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Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes

Nature Neuroscience volume 21, pages 1680–1688 (2018)Cite this article

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Abstract

Heterogeneity in early language development in autism spectrum disorder (ASD) is clinically important and may reflect neurobiologically distinct subtypes. Here, we identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate functional neuroimaging (fMRI) response to speech. Gene coexpression modules associated with the multivariate fMRI response to speech were different for all pairwise comparisons between typically developing toddlers and toddlers with ASD and poor versus good early language outcome. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known. Associations between neuroimaging measures and gene expression levels in blood leukocytes may offer a unique in vivo window into identifying brain-relevant molecular mechanisms in ASD.

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Fig. 1: Clinical behavioral trajectories over the first 4 years of life in TD toddlers and toddlers with ASD plus good or poor early language outcome.
Fig. 2: Decreased fMRI response to speech in toddlers with ASD and poor early language outcome.
Fig. 3: Multivariate gene coexpression–fMRI association in ASD with good or poor early language outcome and typically developing control toddlers.
Fig. 4: Tissue-class enrichments with sets of nonzero or zero association modules.
Fig. 5: Vocal learning, human-specific, and ASD-associated enrichments with sets of broadly expressed genes and nonzero or zero association modules.

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Data availability

The raw data that support the findings from this study are publicly available from the NIH National Database for Autism Research (NDAR). Raw blood leukocyte gene expression data are publicly available via Gene Expression Omnibus (GEO) (GSE42133; GSE111175). Songbird Area X gene expression data are publicly available via GEO (GSE34819). GTEx data are publicly available at https://gtexportal.org/. ASD postmortem cortical gene expression can be found at https://github.com/dhglab/Genome-wide-changes-in-lncRNA-alternative-splicing-and-cortical-patterning-in-autism/.

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Acknowledgements

This research was supported by grants to E.C. and K.P. (NIMH R01-MH080134 (K.P.), NIMH R01-MH104446 (K.P.), NFAR grant (K.P.), NIMH Autism Center of Excellence grant P50-MH081755 (E.C. and K.P.), NIMH R01-MH036840 (E.C.), NIMH R01-MH110558 (E.C. and N.E.L.), NIMH U01-MH108898 (E.C.), NIDCD R01-DC016385 (E.C., K.P., and M.V.L.), CDMRP AR130409 (E.C.), and Simons Foundation 176540 (E.C.)). The work was additionally supported by an ERC Starting Grant (ERC-2017-STG; 755816) to M.V.L. and a grant from the Brain & Behavior Research Foundation (NARSAD) to T.P. We thank R. Znamirowski, C. Ahrens-Barbeau, S. Solso, K. Campbell, M. Mayo, and J. Young for help with data collection, and S. Spendlove and M. Weinfeld for assistance with clinical characterization of subjects.

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Authors and Affiliations

  1. Department of Psychology, University of Cyprus, Nicosia, Cyprus

    Michael V. Lombardo

  2. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK

    Michael V. Lombardo, Varun Warrier & Richard A. I. Bethlehem

  3. Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA

    Tiziano Pramparo, Cynthia Carter Barnes, Linda Lopez, Karen Pierce & Eric Courchesne

  4. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Vahid Gazestani & Nathan E. Lewis

  5. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Nathan E. Lewis

  6. Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA

    Nathan E. Lewis

  7. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

    Lisa Eyler

  8. Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

    Lisa Eyler

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Contributions

E.C., K.P., L.E., M.V.L., and T.P. conceived the idea and designed the study. M.V.L. conceived and performed all analyses. T.P., V.G., V.W., R.A.I.B., and N.E.L. aided in data analyses. E.C., K.P., L.E., L.L., and C.C.B. collected data. E.C., K.P., L.E., N.E.L., T.P., and M.V.L. obtained grant funding. M.V.L. and E.C. wrote the manuscript. All authors contributed to editing the manuscript.

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Correspondence to Michael V. Lombardo or Eric Courchesne.

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Integrated supplementary information

Supplementary Figure 1 Preservation of ASD blood leukocyte modules in ASD frontal and temporal cortical tissue.

Zsummary preservation statistics for all ASD blood leukocyte modules. Zsummary > 10 indicates strong preservation, while 2 < Zsummary < 10 indicates moderate preservation. Non-zero modules M2, M8, and M11 are annotated on this plot, since they show Zsummary > 2. M2 has Zsummary = 8.1, which indicates high-moderate preservation.

Supplementary Figure 2 Enrichments between non-zero and zero modules with vocal learning, human-specific, and ASD-associated gene lists after broadly expressed genes are removed.

Numbers within the cells indicate the enrichment odds ratio, while the color indicates the -log10(p-value). Cells highlighted in green pass multiple comparison correction at FDR q < 0.05.

Supplementary Figure 3 Soft power, TOM dendrogram, and module preservation plots for comparisons of each group.

Panel A shows the soft power plot for the main WGCNA analysis including data from all groups. A horizontal red line depicts soft power topology model fit R2 of 0.9, where the chosen soft power of 16 is located. Panel B shows the TOM dendrogram with modules labeled at the bottom. Panel C shows the module preservation Zsummary statistic for WGCNA analyses run separately on each group in order to show that networks are highly preserved (Zsummary > 10) across groups.

Supplementary Figure 4 Plots of module eigengenes for all modules and all groups.

For all comparisons shown below, the sample sizes are TD n = 37 (blue), ASD Good n = 40 (pink), ASD Poor n = 41 (green). The box in the boxplots indicates the interquartile range (IQR; Q1 indicates the 25th, while Q3 indicates the 75th percentile) and the whiskers indicate Q1-(1.5*IQR) or Q3 + (1.5*IQR). The line within the box represents the median. The y-axis indicates the module eigengene, while the x-axis shows each group.

Supplementary Figure 5 Module preservation between groups.

This figure shows the module preservation Zsummary statistic for WGCNA analyses run separately on each group in order to show that networks are highly preserved (Zsummary > 10) across groups.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5

Reporting Summary

Supplementary Table 1

Statistics for linear mixed effect model analysis of clinical trajectory differences. Sheet 1 (Trajectories_ANOVA_allGrps) shows the F-statistic and p-values for main effects of Age and Group and the Age*Group interaction. Sheets 2–4 are the same F-statistic and p-values for analyses on pairwise group comparisons. Any comparison passing multiple comparison correction at FDR q<0.05 is highlighted in red

Supplementary Table 2

Statistics for ROI analyses. Sheet 1 called “ROI_ANOVAstats” are the ANOVA statistics from ROI analyses. Sheet 2 called “ROI_PairwiseGroupComparisons” are the statistics for the pairwise group-comparisons for ROI analyses

Supplementary Table 3

Differential expression analysis results. Sheet 1 called “DE_subtypes_genes” shows results from differential expression analysis at the gene-level. The main model evaluates the effect of group (TD, ASD Good, ASD Poor) with an F-test and corresponding p-value. The FDR q-value is also shown for each gene. No gene passes for a statistically significant of group at FDR q<0.05. All other columns in Sheet 1 show the pairwise group comparisons (t-statistic, p-value, and FDR q per each gene). Sheet 2 called “DE_subtypes_ME” shows results from differentially expression analysis on module eigengenes from the WGCNA analysis. The main model evaluated is the same model as the gene-level DE analysis, testing an effect of group (TD, ASD Good, ASD Poor), and the remaining columns show statistics for each pairwise group comparison. Just like the DE analysis at the gene-level, no modules show statistically significant effects of group surviving FDR q<0.05

Supplementary Table 4

Statistics from PLS analyses. Sheet 1 (“Subtype_PLS”) shows statistics for the PLS model that included TD and ASD subtypes (ASD Good and ASD Poor) as separate groups. Sheet 2 (“CaseCtrl_PLS”) shows statistics for the PLS model that included TD and ASD as the main group distinction (with no distinction for ASD language outcome subtypes). Sheet 3 (“Subtype_PLS_LV1_moduleCorr”) shows the fMRI-gene co-expression correlations and 95% confidence intervals for each group and each module and indicates which modules are non-zero or not

Supplementary Table 5

Biological process enrichment results for each module. Each sheet shows the results of biological process enrichment tests for each module from MetaCore GeneGO

Supplementary Table 6

Lists of genes within each module along with module membership statistics for each module. Each row shows a gene and each column shows the module membership value of that gene to each module. The column labeled “Module” shows the module label assigned to each gene

Supplementary Table 7

Demographic and clinical behavioral variables summary. This table shows demographic information such as sample size, sex, age, and descriptive statistics (mean with standard deviation in parentheses) for each clinical variable at the intake and outcome assessments

Supplementary Table 8

ANOVA statistics for cell type surrogate proportion variables estimated with CellCODE. Surrogate proportion variables of different leukocyte cell types were estimated with CellCODE and tested for a group difference with an ANOVA. The table reports the F-statistic and p-value for the group effect from these ANOVAs

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Lombardo, M.V., Pramparo, T., Gazestani, V. et al. Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat Neurosci 21, 1680–1688 (2018). https://doi.org/10.1038/s41593-018-0281-3

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