Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways Jeremy A. Millera,b, Steve Horvathc,d, and Daniel H. Geschwindb,c,1 aInterdepartmental Program for Neuroscience, Departments of cHuman Genetics and dBiostatistics, and bProgram in Neurogenetics and Neurobehavioral Genetics, Department of Neurology and Semel Institute, University of California, Los Angeles, CA 90095-1769 Edited by Joseph S. Takahashi, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, and approved June 1, 2010 (received for review December 10, 2009) Because mouse models play a crucial role in biomedical research works by merging data from many microarray studies, and then related to the human nervous system, understanding the similari- applying weighted gene coexpression network analysis (WGCNA) ties and differences between mouse and human brain is of (4–7). WGCNA elucidates the higher-order relationships between fundamental importance. Studies comparing transcription in hu- genes based on their coexpression relationships, delineating modules man and mouse have come to varied conclusions, in part because of of biologically related genes and permitting a robust view of their relatively small sample sizes or underpowered methodologies. transcriptome organization (3, 5, 8, 9). Within groups of highly coexpressed genes (“modules”) that comprise the core functional To better characterize gene expression differences between mouse fi and human, we took a systems-biology approach by using weighted units of the transcriptional network, WGCNA also identi es gene coexpression network analysis on more than 1,000 micro- the most highly connected, or most central genes within each module, referred to as “hubs.” We find that both gene expression arrays from brain. We find that global network properties of the and connectivity—the summation of coexpression relationships brain transcriptome are highly preserved between species. Further- for each gene with all other genes—tend to be preserved between more, all modules of highly coexpressed genes identified in mouse fi fi species. Furthermore, we nd that many modules in human show were identi ed in human, with those related to conserved cellular preserved expression patterns in mouse. This includes, for ex- functions showing the strongest between-species preservation. ample, all modules associated with core cellular processes, such Modules corresponding to glial and neuronal cells were sufficiently as ribosomal and mitochondrial function, consistent with pre- preserved between mouse and human to permit identification of vious results (10, 11). NEUROSCIENCE cross species cell-class marker genes. We also identify several robust We also find many between-species differences that provide human-specific modules, including one strongly correlated with insight into human disease. First, we identify a human-specific measures of Alzheimer disease progression across multiple data sets, module that was originally associated with AD progression in an whose hubs are poorly-characterized genes likely involved in Alz- earlier study of aging and AD (7),aswellasanotherrelated heimer disease. We present multiple lines of evidence suggesting human-specific module containing GSK3β and tau, both of links between neurodegenerative disease and glial cell types in hu- which are implicated in AD and other dementias (12). Next, man, including human-specific correlation of presenilin-1 with oligo- we find that significant changes in network position between dendrocyte markers, and significant enrichment for known neuro- the species may reflect a gene’s relationship to human-specific degenerative disease genes in microglial modules. Together, this disease phenotypes. For example, the AD-associated gene pre- work identifies convergent and divergent pathways in mouse and senilin 1 (PSEN1) exhibits poor between-species network pres- human, and provides a systematic framework that will be useful for ervation, showing strong transcriptional coexpression with oligodendrocyte markers in human alone, suggesting that its understanding the applicability of mouse models for human brain fi disorders. role in adult human brain has signi cantly diverged from its role in mouse. We also find evidence for clustering of neurodegener- ative disease related genes within microglial modules, highlighting neurodegenerative disease | systems biology | evolution | metaanalysis | the potential role of this glial cell type in human neurodegene- gene expression ration. To the best of our knowledge this is the first metaanalysis to focus solely on brain-specific data, and can therefore provide lthough certain disease mutations cause comparable pheno- unique insight into similarities and differences in transcriptional Atypes in mouse and human, the effects in mouse of any given patterns between the human and mouse brain. disease-causing mutation can diverge wildly from human, especially in the case of neurological disorders. For example, whereas mouse Results knock-in models of Huntington disease (HD) display many of the Constructing the Mouse and Human Networks. We reasoned that same behavioral and pathological features seen in human HD (1), comparison of coexpression networks between mouse and hu- none of the three highly penetrant, dominantly transmitted causes man could provide valuable insight into human brain disorders. of Alzheimer disease (AD), alone, produce AD-like pathology We sought to compile inclusive coexpression networks repre- consisting of both plaques and tangles in mouse brain (2). Given the senting a general survey of brain transcription in both species multifactorial, complex genetic nature of most human neurological (Fig. 1A shows a schematic of network creation, and the SI Text and psychiatric diseases, such species differences are unlikely to be includes a glossary of network related terms). After careful data caused by one single pathway or gene. Therefore, understanding the filtering and preprocessing to eliminate outliers (3) (Materials and molecular basis of phenotypic differences between mouse and hu- Methods and SI Text), our analysis included 1,066 samples from 18 man caused by even a single mutation is likely to require using a systems biology approach and a genome-wide view. Previously, we demonstrated that the human brain transcriptome has a reproducible, higher-order organization, and that knowing Author contributions: J.A.M., S.H., and D.H.G. designed research; J.A.M. performed re- fi search; and J.A.M. and D.H.G. wrote the paper. this structure permits signi cant functional insights (3). So, to sys- fl tematically appreciate potential species similarities and differences, The authors declare no con ict of interest. we created and compared separate transcriptional networks from This article is a PNAS Direct Submission. mouse and human brain tissue. Such a characterization in mouse Freely available online through the PNAS open access option. brain could orient future brain-related studies, by providing 1To whom correspondence should be addressed. E-mail: [email protected]. a priori knowledge regarding the similarity of specificgeneex- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. pression patterns between human and mouse. We created net- 1073/pnas.0914257107/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.0914257107 PNAS Early Edition | 1of6 Downloaded by guest on September 28, 2021 human and 20 mouse data sets, representing various diseases, indicating that connectivity is a more sensitive measure of evo- brain regions, study designs, and Affymetrix platforms (Table S1). lutionary divergence than differential expression (4). We also For each species, we created a network from these data, first by found greater between-species expression correlations than pre- calculating weighted Pearson correlation matrices corresponding vious microarray studies of mouse and human brain (14) as well to gene expression, then by following the standard procedure of as liver, testes, and muscle (15) (R of approximately 0.45 for all WGCNA to create the two networks. Briefly, weighted correla- studies). These results suggest that our large data sets allow us to tion matrices were transformed into matrices of connection recognize interspecies transcriptional similarities as well as, or strengths using a power function (5). These connection strengths better than, previous methods. As a further validation of pre- were then used to calculate topological overlap (TO), a robust dicted network interactions on a global level, we compared gene– and biologically meaningful measurement that encapsulates the gene connectivity based on TO to known protein–protein inter- similarity of two genes’ coexpression relationships with all other actions (PPIs), demonstrating a linear relationship between TO SI Text genes in the network (5, 13). Hierarchical clustering based on TO and PPIs in both human and mouse (Fig S2 and ). Genes was used to group genes with highly similar coexpression rela- with high TO were much more likely to interact, consistent with tionships into modules. In all, we found 15 modules in the human previous results from multiple species (3, 16). network (Fig. 1B) and nine modules in the mouse network (Fig. 1C), which were used to guide our final module characterizations Many Mouse and Human Network Modules Are Highly Similar. To (Materials and Methods). assess gene coexpression preservation between the species on a module-by-module basis, we first calculated the module