Thousands of Pristionchus pacificus orphan genes were integrated into developmental networks that respond to diverse environmental microbiota

Abstract

Adaptation of organisms to environmental change may be facilitated by the creation of new genes. New genes without homologs in other lineages are known as taxonomically-restricted orphan genes and may result from divergence or de novo formation. Previously, we have extensively characterized the evolution and origin of such orphan genes in the nematode model organism Pristionchus pacificus. Here, we employ large-scale transcriptomics to establish potential functional associations and to measure the degree of transcriptional plasticity among orphan genes. Specifically, we analyzed 24 RNA-seq samples from adult P. pacificus worms raised on 24 different monoxenic bacterial cultures. Based on coexpression analysis, we identified 28 large modules that harbor 3,727 diplogastrid-specific orphan genes and that respond dynamically to different bacteria. These coexpression modules have distinct regulatory architecture and also exhibit differential expression patterns across development suggesting a link between bacterial response networks and development. Phylostratigraphy revealed a considerably high number of family- and even species-specific orphan genes in certain coexpression modules. This suggests that new genes are not attached randomly to existing cellular networks and that integration can happen very fast. Integrative analysis of protein domains, gene expression and ortholog data facilitated the assignments of biological labels for 22 coexpression modules with one of the largest, fast-evolving module being associated with spermatogenesis. In summary, this work presents the first functional annotation for thousands of P. pacificus orphan genes and reveals insights into their integration into environmentally responsive gene networks.

Fig 1. Transcriptional response to 24 bacterial environments.

(A) The heatmap shows the correlation between transcriptomes. Apart from Wautersiella LRB104 all transcriptomes are highly similar. (B) Complementary analysis using principal component analysis identifies Wautersiella LRB104 as the most distinct environment. C) The histograms show the distribution of correlation values for comparisons within and across bacterial families. The expression profiles of P. pacificus worms on bacteria of different families can be more similar than the profile on bacteria of the same family. This suggests that the transcriptional response does not strictly reflect bacterial phylogeny.

Fig 2. Expression level of environmentally responsive coexpression modules.

We visualized the z-score normalized expression levels for the 28 largest modules across the bacterial environments. Modules with more than 100 genes were randomly downsampled to 100 genes. While genes of module 4 are most strongly expressed on Wautersiella LRB104, module 3 shows the highest expression in response to the two Hafnia strains. This demonstrates that environmental microbiota can modulate specific coexpression modules.

Fig 3. Developmental signature of coexpression modules.

We visualized the z-score normalized expression levels of genes in the 28 largest coexpression modules throughout postembryonic development after hatching (0h) on E. coli OP50 [43]. Modules with more than 100 genes were randomly downsampled to 100 genes. The coexpression modules show distinct expression profiles suggesting a link between environmental response and developmental regulation.

Fig 4. Phylostratigraphic analysis across coexpression modules.

Phylostrata were defined based on the presence of homologs for a given gene in the most distantly related species. Each phylostratum defines a branch in the phylogeny where a gene likely originated. The barplot shows the distribution of phylostrata across coexpression modules with the dashed line marking the fraction of diplogastrid-specific orphan genes across all genes. The stars indicate significant enrichment and depletion of orphan genes based on simulating the integration of new genes to existing network modules (P < 0.01, S7 Fig).

Fig 5. Transcriptomic and metabolic enrichment of coexpression modules.

(A) Coexpression modules show distinct overlaps with regional genes (P1-P11) that were identified from spatial transcriptomics. (B) Multiple metabolic pathways are enriched in individual coexpression modules. (C) Coexpression modules were compared with multiple expression gene sets. 16 out of 28 coexpression modules show significant overlap with differentially expressed genes in previous comparisons of environmental microbiota.