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Blanchard MW, Sigmon JS, Brennan J, Ahulamibe C, Allen ME, Baric RS, Bell TA, Farrington J, Ciavatta D, Cruz Cisneros M, Drushal M, Ferris MT, Fry R, Gaines C, Gu B, Heise MT, Hodges RA, Kafri T, Lynch R, Magnuson T, Miller D, Murphy CEY, Nguyen DT, Noll KE, Proulx M, Sassetti C, Shaw GD, Simon JM, Smith C, Styblo M, Tarantino L, Woo J, Pardo Manuel de Villena F. The Updated Mouse Universal Genotyping Array Bioinformatic Pipeline Improves Genetic QC in Laboratory Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.29.582794. [PMID: 38464063 PMCID: PMC10925293 DOI: 10.1101/2024.02.29.582794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The MiniMUGA genotyping array is a popular tool for genetic QC of laboratory mice and genotyping of samples from most types of experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve array's performance and the associated report's usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA in 2020. Here we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for inbred strains and increase the number of constructs that can reliably be detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have made changes to the layout of the report, to simplify the interpretation and completeness of the analysis and added a table summarizing the ideogram. We believe that these changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research.
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Abrantes A, Giusti-Rodriguez P, Ancalade N, Sekle S, Basiri ML, Stuber GD, Sullivan PF, Hultman R. Gene expression changes following chronic antipsychotic exposure in single cells from mouse striatum. Mol Psychiatry 2022; 27:2803-2812. [PMID: 35322200 DOI: 10.1038/s41380-022-01509-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 02/10/2022] [Accepted: 02/23/2022] [Indexed: 11/09/2022]
Abstract
Schizophrenia is an idiopathic psychiatric disorder with a high degree of polygenicity. Evidence from genetics, single-cell transcriptomics, and pharmacological studies suggest an important, but untested, overlap between genes involved in the etiology of schizophrenia and the cellular mechanisms of action of antipsychotics. To directly compare genes with antipsychotic-induced differential expression to genes involved in schizophrenia, we applied single-cell RNA-sequencing to striatal samples from male C57BL/6 J mice chronically exposed to a typical antipsychotic (haloperidol), an atypical antipsychotic (olanzapine), or placebo. We identified differentially expressed genes in three cell populations identified from the single-cell RNA-sequencing (medium spiny neurons [MSNs], microglia, and astrocytes) and applied multiple analysis pipelines to contextualize these findings, including comparison to GWAS results for schizophrenia. In MSNs in particular, differential expression analysis showed that there was a larger share of differentially expressed genes (DEGs) from mice treated with olanzapine compared with haloperidol. DEGs were enriched in loci implicated by genetic studies of schizophrenia, and we highlighted nine genes with convergent evidence. Pathway analyses of gene expression in MSNs highlighted neuron/synapse development, alternative splicing, and mitochondrial function as particularly engaged by antipsychotics. In microglia, we identified pathways involved in microglial activation and inflammation as part of the antipsychotic response. In conclusion, single-cell RNA sequencing may provide important insights into antipsychotic mechanisms of action and links to findings from psychiatric genomic studies.
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Affiliation(s)
- Anthony Abrantes
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | | | - NaEshia Ancalade
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Shadia Sekle
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Marcus L Basiri
- Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Garret D Stuber
- Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rainbo Hultman
- Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA, USA. .,Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
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Sun KY, Oreper D, Schoenrock SA, McMullan R, Giusti-Rodríguez P, Zhabotynsky V, Miller DR, Tarantino LM, Pardo-Manuel de Villena F, Valdar W. Bayesian modeling of skewed X inactivation in genetically diverse mice identifies a novel Xce allele associated with copy number changes. Genetics 2021; 218:6162162. [PMID: 33693696 DOI: 10.1093/genetics/iyab034] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 02/15/2021] [Indexed: 11/13/2022] Open
Abstract
Female mammals are functional mosaics of their parental X-linked gene expression due to X chromosome inactivation (XCI). This process inactivates one copy of the X chromosome in each cell during embryogenesis and that state is maintained clonally through mitosis. In mice, the choice of which parental X chromosome remains active is determined by the X chromosome controlling element (Xce), which has been mapped to a 176-kb candidate interval. A series of functional Xce alleles has been characterized or inferred for classical inbred strains based on biased, or skewed, inactivation of the parental X chromosomes in crosses between strains. To further explore the function structure basis and location of the Xce, we measured allele-specific expression of X-linked genes in a large population of F1 females generated from Collaborative Cross (CC) strains. Using published sequence data and applying a Bayesian "Pólya urn" model of XCI skew, we report two major findings. First, inter-individual variability in XCI suggests mouse epiblasts contain on average 20-30 cells contributing to brain. Second, CC founder strain NOD/ShiLtJ has a novel and unique functional allele, Xceg, that is the weakest in the Xce allelic series. Despite phylogenetic analysis confirming that NOD/ShiLtJ carries a haplotype almost identical to the well-characterized C57BL/6J (Xceb), we observed unexpected patterns of XCI skewing in females carrying the NOD/ShiLtJ haplotype within the Xce. Copy number variation is common at the Xce locus and we conclude that the observed allelic series is a product of independent and recurring duplications shared between weak Xce alleles.
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Affiliation(s)
- Kathie Y Sun
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Bioinformatics and Computational Biology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel Oreper
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Bioinformatics and Computational Biology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sarah A Schoenrock
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Neuroscience Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rachel McMullan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Genetics and Molecular Biology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paola Giusti-Rodríguez
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vasyl Zhabotynsky
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Darla R Miller
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lisa M Tarantino
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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