum effort was made to reduce the number of animals necessary to obtain data and suffering of the animals according to the 3R strategy.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Papazoglou, A., Henseler, C., Weickhardt, S. et al. Sex-specific cortical, hippocampal and thalamic whole genome transcriptome data from controls and a G72 schizophrenia mouse model. BMC Res Notes 17, 143 (2024). https://doi.org/10.1186/s13104-024-06799-4

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