Search bioRxiv⌕ Search

Biology subjects

Traver-Azuara, J.

Publications and source records attributed to Traver-Azuara, J..

2 recordsLinked to original sources

Temperature-driven dynamics of Chlorella sorokiniana and its microbiome

Temperature is a critical factor in microalgal production, yet its impact on microalgae and their microbiome remains poorly understood. Here, we investigated the responses of Chlorella sorokiniana and its microbial community to temperature increases in four wastewater-based cultures over two months. Two Test cultures were subjected to a thermal gradient from 20 {degrees}C to 34 {degrees}C, and two were Controls at 20 {degrees}C. Eukaryotic and bacterial communities were analyzed using 18S and 16S rRNA gene sequencing, and the microalga genome was reconstructed using PacBio HiFi sequencing and functionally annotated. Control cultures remained stable, whereas Test cultures showed a decline in C. sorokiniana relative abundance and biomass from approximately 24 {degrees}C. In contrast, photosynthetic efficiency remained stable until temperatures exceeded 33 {degrees}C. Above 22-24 {degrees}C, the bacterial community, dominated by Fontibacter, Flavobacterium, and Luteolibacter, was replaced by taxa whose relative abundance increased with rising temperature and microalgal decline, pointing to bacteria better adapted to the stress conditions. Candidate predator-prey dynamics with the protist Cercozoa also intensified as temperature increased. Microalgal genome annotation identified 28 candidate genes corresponding to seven of eight literature-informed high-temperature/heat-stress targets, primarily associated with protein homeostasis, Reactive oxygen species protection, membrane lipid remodeling, and protein degradation. Collectively, these genes likely support the microalga's response and adaptation to environmental stress. Overall, culture destabilization resulted from interacting thermal, microbial, and grazing-related processes rather than temperature alone. Early microbiome changes may therefore provide useful warning signals of culture instability and help improve the management of large-scale microalgal production systems.

microbiology↗

The complex interplay between microalgae and the microbiome in production raceways

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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/633910v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@dfa478org.highwire.dtl.DTLVardef@a6ce03org.highwire.dtl.DTLVardef@11f41f6org.highwire.dtl.DTLVardef@12397b2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract.C_FLOATNO 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. C_FIG

microbiology↗