Back

Building the Marine Probiotic Platform: Developing a High-Throughput Process to Discover Coral Probiotics for Stony Coral Tissue Loss Disease

Ushijima, B.; Walter, M. K.

2026-01-11 microbiology
10.64898/2026.01.11.698879 bioRxiv
Show abstract

Stony coral tissue loss disease (SCTLD) is a deadly, waterborne coral disease. Since 2014, SCTLD has spread throughout Floridas Coral Reef and is now confirmed in 28 Caribbean countries and territories. The causative agent(s) remain unknown; however, pathogenic bacteria are implicated in disease progression. In contrast, a beneficial microbe (a probiotic) with antibacterial activity isolated from a disease-resistant coral has arrested SCTLD transmission and progression during laboratory and field trials. Due to host and environmental specificity, more probiotics sourced from vulnerable coral species and different SCTLD-impacted regions are needed. Conventional methods used for probiotic discovery include plating samples for single colonies, streaking for purification, and then testing strains for antibacterial activity with drop culture assays. However, these manually intensive methods are low-throughput and the measurements for antibacterial activity can be subjective. Given these constraints, this study developed a platform to greatly increase the isolation and screening of coral probiotics. Using the new platform, microbial cells were isolated from a coral mucus sample using a microfluidic cell sorter, and isolates were then screened for inhibitory activity against target pathogenic bacteria modified to express yellow fluorescent protein (YFP) that allows growth quantification using a microplate reader. In a single run using the platform, 433 isolates were sorted then individually screened against two target pathogens within six days. The workflow described herein represents a proof of concept for high-throughput environmental probiotic discovery with potential to push forward treatment development for SCTLD and future disease outbreaks.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.