The complex interplay between microalgae and the microbiome in production raceways

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Abstract Algae-associated microbiomes are underexplored, limiting our understanding of their influence on the productivity of large-scale microalgae reactors. To address this, we monitored microbial dynamics in two microalgae biomass production raceways over two 8-month intervals inoculated with Desmodesmus armatus . One reactor was fed with wastewater, while the other received clean water and fertilizers. Metabarcoding of the 18S and 16S rRNA genes revealed a high microbial diversity across two time series, showing thousands of eukaryotic and prokaryotic species growing alongside the microalgae. Chlorophyta and Fungi were the dominant eukaryotic groups, while Alphaproteobacteria, Gammaproteobacteria, Actinobacteria, and Bacteroidia dominated the prokaryotic communities. We found contrasting ASVs (Amplicon Sequence Variant) patterns between healthy ( D. armatus abundance >70%) and unhealthy ( D. armatus abundance <20%) microbiomes, across reactors and time series. Network analysis identified up to 10 potential ecological interactions among D. armatus and its microbiome, predominantly positive. Our results suggest a link between microbiome composition and D. armatus abundance. Specifically, ASVs associated with a healthy microbiome were positively correlated with D. armatus , while ASVs characteristic of an unhealthy microbiome were negatively correlated. Potentially pathogenic bacteria included Mycobacterium and Flavobacterium , whereas potentially beneficial taxa included Geminocystis, Thiocapsa, Ahniella and Bosea . Several fungal ASVs showed context-specific associations, whereas specific P. tribonemae , A. parallelum , A. desmodesmi , Aphelidiomycota sp., Rozellomycota sp. and, Rhizophidium sp. ASVs were identified as potentially harmful. This study reveals the striking diversity and complexity of microalgae-associated microbiomes within raceways, providing valuable insights for optimizing industrial production processes, particularly for wastewater treatment and sustainable green biomass generation. Abstract Figure Graphical Abstract. General overview of the metabarcoding methodology. Step 1: Sample collection, 2: DNA extraction of the samples, 3: PCR and DNA sequencing of the 18S and 16S rRNA genes, 4: Processing and quality filtering of the DNA sequencing data, 5: Taxonomic, community, and network analyses and, 6: Interpretation and discussion of the results.

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