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Gene regulatory networks and essential transcription factors for de novo-originated genes

Nature Ecology & Evolution volume 9, pages 1487–1498 (2025)Cite this article

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Abstract

The regulation of gene expression is crucial for the functional integration of evolutionarily young genes, particularly those that emerge de novo. However, the regulatory programmes governing the expression of de novo genes remain unknown. To address this, we applied computational methods to single-cell RNA sequencing data, identifying key transcription factors probably instrumental in regulating de novo genes. We found that transcription factors do not have the same propensity for regulating de novo genes; some transcription factors regulate more de novo genes than others. Leveraging genetic and genomic tools in Drosophila, we further examined the role of two key transcription factors, achintya and vismay, and the regulatory architecture of new genes. Our findings identify key transcription factors associated with the expression of de novo genes and highlight how transcription factors, and possibly their duplications, are linked to the expressional regulation of de novo genes.

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Fig. 1: Expression patterns of de novo gene candidates across different tissues and major cell types.
Fig. 2: Transcriptional regulation of de novo genes.
Fig. 3: RNA sequencing of testis from five strains with different copy numbers of vis and achi.
Fig. 4: The expression of the genes in the Achi/Vis regulon is correlated with the abundance of Vis mRNA.
Fig. 5: Young de novo genes originated in mel-complex species were more likely to be regulated by Vis/Achi.

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Data availability

RNA-seq data are submitted to NCBI BioProject under accession number PRJNA1197503.

Code availability

Code and scripts for this study are available at https://github.com/LiZhaoLab/DeNovoGene_Transcriptional_Regulation and available via Zenodo at https://doi.org/10.5281/zenodo.15304293 (ref. 71).

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Acknowledgements

We thank members of L.Z.'s laboratory for helpful discussions. Stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used in this study. We thank The Rockefeller University High Performance Computing Center for the support in computation. This work was supported by National Institutes of Health MIRA R35GM133780, the Robertson Foundation and an Allen Distinguished Investigator Award from Paul G. Allen Family Foundation and Kellen Women’s Entrepreneurship Fund to L.Z.

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  1. These authors contributed equally: Junhui Peng, Bing-Jun Wang.

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  1. Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA

    Junhui Peng, Bing-Jun Wang, Nicolas Svetec & Li Zhao

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  1. Junhui Peng
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  2. Bing-Jun Wang
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Contributions

J.P., L.Z. and N.S. conceived the project. J.P. designed and performed large-scale scRNA-seq and regulon analysis. N.S. designed the genetic cross scheme. B.-J.W. and N.S. carried out the genetic experiments and RNA-seq analysis. J.P., L.Z., N.S. and B.-J.W. wrote the paper.

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Correspondence to Nicolas Svetec or Li Zhao.

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Nature Ecology & Evolution thanks Anna Grandchamp, Mohammad Siddiq and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Random permutation analysis of tissue- and cell-type- specificity.

(a). Random permutation with simulated annealing can be used to randomly sample genes with similar expression distribution (blue line) to that of de novo genes (dashed line). Randomly sampled genes before random permutation with simulated annealing show very different expression distributions (red solid line). (b) Differences in distributions before (red solid line) and after random permutation with simulated annealing (blue solid line). Tissue (c) and cell-type- (d) specificity of de novo genes (bootstrapped n = 100) were significantly larger than randomly sampled genes (bootstrapped n = 100) with similar expression distribution, with one-sided t-test p = 3e-162 and 4e-163, respectively. Boxplots show the median (center line), the 25th-75th percentiles (box), and whiskers extending to 1.5× IQR.

Extended Data Fig. 2 Clustering analysis of de novo gene expression across 250 different cell types.

The expression shows clusters in spermatogenesis, gland cells, sensory neurons, hemocytes, ovary cells, midguts, etc.

Extended Data Fig. 3 Regulon profiles of current and additional SCENIC predictions.

(a) and (bb) Current SCENIC predictions. (a) The ratio of de novo genes and testis-specific genes in each regulon. (b) Number of de novo genes and de novo and non-de novo genes in each regulon. (c)–(e) Additional SCENIC analysis with a lower TF expression cutoff of 10 (Methods). (c) Top regulons from this additional SCENIC analysis were similar to the current SCENIC analysis (Fig. 2a). (d) The ratio of de novo genes and testis-specific genes in each regulon. (e) Number of de novo genes and de novo and non-de novo genes in each regulon. (g)–(h) Additional SCENIC analysis with two additional gene sets except for de novo genes and TF genes, which are non-testis tissue specific genes and non-tissue specific genes, with the same cutoff as the original SCENIC analysis. (f) Top regulons from this additional SCENIC analysis were also similar to the current SCENIC analysis (Fig. 2a). (g) The ratio of de novo genes and non-testis tissue-specific genes in each regulon (n = 83). (h) The ratio of de novo genes and non-tissue specific genes in each regulon (n = 83). In (g) and (h), p-values were calculated by Kendall’s Tau and Spearman rank correlations, and shaded areas indicate 95% confident intervals.

Extended Data Fig. 4 Expression pattern of achi, Jra, and de novo genes predicted to be regulated by Achi and Jra.

(a) De novo genes that were predicted to be regulated by Achi were mostly expressed in the testis. (b) 30 out of 45 of the de novo genes predicted to be regulated by Achi have significant correlations with achi in the testis (p < 0.05), with 25 of them being positively correlated and 5 of them negatively correlated. (c) De novo genes that were predicted to be regulated by Jra were mostly expressed in the testis, but also show some expression in the fat body, heart, etc. (d) 49 out of 66 of the de novo genes predicted to be regulated by Jra have significant correlations in testis (p < 0.05), with 48 being positively correlated and only 1 negatively correlated. In (b) and (d), for each predicted target, the p-value was determined as the smallest obtained from a two-sided t-test, Pearson Correlation Coefficient, and Kendall Tau rank test, with the sample size (n) of 7, corresponding to the 7 different spermatogenesis stages.

Extended Data Fig. 5 Expression correlation between vis and Vis targets.

The slope and the coefficient of determination between vis and Vis targets, including (a) testis-specific genes, (b) de novo genes, and (c) TFs.

Extended Data Fig. 6 Prevalence of Vis/Achi binding motifs across the genome.

(a) (a) The number matches found for the binding motifs of TFs in the whole genome. (b) The number of ATAC-seq peaks harboring matches found for different TFs. (c) The number of genes that have matches for different TFs in their promoter regions. (d) The number of de novo gene candidates that have matches for different TFs in their promoter regions.

Extended Data Fig. 7 Gene tree of vis/achi inferred from protein sequence alignments in melanogaster subgroup species.

The protein sequences were obtained from blastp search against non-redundant protein sequence database. Protein sequences from suppressed genome assemblies and annotations were shown in gray.

Extended Data Table 1 Tissue specificity of top 31 regulons
Full size table
Extended Data Table 2 The correlations between the expression levels of vis and other TF genes
Full size table

Supplementary information

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Supplementary Figs. 1–8 and Table 1.

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Supplementary Data 1 (download CSV )

Tissue specificity of all annotated genes (including coding and non-coding) in Fly Cell Atlas dataset.

Supplementary Data 2 (download CSV )

Predicted de novo gene regulons.

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Peng, J., Wang, BJ., Svetec, N. et al. Gene regulatory networks and essential transcription factors for de novo-originated genes. Nat Ecol Evol 9, 1487–1498 (2025). https://doi.org/10.1038/s41559-025-02747-y

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