Research paperOmics analysis of mouse brain models of human diseases
Introduction
Brain pathologies alter the expression of thousands of genes and proteins. Some of these molecular changes likely contribute to cognitive decline and behavioural deficits. In the present study, six pathologies affecting mammalian brain function were considered. The pathologies could be grouped into neurodegenerative diseases and neuropsychiatric disorders. Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, are characterized by different clinical profiles and diverse initiating triggers. Despite these differences, overlap between these neurodegenerative disorders has been reported at various cellular levels, including abnormal protein deposition, death of neuronal and glial cells, and mitochondrial deficits (Irwin et al., 2013, Weintraub et al., 2012). Neuropsychiatric disorders, such as autism and schizophrenia, share a number of features, including marked impairments in interpersonal relations, cognitive dysfunction and several genetic signals (Goldstein et al., 2002, Sullivan et al., 2012). The current study expands on this background using omics approaches to identify a conserved molecular signature between this diverse collection of brain dysfunctions. The identification of common gene/protein profiles related to brain alterations, if they exist, might be a pertinent step for the development of potential therapeutic agents, some of which may target two, three, or more brain pathologies.
One of the most powerful approaches to identify genes and related pathways altered by brain disorders is to assess changes in gene expression using microarray technology (Blalock et al., 2010, McCarroll et al., 2004). Using a cohort of well-characterized post-mortem brain tissues from patients with six human disorders, including Alzheimer's, Parkinson's and Huntington's diseases, amyotrophic lateral and multiple sclerosis, and schizophrenia, Durrenberger et al. (2015) reported that there were no common dysregulated genes between these various disorders. Importantly, tissue blocks were chosen according to the regions and disease stages in which it would have been expected to observe ongoing pathology as a result of the primary disease mechanisms. In animal models of Alzheimer's disease, cerebral stroke, and multiple sclerosis, Tseveleki et al. (2010) showed that only 18 genes overlapped between the three disease models.
A major challenge towards a comprehensive analysis of biological systems is the integration of data from different “omics” sources (Noorbakhsh et al., 2009). Indeed, altered patterns of gene expression are not always associated with changes in protein levels. It is not known how tightly the changes in gene expression correspond to the changes in the proteomic profiles. Thus, applying a proteomic analysis adds an extra level of understanding of what is occurring in brain disease-related processes. Therefore, in the present study we combined both transcriptomic and proteomic experiments to uncover common markers of different neurodegenerative conditions. Attention was only paid to molecules identified using both techniques. These selected genes/proteins were further studied using graph theory analysis, which allowed us to define networks and to quantify their properties. Networks can be described as graphs composed of nodes (genes/proteins) and edges (correlations) among the nodes (Watts and Strogatz, 1998). Various network properties can be measured, allowing us to identify special nodes, which are now usually referred to as hubs and bottlenecks. These hubs and bottlenecks represent essential genes/proteins in the network; it is widely believed that highly connected hubs and bottlenecks are the best therapeutic targets for modifying network behaviour (Albert et al., 2000, Jeong et al., 2000).
A common approach to the investigation of human diseases and therapeutics is preliminary investigations using animal models. Six animal models simulating certain aspects of human dysfunctions were considered in the present study. The models include the APPswe/PS1 mouse, which mimics the accumulation of amyloid peptides observed in Alzheimer's disease (Filali et al., 2011, Paban et al., 2014, Woo et al., 2010); the THY-Tau22 mouse, which is an animal model of Tauopathy, a large group of brain diseases including Alzheimer's disease (Laurent et al., 2016, Schindowski et al., 2006, Schindowski et al., 2008, Van der Jeugd et al., 2011); the PINK1-Knock Out (KO) mouse, which models Parkinson's disease (Dehorter et al., 2012, Gispert et al., 2009, Kitada et al., 2007); the R6/1 mouse, which reproduces Huntington's disease (HD) (Lebreton et al., 2015, Mangiarini et al., 1996, Pietropaolo et al., 2011a); the Fmr1-KO mouse, which models Fragile X syndrome, a well-known genetic disorder presenting autism symptoms (Oddi et al., 2013, Pietropaolo et al., 2016); and the MAP6-KO mouse, which models the schizophrenia-like phenotype (Andrieux et al., 2002, Hanaya et al., 2008, Volle et al., 2013). We are aware that no mouse model perfectly mimics all aspects of a human disease (Leung and Jia, 2016). Nonetheless, we believe that these models cover a wide range of human neurodegenerative conditions that will allow us to better understand how the brain responds to dysfunction. By analysing the whole genome and proteome expression changes, the molecular profile of each brain disorder could be studied and used to further identify shared and conserved molecular signatures.
Section snippets
Animals
The APPswe/PS1 mice were male transgenic mice harbouring the chimeric human/mouse APP gene with the Swedish mutation and human presenilin I (A246E variant) (strain B6C3-Tg(APP695)3Dbo Tg(PSEN1)5Dbo/J; Jackson Laboratories, Canada). APPswe/PS1 (N = 4) and wild-type (Wt) littermate mice (N = 4) were used at 9–10 months of age, i.e., an age at which amyloid deposits are readily detected in the cortex and hippocampus and the animals display cognitive disturbances (Filali and Lalonde, 2009, Paban et al.,
Behavioural analysis
The data are reported in Fig. 1. The APPswe/PS1 mouse model and Wt animals exhibited comparable performance during the acquisition phase in the T-water maze (data not shown). In contrast, during the reversal-learning phase, the number of trials to criterion for the APPswe/PS1 mice was higher than that of the Wt mice (Mann-Whitney test; p < 0.03) (Fig. 1A). The THY-Tau22 mice showed a prolonged escape latency during the acquisition phase in the Morris water maze, which occurred from training day
Discussion
The aim of the present study was to explore whether human disorders and the corresponding dysregulated genes and proteins might be related to each other. In other words, the idea was to determine whether common dysregulated molecules exist between various brain diseases and to identify the common molecular pathways responsible for brain dysfunction. Therefore, six genetic mouse models of Alzheimer's, Parkinson's, and Huntington's diseases, Tauopathy, autism, and schizophrenia were considered.
Author contribution
Conceived and designed the experiments: VP. Performed the experiments: BL, CV, DB, LB, SP, YHC, SGF. Technical support: EM, AG. Analysed the data: VP. Wrote the paper: VP.
Funding
This research was supported by the French Ministry for Education and Research Funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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