Temporal and Spatial Variations in Transcriptional Patterns among Closely Related Marine Microbial Taxa

Irina N Shilova1, Julie Robidart2, Edward DeLong3 and Jonathan P Zehr1, (1)University of California Santa Cruz, Ocean Sciences, Santa Cruz, CA, United States, (2)National Oceanographic Centre, Southampton, United Kingdom, (3)University of Hawaiʻi at Mānoa, Department of Oceanography, School of Ocean and Earth Science and Technology, Honolulu, HI, United States
Abstract:
Marine microbial communities are complex, and even closely related marine microbial populations are genetically and physiologically diverse. Despite such great diversity, conserved and highly synchronized rhythmic transcriptional patterns have been observed in microbial communities worldwide. The current widely used approaches analyzing high-throughput sequence data from microbiomes are not designed to differentiate transcription at strain or ecotype level. We used a novel MicroArray-inspired Gene-Centric (MAGC) bioinformatics approach to discern daily transcription by individual strains in previously analyzed metatranscriptomes from two oceanic regions, California Current System and central North Pacific. The results demonstrated that marine microbial taxa (within cyanobacteria Prochlorococcus and Synechococcus, Alphaproteobacterium Pelagibacter and picoeukaryote Ostreococcus) have unique transcription patterns and respond differentially to variability in space and time in the ocean. For example, the timing of maximum transcription for the photosynthesis and pigments genes varied among Synechococcus strains in the California Current study, likely for optimizing light utilization based on their differences in genetics and physiology. While several Prochlorococcus genotypes were present in the North Pacific study, transcription of the phosphate transporter gene, pstS, in specific genotypes was negatively correlated with phosphate concentrations. These individual transcriptional patterns underlie whole microbial community responses and may be sensitive indicators of environmental conditions, including those associated with long-term environmental change. The MAGC applied here to ocean ecosystems is a promising complementary approach that can enhance the ability to analyze metatranscriptomic data from a variety of environmental microbiomes.