Recent research on host-microbe interactions has focused on intimate sym- bioses. Yet transient interactions, such as the stimulation of animal metamorphosis by bac- teria, can have significant impacts on each partner. During these short-lived interactions, swimming animal larvae identify a desirable location on the seafloor and undergo meta- morphosis into a juvenile based on the presence of specific bottom-dwelling bacteria. While this phenomenon is critical for seeding new animals to establish or maintain benthic ecosystems, there is an ocean of fundamental questions that remain unanswered. Here, I propose an updated model of how bacteria stimulate animal metamorphosis based on evi- dence that bacteria inject a stimulatory protein that prompts tubeworm metamorphosis. I consider what we hope to learn about stimulatory bacterial products, how animals recog- nize these products, and the consequences for both partners. Finally, I provide examples of how studying an enigmatic host-microbe interaction can serve as an engine for scientific discovery.
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The Influence of Bacteria on Animal Metamorphosis
The swimming larvae of many marine animals identify a location on the seafloor to settle and undergo metamorphosis based on the presence of specific surface-bound bacteria. While bacteria-stimulated metamorphosis underpins processes such as the fouling of ship hulls, animal development in aquaculture, and the recruitment of new animals to coral reef ecosystems, little is known about the mechanisms governing this microbe-animal interaction. Here we review what is known and what we hope to learn about how bacteria and the factors they produce stimulate animal metamorphosis. With a few emerging model systems, including the tubeworm Hydroides elegans, corals, and the hydrozoan Hydractinia, we have begun to identify bacterial cues that stimulate animal metamorphosis and test hypotheses addressing their mechanisms of action. By understanding the mechanisms by which bacteria promote animal metamorphosis, we begin to illustrate how, and explore why, the developmental decision of metamorphosis relies on cues from environmental bacteria.
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- Award ID(s):
- 1942251
- NSF-PAR ID:
- 10203590
- Date Published:
- Journal Name:
- Annual Review of Microbiology
- Volume:
- 74
- Issue:
- 1
- ISSN:
- 0066-4227
- Page Range / eLocation ID:
- 137 to 158
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Pseudoalteromonas luteoviolacea is a globally distributed marine bacterium that stimulates the metamorphosis of marine animal larvae, an important bacteria–animal interaction that can promote the recruitment of animals to benthic ecosystems. Recently, differentP .luteoviolacea isolates have been shown to produce two stimulatory factors that can induce tubeworm and coral metamorphosis; Metamorphosis‐Associated Contractile structures (MACs) and tetrabromopyrrole (TBP) respectively. However, it remains unclear what proportion ofP .luteoviolacea isolates possess the genes encoding MACs, and what phenotypic effect MACs and TBP have on other larval species. Here, we show that 9 of 19 sequencedP .luteoviolacea genomes genetically encode both MACs and TBP. WhileP .luteoviolacea biofilms producing MACs stimulate the metamorphosis of the tubewormHydroides elegans , TBP biosynthesis genes had no effect under the conditions tested. Although MACs are lethal to larvae of the cnidarianHydractinia symbiologicarpus ,P .luteoviolacea mutants unable to produce MACs are capable of stimulating metamorphosis. Our findings reveal a hidden complexity of interactions between a single bacterial species, the factors it produces and two species of larvae belonging to different phyla. -
Neural learning rules for generating flexible predictions and computing the successor representationMemories are an important part of how we think, understand the world around us, and plan out future actions. In the brain, memories are thought to be stored in a region called the hippocampus. When memories are formed, neurons store events that occur around the same time together. This might explain why often, in the brains of animals, the activity associated with retrieving memories is not just a snapshot of what happened at a specific moment-- it can also include information about what the animal might experience next. This can have a clear utility if animals use memories to predict what they might experience next and plan out future actions. Mathematically, this notion of predictiveness can be summarized by an algorithm known as the successor representation. This algorithm describes what the activity of neurons in the hippocampus looks like when retrieving memories and making predictions based on them. However, even though the successor representation can computationally reproduce the activity seen in the hippocampus when it is making predictions, it is unclear what biological mechanisms underpin this computation in the brain. Fang et al. approached this problem by trying to build a model that could generate the same activity patterns computed by the successor representation using only biological mechanisms known to exist in the hippocampus. First, they used computational methods to design a network of neurons that had the biological properties of neural networks in the hippocampus. They then used the network to simulate neural activity. The results show that the activity of the network they designed was able to exactly match the successor representation. Additionally, the data resulting from the simulated activity in the network fitted experimental observations of hippocampal activity in Tufted Titmice. One advantage of the network designed by Fang et al. is that it can generate predictions in flexible ways,. That is, it canmake both short and long-term predictions from what an individual is experiencing at the moment. This flexibility means that the network can be used to simulate how the hippocampus learns in a variety of cognitive tasks. Additionally, the network is robust to different conditions. Given that the brain has to be able to store memories in many different situations, this is a promising indication that this network may be a reasonable model of how the brain learns. The results of Fang et al. lay the groundwork for connecting biological mechanisms in the hippocampus at the cellular level to cognitive effects, an essential step to understanding the hippocampus, as well as its role in health and disease. For instance, their network may provide a concrete approach to studying how disruptions to the ways neurons make and break connections can impair memory formation. More generally, better models of the biological mechanisms involved in making computations in the hippocampus can help scientists better understand and test out theories about how memories are formed and stored in the brain.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-micro-011320-012753
