Predation, evo-devo, and historical contingency: A nematode predator drives evolution of aggregative multicellularity

This study demonstrates that predation by the nematode *Pristionchus pacificus* drives the evolution of *Myxococcus xanthus* aggregative multicellularity, revealing complex inter-trophic interactions and strong historical contingency influenced by antibiotic resistance.

The Gist

Imagine a tiny, microscopic world where bacteria are the main characters. In this story, we have three key players:

1. The Social Bacteria (Myxococcus xanthus): Think of these as the "middle managers" of the microbial world. They are predators that hunt other bacteria, but they are also prey for bigger animals. They have a superpower: when things get tough, they can stop being individuals and huddle together to build a giant, mushroom-shaped tower called a fruiting body. This tower helps them survive starvation and protects their "babies" (spores).
2. The Tiny Worm (Pristionchus pacificus): This is the "apex predator" or the "boss" of this specific food chain. It's a nematode (a tiny worm) that loves to eat the social bacteria.
3. The Common Bacteria (Escherichia coli): These are the "food" or the "grass" at the bottom of the chain. Both the social bacteria and the worm eat them.

The Big Experiment: A Microscopic Reality TV Show

Scientists set up a long-term experiment (like a reality TV show for bacteria) to see how the "middle manager" bacteria would evolve when faced with different threats. They created four different "seasons" or environments for the bacteria to live in for 20 generations:

• Season 1 (The Quiet Life): Bacteria alone. No food competition, no predators.
• Season 2 (The Buffet): Bacteria + E. coli (food). Plenty to eat, no predators.
• Season 3 (The Hunter): Bacteria + the Worm. No extra food, but a scary predator is hunting them.
• Season 4 (The Chaos): Bacteria + E. coli + the Worm. A full-blown ecosystem with food and a predator.

The Plot Twists

Here is what happened when the scientists looked at the bacteria after 20 generations:

1. The Worm Changes Everything
When the bacteria faced the worm (Seasons 3 and 4), they changed their strategy. Instead of building a few huge, impressive towers, they started building many, many small towers.

• The Analogy: Imagine a village under attack by a dragon. Instead of building one giant castle (which is hard to defend and takes forever), the villagers decide to build hundreds of tiny, quick-to-construct bunkers. If the dragon eats one bunker, the others are still safe. The bacteria learned that "safety in numbers" (many small groups) was better than "safety in one big group" when worms were around.

2. The Food Trap
When the bacteria had plenty of E. coli to eat but no worm (Season 2), they got lazy. They stopped building towers almost entirely and stopped making spores.

• The Analogy: It's like a family that suddenly gets an unlimited supply of pizza delivered to their door. They stop cooking, stop gardening, and stop preparing for winter because "why bother? We have food!" The bacteria forgot how to build their survival towers because they were too comfortable.

3. The Worm Saves the Day (Again)
Here is the most surprising part: When the bacteria had both the food and the worm (Season 4), the worm "saved" the bacteria's ability to build towers. Even though there was plenty of food, the presence of the worm forced the bacteria to keep building their towers.

• The Analogy: The worm acted like a strict parent. Even though the kids (bacteria) had a full fridge (food), the parent (worm) said, "You still need to do your chores (build towers) because I'm watching you!" The fear of being eaten overrode the comfort of having food.

4. The "Genetic Glitch" (Historical Contingency)
The scientists started some bacteria with a tiny genetic mutation (a "glitch" in their code that made them resistant to antibiotics).

• The Analogy: Imagine two groups of people trying to learn a new dance. One group is healthy; the other has a slight limp. When the music changes (the environment changes), the healthy group learns the new dance moves quickly and adapts. The group with the limp? They get stuck. They can't adapt to the new threats as well.
• The bacteria with the mutation didn't change much at all. They were "stuck" in their old ways. This shows that your starting genetic makeup (your history) can completely determine how well you can adapt to new challenges.

The Takeaway

This paper teaches us three big lessons about evolution:

1. Predators shape behavior: Being hunted forces prey to change how they live and reproduce. In this case, the fear of being eaten made the bacteria build more, smaller survival towers.
2. It's complicated: You can't just look at one thing. The presence of food and a predator together created a unique reaction that you wouldn't predict by looking at food or predators alone. It's a complex dance of interactions.
3. History matters: A tiny, random genetic change at the very beginning can lock a species into a specific path, preventing it from adapting to new threats later on.

In short, evolution isn't just about "survival of the fittest" in a vacuum; it's about how organisms react to their neighbors, their enemies, and the tiny genetic cards they were dealt at the start of the game.

Technical Summary

1. Problem Statement

The evolution of multicellularity is a major transition in biology, yet the specific selective drivers behind aggregative multicellularity (AM)—where independent cells come together to form a group—remain poorly understood compared to clonal multicellularity. While predation is a known driver for clonal multicellularity, its role in shaping the evolution of AM behaviors and morphologies (specifically fruiting body formation) in response to higher-order predators is unclear.

This study addresses three core questions:

1. How does predation by an apex predator (a nematode) influence the evolutionary trajectory of a bacterial mesopredator (Myxococcus xanthus)?
2. How do interactions between a basal prey (Escherichia coli) and an apex predator jointly shape the evolution of the mesopredator?
3. To what extent does historical contingency (specifically a pre-existing antibiotic resistance mutation) constrain or alter these evolutionary responses?

2. Methodology

Experimental System:
The authors constructed a tri-trophic food web in a laboratory setting:

• Mesopredator: Myxococcus xanthus (bacterium), capable of forming spore-filled fruiting bodies (FBs) upon starvation.
• Apex Predator: Pristionchus pacificus (nematode), capable of preying on M. xanthus.
• Basal Prey: Escherichia coli (bacterium), prey for both the nematode and M. xanthus.

Evolution Experiment Design:

• Duration: 20 cycles (approx. 20 weeks).
• Selective Regimes: M. xanthus populations were evolved in four distinct treatments:
  1. M: Monoculture (only M. xanthus).
  2. ME: Co-culture with non-evolving E. coli.
  3. MP: Co-culture with non-evolving P. pacificus.
  4. MEP: Co-culture with both E. coli and P. pacificus.
• Ancestral Genotypes: The experiment utilized two ancestral strains of M. xanthus:
  • GJV1: Wild-type.
  • GJV2: A spontaneous mutant with a single mutation in rpoB (conferring rifampicin resistance). This allowed for direct competition assays and testing for historical contingency.
• Protocol: Populations were passaged weekly on CFcc agar (amino acid-restricted medium). M. xanthus was harvested, treated with gentamicin to kill E. coli and heat to kill nematodes, and transferred to fresh plates.

Phenotypic Assays:

• Fruiting Body Morphology: Quantified using image analysis (Fiji) to measure:
  • Number of aggregates.
  • Aggregate size (area).
  • Average grey value (density/maturity).
  • Standard deviation of grey values (density heterogeneity).
• Spore Production: Direct counting of spores from harvested plates.
• Time-Lapse Imaging: 7-day monitoring of developmental trajectories to test for changes in the timing of development.
• Statistical Analysis: Principal Component Analysis (PCA), PERMANOVA, ANOVA, and morphological integration analysis (eigenvector variance) were used to assess trait evolution and correlations.

3. Key Results

A. Predation Drives Specific Morphological Evolution

• Apex Predator Effect: The presence of the nematode (P. pacificus) was the primary driver of evolutionary change in fruiting body number. Lineages evolved with nematodes (MP and MEP) produced significantly more fruiting bodies (approx. 855+ more) than those in monoculture or with prey alone.
• Morphological Shifts: Nematode-exposed lineages evolved fruiting bodies that were smaller, less dense, and less heterogeneous compared to ancestors.
• Prey Effect: Evolution with E. coli alone (ME) resulted in a drastic reduction in spore production (~90%) and smaller, less mature fruiting bodies, suggesting relaxed selection for sporulation when resources are abundant.

B. Non-Additive Interactions (Context Dependence)

• The effects of prey and predator were not additive. In the three-species treatment (MEP), the presence of the nematode prevented the evolutionary degradation of spore production and fruiting body density caused by E. coli alone.
• This indicates a complex, non-linear interaction where the apex predator mitigates the negative evolutionary effects of the basal prey on the mesopredator's developmental traits.

C. Historical Contingency (The rpoB Mutation)

• GJV1 (Wild-type): Showed robust evolutionary responses to all selective regimes, particularly the increase in fruiting body number in response to nematodes.
• GJV2 (rpoB mutant): Showed significantly reduced evolvability. While it evolved some changes in density, it failed to evolve the increased fruiting body number seen in GJV1 when exposed to nematodes.
• Conclusion: A single mutation conferring antibiotic resistance profoundly constrained the ability of the lineage to adapt to biotic selection pressures (predation), demonstrating strong historical contingency.

D. Developmental Timing and Integration

• Timing: The increase in fruiting body number in nematode-exposed lineages was not due to earlier initiation of development (time-lapse data showed similar onset times). Instead, it appears to be a change in the aggregation strategy itself.
• Integration: Evolution in multi-species communities (ME, MP, MEP) increased the morphological integration (covariance) of fruiting body traits compared to monoculture, suggesting selection acts on the developmental system as a coordinated whole.

4. Key Contributions

1. Empirical Evidence for Predation-Driven AM: This study provides direct experimental evidence that predation by a higher-order consumer drives the evolution of aggregative multicellular traits (specifically fruiting body number and morphology) in a bacterial mesopredator.
2. Tri-Trophic Complexity: It reveals that the evolutionary response of a mesopredator is not determined solely by its immediate predator or prey but by the non-additive interaction between them. The apex predator can rescue the mesopredator from maladaptive evolutionary trajectories caused by resource-rich prey environments.
3. Historical Contingency in Evo-Devo: The study highlights how a single pre-existing mutation (rpoB) can act as a "genetic filter," drastically altering the evolutionary potential of a population in response to ecological pressures. This suggests that antibiotic resistance may carry hidden costs regarding adaptability to biotic threats.
4. Decoupling of Traits: The results suggest that the aggregation step of multicellular development (forming fruiting bodies) and the differentiation step (spore production) can evolve independently, with aggregation being more responsive to predation pressure.

5. Significance

• Evolution of Multicellularity: The findings support the hypothesis that predation is a critical selective force in the origin and diversification of aggregative multicellularity, potentially explaining the diversity of fruiting body morphologies observed in nature.
• Food Web Dynamics: The research demonstrates that evolutionary changes in lower trophic levels (mesopredators) are deeply influenced by top-down control (apex predators), challenging simple pairwise predator-prey models.
• Antibiotic Resistance Trade-offs: The study adds a new dimension to the understanding of antibiotic resistance costs, suggesting that resistance mechanisms may limit an organism's ability to adapt to other ecological challenges, such as predation.
• Evo-Devo Framework: By linking specific ecological pressures to quantitative changes in developmental morphology and trait integration, the paper bridges the gap between ecological selection and developmental evolution.

In summary, this paper demonstrates that the evolution of complex social behaviors in bacteria is a dynamic process shaped by the interplay of predation, resource availability, and the historical genetic background of the evolving population.