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. 2022 May 6;50(8):4302-4314.
doi: 10.1093/nar/gkac276.

Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms

Megan Crow  1 , Hamsini Suresh  1 , John Lee  1 , Jesse Gillis  1
Affiliations

Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms

Megan Crow et al. Nucleic Acids Res. .

Abstract

What makes a mouse a mouse, and not a hamster? Differences in gene regulation between the two organisms play a critical role. Comparative analysis of gene coexpression networks provides a general framework for investigating the evolution of gene regulation across species. Here, we compare coexpression networks from 37 species and quantify the conservation of gene activity 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. We find that ancient genes are expressed in multiple cell types and have well conserved coexpression patterns, however they are expressed at different levels across cell types. Thus, differential regulation of ancient gene programs contributes to transcriptional cell identity. We propose that this differential regulation may play a role in cell diversification in both the animal and plant kingdoms.

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Figures

Figure 1.
Figure 1.
Schematic to illustrate the variation of functional conservation with phylogenetic distance and lineage-specific gene gains and losses. (A) After the first speciation event, species A loses a gene (red square), while species B* and B** undergo lineage-specific gene duplications (checkered red circles and squares) after the second speciation event. Black lines indicate strength of coexpression between gene pairs within a species, and colored lines indicate the extent of functional conservation between select orthologous genes across species. (B) Heatmaps of functional conservation between every pair of genes in species B* and B** and in species B* and A indicate that functional conservation negatively correlates with phylogenetic distance. Gene modules retaining a single-copy of genes and displaying high coexpression similarity across the species pair are highlighted by green boxes. Duplicated genes are highlighted in purple boxes, and are labeled conserved or diverged based on their coexpression similarity post-duplication.
Figure 2.
Figure 2.
Aggregate coexpression networks are a powerful tool for comparative genomics. (A) In this work we examine coexpression across species from 3 kingdoms: plants, animals and fungi. Here we focus on the subset of species with GO annotations. The dendrogram shows phylogenetic relationships between these species, and the barplots indicate the number of datasets used to build aggregate coexpression networks. (B) Circles show the mean GO prediction performance for individual networks (+/− standard deviation) while triangles indicate aggregate network performance. (C) Aggregate robustness is high across all species, with variation dependent on n.
Figure 3.
Figure 3.
Divergence in gene coexpression correlates with phylogeny. (A) Method schematic. Circles represent genes and line thickness indicates strength of coexpression between one target gene (red) and all others. For each target gene in pig, we identify the set of pig genes that are maximally coexpressed with it, shown in blue. We evaluate how conserved this coexpression pattern is in yeast, then repeat the task in the other direction. Genes with high coexpression to the target in both species are highlighted in the gray ovals. Coexpression conservation is reported as the average AUROC in both directions (i.e. pig-yeast and yeast-pig). (B) Points show mean coexpression conservation for 1-to-1 orthologs between human and each other species. Coexpression conservation is negatively correlated with phylogenetic distance (rho = −0.95, P < 10–6). (C) Mean coexpression conservation for 1-to-1 orthologs between human and each species are plotted against the number of networks included in the aggregate network. Performance increases with additional data. (D) Boxplots show coexpression conservation for 492 orthologous groups defined at the last common ancestor of all eukaryotes, plotted with respect to species divergence times. As in panel B, coexpression is more conserved among more recently diverged species. (E) Boxplots show coexpression conservation scores. 1-to-1 orthologs are more conserved than N-to-M orthologs (Wilcoxon P < 10–16). (F) Coexpression profiles for a 1-to-2 human-mouse ortholog group. The human gene VWA5A has a strongly conserved coexpression profile with mouse Vwa5a (left, conservation AUROC = 0.83) but not with mouse AW551984 (right, AUROC = 0.46).
Figure 4.
Figure 4.
Gene coexpression conservation is associated with ortholog concordance across algorithms and can predict human-yeast functional analogs. (A) Heatmap of algorithm concordance. The majority of algorithms (9/12) make similar predictions, with outliers arising from selection biases (i.e. inclusion of only a subset of species). (B) Mean conservation for human-worm orthologs is plotted against the number of algorithms predicting the relationship. Conservation of gene neighborhoods correlates with ortholog confidence. (C) Bars show the correlation between the number of algorithms and conservation of neighborhoods for each gene pair, binned into three divergence times. Conservation correlates with ortholog confidence for pairs of species that diverged > 100MYA but not for more recently diverged species. (D) Cumulative success of human-yeast complementation is plotted as a function of gene activity conservation. Human genes with conserved gene neighborhoods are likely to compensate for loss of their yeast orthologs.
Figure 5.
Figure 5.
Ubiquitously expressed genes have strongly conserved coexpression patterns that are not explained by expression level alone. (A) Plots of mouse and Arabidopsis scRNA-seq data, with examples of constitutive (left) vs. cell-type specific expression (right), color indicates expression level. (B) Coexpression conservation is plotted with respect to cell type specificity for mouse (top) and Arabidopsis (bottom). Lines are loess fits on mean values for each species, +/− SD. Cell type specificity is negatively associated with conservation of gene neighborhoods. (C) Pearson correlation of within-species average expression with neighborhood preservation. (Inset) Representative scatterplot showing the relationship between expression level and coexpression preservation within yeast. Because most genes show strong coexpression preservation (x-axis) there is a weak relationship between expression level and coexpression preservation. (D) Human gene expression level is plotted with respect to coexpression conservation for six representative species. No relationship is observed.
Figure 6.
Figure 6.
Genes with conserved coexpression patterns are expressed in continuous gradients across cell types, suggesting an ancient mechanism of cell type divergence. (A) Expression variance is associated with conservation of gene neighborhoods. (Top) Expression variance across > 200 yeast datasets predicts coexpression conservation. TPR = true positive rate, FPR = false positive rate. ROC curves for all species were binned along x-axis, mean +/− SD is plotted. (Bottom) Coexpression conservation is plotted with respect to within-cell type variance in mouse, with loess fits on mean values for each species. (B) Across vs. within-cell type variance in mouse is plotted. Colors indicate local point density. The space can be broken into three regions: genes with high-within and high-across variance are typically more ancient, those that are low/low are more recent, and markers have high-across and low-within cell type variance. Due to their high conservation of coexpression patterns, high/high genes may have conserved functions vis-à-vis cell identity. (C) Examples of continuous (top) vs. marker-like (bottom) expression in mouse (left) and Arabidopsis (right). MSC = mesenchymal stem cell, QC = quiescent center. The conserved gene with high within- and across-cell type variance is more continuous across cell types, whereas the markers (high/low) are either on or off. (D) Schematic illustrating the difference between marker-like and continuous expression across cell types. See Supplementary Figure S7 for additional discussion.
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