PhaeoNet: A Holistic RNAseq-Based Portrait of Transcriptional Coordination in the Model Diatom Phaeodactylum tricornutum

Abstract

Transcriptional coordination is a fundamental component of prokaryotic and eukaryotic cell biology, underpinning the cell cycle, physiological transitions, and facilitating holistic responses to environmental stress, but its overall dynamics in eukaryotic algae remain poorly understood. Better understanding of transcriptional partitioning may provide key insights into the primary metabolism pathways of eukaryotic algae, which frequently depend on intricate metabolic associations between the chloroplasts and mitochondria that are not found in plants. Here, we exploit 187 publically available RNAseq datasets generated under varying nitrogen, iron and phosphate growth conditions to understand the co-regulatory principles underpinning transcription in the model diatom Phaeodactylum tricornutum. Using WGCNA (Weighted Gene Correlation Network Analysis), we identify 28 merged modules of co-expressed genes in the P. tricornutum genome, which show high connectivity and correlate well with previous microarray-based surveys of gene co-regulation in this species. We use combined functional, subcellular localization and evolutionary annotations to reveal the fundamental principles underpinning the transcriptional co-regulation of genes implicated in P. tricornutum chloroplast and mitochondrial metabolism, as well as the functions of diverse transcription factors underpinning this co-regulation. The resource is publically available as PhaeoNet, an advanced tool to understand diatom gene co-regulation.

Keywords: aureochromes; chloroplast-mitochondria; epigenetics; sigma factors; stramenopile; transcriptomics.

FIGURE 1

Construction and topology of PhaeoNet. (A) Workflow diagram of the steps performed to construct PhaeoNet. (B) A Multi-Dimensional Scaling (MDS) plot of PhaeoNet. The dots correspond to genes and the colors correspond to the WGCNA modules. The tips of the plot correspond to hub genes of PhaeoNet. (C) Heatmap plot showing the TOM supplemented by the WGCNA module colors prior to merging. (D) Gene dendrogram of all incorporated PhaeoNet genes, obtained by average linkage hierarchical clustering. The first color row underneath the dendrogram shows the WGCNA module assignment obtained by the Dynamic Tree Cut method. The bottom color row shows the merged modules based on a correlation threshold of 0.75.

FIGURE 2

Visualization and analysis of an exemplar PhaeoNet merged module (paleturquoise). (A) Density heatmap of all genes assigned to the paleturquoise merged module. The y-coordinate positions in the graph relate to density distribution of gene expression in each sample (shown on the left y-axis); the four middle dashed lines (indicated by horizontal arrows on either side of the graph) correspond to the median, first and third quantiles (shown on the right y-axis). The majority of the genes in this specific module show limited variation in expression profiles over different conditions samples. (B) A topological representation of connectedness within the paleturquoise merged module, visualized with Cytoscape (version 3.6.1). Each node represents a gene. Edges represent pairwise correlations between genes. The network shows all the paleturquoise module genes with a correlation value over a threshold of 0.20.

FIGURE 3

Biological properties associated with PhaeoNet merged modules. This Figure provides an overview of enrichments of different organelle targeting (Horton et al., 2007; Gschloessl et al., 2008; Fukasawa et al., 2015; Gruber et al., 2015), epigenetic (Veluchamy et al., 2013, 2015; Zhao et al., 2020), evolutionary (Rastogi et al., 2018) and KEGG pathway annotations (Kanehisa, 2017) enriched in merged modules. The first seven (shaded) columns provide a score for different conditions, aggregated from chi-squared P-values of multiple enrichment predictors (defined beneath): enrichments in each condition carry a score of +1 if significant to P < 0.05 and +2 if significant to P < 10^–05; and depletions in each condition carry a score of –1 if significant to P < 0.05 and –2 if significant to P < 10^–05, assessed by chi-squared test against a null hypothesis of a random distribution of these features across all genes assigned to a PhaeoNet merged module. The final column lists all metabolic pathways enriched to P < 0.05, or P < 10^–05 (asterisked) for each merged module, assessed by chi-squared test as above. Verbose outputs for each set of conditions are provided in Supplementary Figure 5. Additional annotations, e.g., enrichments in inferred evolutionary origins of each merged module, are provided for user exploration in Supplementary Table S2.

FIGURE 4

Main metabolic pathways and functional complexes of P. tricornutum plastid (left) and mitochondrion (right) and their composition in regard to PhaeoNet merged modules. Each square represents a gene encoding a protein identified either from N-terminal targeting predictions to function in the chloroplast or mitochondrion. Clusters of adjacent squares pertain to genes encoding different components of a specific multi-unit enzyme or complex; and split squares pertain to genes encoding functional homologs of one specific protein. The assigned merged modules are indicated as their respective colors, with the 16 most abundant merged modules shown in the legend. Additionally, proteins coded in organellar genomes (Oudot-Le Secq et al., 2007; Oudot-Le Secq and Green, 2011; Yu et al., 2018) are shown as dotted green or red; proteins for which chloroplast- or mitochondria-targeted isoforms or merged modules could not be assigned are shown as light gray; and enzymatic steps not identified in the genome are shown as light gray squares without borders. Dual-localized proteins (Gile et al., 2015; Dorrell et al., 2017) are marked by checkered yellow boxes; while orange boxes highlight potential connection points between the two organelles. Abbreviations are as follows: CAs, carbonic anhydrases; MEP/DOXP, mevalonate and non-mevalonate pathways for isoprenoid biosynthesis; SUF, iron-sulfur complex assembly; MPP,/TPP/SPP, mitochondrial, thylakoid and stromal processing peptidases; TAT, twin-arginine-dependent thylakoid protein import pathways; AOX/PTOX, mitochondrial and chloroplast alternative oxidases; TCA, Citric Acid cycle; Orn, ornithine; GCS, glycine shuttle; GS-GOGAT, glutamine synthetase/glutamate synthase shuttle. Detailed enzyme distributions for each pathway are shown in Supplementary Figure 6.

FIGURE 5

Phylogenetic and transcriptional dynamics of P. tricornutum sigma factors. This Figure shows an unrooted best-scoring tree topology for an 86 taxa x 453 aa alignment of subsampled diatom and non-diatom sigma factors and realized using MrBayes v 3.2.7a with the Jones substitution matrix, 600,000 generations, two start chains and 0.5 burnin thresholds (Huelsenbeck and Ronquist, 2001); and RAxML v 8.2 with the PROTGAMMAJTT substitution model with 300 bootstrap replicates (Stamatakis, 2014). Chloroplast-targeting predictions were performed using ASAFind with SignalP v 3.0 (Gruber et al., 2015); and HECTAR (Gschloessl et al., 2008) under default conditions. Branches are colored by phylogenetic affiliation and bootstrap values of nodes recovered with > 40% support are shown. Eight P. tricornutum sigma factors are labeled with PhaeoNet merged module repartition and chloroplast targeting sequences were predicted by HECTAR or ASAFind (Gruber et al., 2015).