When do bacteria decide to cooperate?

Bacteria don’t think since they’re just single celled organisms. But they do make decisions. Take the lac operon, arguably the best studied molecular system that enables an organism to adapt to changing conditions. When bacteria have plenty of a nutrient that they like, glucose, they don’t express the genes to eat up another nutrient that they like less, lactose. When glucose runs out, glucose starvation sends a signal as a series of molecular processes inside the cell that end in the expression of the genes in the lac operon. The enzymes encoded in those genes help the cell break down lactose into glucose and galactose and the cell is happy again.

The lac operon is a cellular decision-making system, but one that is pretty individualistic. When a cell makes a decision to express genes based on whether it lacks a nutrient, the decision affects that cell first and foremost (even though decisions to eat new sugars from the environment can always also affect others that compete for the same food). But bacteria can make decisions that are more social.

Mutants that don't produce surfactants (rhlA- here in green) swarm using the surfactants produced by wilt-type bacteria (in red)

Mutants that don’t produce surfactants (rhlA- here in green) swarm using the surfactants produced by wilt-type bacteria (in red)

In our lab we study how cells make decisions to cooperate with other bacteria. Bacteria have many collective traits like biofilms and swarming, which can only happen when there are many cells in a community. One thing in common for these traits to happen is that many individual bacteria need to come together and start producing substances that accumulate in the extracellular space. To build a biofilm, bacteria must secrete large amounts of polymers and make an extracellular matrix. In order to swarm, bacteria must secrete lots of rhamnolipid surfactants and lubricate the surface of a Petri dish. Each cell must spend resources to produce their individual share of a common good. Without these individual contributions the social trait could happen. Cheaters within the population (like the rhlA- strain in this picture) could take advantage of the public good without themselves contributing.

P. aeruginosa decides to cooperate only when they are in a crowd (quorum sensing) and have enough carbon source due to growth limitation by some other nutrient (iron, in this case)

P. aeruginosa decides to cooperate only when they are in a crowd (quorum sensing) and have enough carbon source due to growth limitation by some other nutrient (iron, in this case)

How can cooperating cells prevent cheating? One way is to invest in cooperation only when there are plenty of resources to do so. We uses quantitative experiments and mathematical modeling to analyze how bacteria do this. We saw that bacteria decide to express genes to make the surfactants needed to swarm when they have more than enough carbon rich nutrients, a process that we call metabolic prudence. However, they also count their neighbors using a process called quorum sensing. This way, each individual bacterium checks if it has enough nutrients and enough neighbors for swarming before cooperating. At any point in this process, if the bacterium gets starved too severely it shuts down cooperation, possibly to dedicate resources in preparing for survival.

Check out our paper, out this week in PLoS Computational Biology.

Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait
Kerry E. Boyle, Hilary Monaco, Dave van Ditmarsch, Maxime Deforet, Joao B. Xavier

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2015 lab mug

Here’s the 2015 lab MUG designed by Silja Heilmann. Great for warm coffee or tea on a cloudy New York Sunday.

labmug2015

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Shapes in math

Mathematical biology has always been obsessed with shapes, specially because many that look so complicated can actually be created with simple rules. Take the Turing patterns – these are regular shapes that obey the same type of rules but can make spots, stripes, labyrinths or holes by changing its parameters.

patterns

Turing-like patters in a model of biofilm, from Xavier et al (2009) Am Nat

Some years ago I read a paper by ecologists Rietkerk, Dekker, de Ruiter and van de Koppel that clicked for me. They explained that patterns in arid vegetation can have a simple explanation similar to Turing patterns. Plants in arid regions require moister (the growth limiting resource) and  benefit from having other plants close by because neighbors provide shade and reduce water evaporation from the soil. However, too many neighbors means there is less moister to go around. In their words “vegetation patterns are the result of fine-scale positive feedback and coarse-scale negative feedback.” This means that having a few neighbors close by is good, but too many neighbors is bad. They presented a very simple cellular automata model that could reproduce the patterns. The cellular automaton is like a simple iterative game where the elements on a square grid follow a simple set of rules at every turn. Every element in the cellular automaton does this:

  1. count the number of close neighbors and multiply that number by a positive value b, which produces the total benefit of having close neighbors provide shade (B)
  2. count the number of neighbors over a wider range and multiply by a negative value –a, which produces the total cost of having neighbors that compete for water (-C)

In the supporting material for their paper they show a convolution kernel:

convolution

The kernel is an intuitive way to think about the cellular automaton rule. A focal bush located at the center of the kernel (represented by *) “looks” at the space around it and adds the value “b” or “-a” depending on whether that patch of land is occupied by another bush. If the final result is greater or equal to 0 then the focal bush gets to live and play another round. If the value is 0 then the bush dies and that grid element becomes free. The model is explained in detail the supporting material of the paper by Rietkerk et al. But the really striking result is that this very simple model produces a diversity of shapes depending on the value of a and b. The patterns can be made larger by using a larger kernel and also by changing the relative sizes of the “good” and “bad” neighborhoods.

We recently applied a similar idea to explain branching patterns of swarming colonies. Branching is a different process, we think, because it happens as a population spreads in space, but the patterns may have an equally simple explanation. In our case, a colony of Pseudomonas aeruginosa spreads across a petri dish and branches along the way suggesting that having too many neighbors is bad. We did some experiments that show support for these rules. For example, if we put two colonies in the same petri dish they repel each other suggesting the long range negative feedback (see video). Our model and experiments show that like the patterns in arid vegetation the branching in swarming can be caused by a short range positive feedback and a long range negative feedback (see video of model SIMSWARM).

Read the paper
The ecological basis of morphogenesis: branching patterns in swarming colonies of bacteria
Deng P, de Vargas Roditi L, van Ditmarsch D, Xavier JB. New Journal of Physics [Article: open access]

The paper  has been selected to appear in the New Journal of Physics “Highlights of 2014” collection and recommended by Rob Palmer at the Faculty of 1000

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2014 Lab Mug

Before the year ends I have to hurry and upload the pictures for our official 2014 X-Lab MUG.
labmug2014preview

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Network inference helps identify mechanism of colonization resistance of gut microbiota

Colonization resistance is the ability of the commensal microbiota to prevent invasion by entheric pathogens. This ability can be compromised when patients take antibiotics – a fact that was known for many antibiotics. However, the mechanisms by which colonization resistance works and why antibiotics compromise that resistance remains poorly understood.
In a paper that just came out today, we report n a significant step forward in understanding how the human gut microbiota protects against Clostridium difficile, an important human pathogen and problem specifically for hospitalized patients. The work was carried out in the Pamer lab. With Vanni Bucci and Richard Stein we analysed mouse and patient time series data to infer network models using the Lotka-Volterra method published last year.

Read our paper
Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile
Buffie CG et al. Nature
[Online]

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NY/BIG conference: Friday, June 13th, 9:00am – 6:15pm at NYU

Image

Originally a meeting of Bacillus researchers located in the New York area, the NY/BIG has expanded to include other bacteria as well. This one day symposium also has speakers from beyond the NY area. Find more about it:

http://biology.as.nyu.edu/object/nybig_2014.html

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Predictive models of the gut microbiota

The primary function of intestinal microbiota seems to be to provide genes for metabolic reactions that are not available in the host’s genome. This enables us to process nutrients that would otherwise be unavailable to us. Another, perhaps secondary, function is to keep pathogens out. A healthy and biodiverse microbiota is resilient against invasion by invasive pathogenic bacteria such as Clostridium difficile and vancomycin-resistant Enterococcus (VRE). When we take antibiotics to cure an infection we may disrupt an ecological balance, compromising the microbiota’s resilience and opening the door for pathogens.
Microbiota resilience can involve ecological processes such as competition for nutrients, bacteriocin mediated bacterial warefare and the production of small molecules that stimulate the host to secrete antibacterial substances into the gut to harm competitor microbes. It should be possible to model these mechanisms with mathematics and build predictive computer models to help design antibiotic prescription regimens and minimize risk of disease.
In a review with Vanni Bucci we ask how far we are from such predictive models? Our conclusion is that we are probably far from a fully mechanistic, spatially structured model such as those used in environmental biotechnology. However, coarser grained models such as network inference and metabolic modeling are making great progress and may soon lead to the clinical applications.
This review is part of a special issue on the human microbiome in the Journal of Molecular Biology.

Microbiota dynamics of a bone marrow transplant patient

Microbiota dynamics of a bone marrow transplant patient

Read our review:
Towards predictive models of the human gut microbiome
Bucci V and Xavier JB. Journal of Molecular Biology [PDF]

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