BACTERIALLY SPEAKING: BONNIE BASSLER

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1. BONNIE BASSLER AND THE LANGUAGES OF BACTERIA

One of my problems when starting to think about bactorgs was to assess the plausibility of the science basis for that extrapolation. I wanted thus to look at what well respected, first class scientists were thinking about multicellular bacterial colonies and chemical signalling. Then I started reading the works of people like Hellingwerf, Kolter and Ben Jacob. Assessing their ideas the best I could and seeing where they were leading, I soon realized that here was a burgeoning yet important bud of science. The idea of communication and signalling in bacteria was rapidly becoming and is now a new paradigm.Today, you will meet another of these first class scientists studying "bacterial chat"...

Bonnie Bassler is a professor of microbiology in Princeton. She was the recipient of a Mac Arthur "Genius" award and is a member of the US National Academy of Sciences. She is thus a mainstream scientist and, from what I can see on the web, she is also a very energetic and kind person.

Her research is focused on disentangling the mechanisms of signalling and communication in bacteria. She started by looking at quorum sensing as classically defined and now proceeds to more advanced signalling (interspecies, with eukariots..). Clearly she goes a long way to reveal the mechanisms underlying the phospho-neural networks suggested by Hellingwerf. I have read as carefully as I can a few of her papers, She writes superbly. In this post, I will look in detail at one of the papers from her group (for more details, see the web page of Bonnie's lab in Princeton.

The paper I am referring to is by Stephan Schauder and Bonnie Bassler. It has been published in "Genes & Dev. 2001 15: 1468-1480". Download the PDF by clicking here.

Lets start with the title..... provocative but well supported by facts
Bacteria speaking.... a flavor of Bactorgs isn'it? Let's look at it more closely.

2) THE LANGUAGE OF BACTERIA
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Bonnie and Stephan discuss in detail many bacterial behaviors which lead them to believe that bacteria have sophisticated communication abilities. They also go at great lengths to explain the molecular and genetic mechanisms used by the bacteria to implement these communication capabilities.

The mechanisms they describe are very compatible with the ideas of Hellingwerf (see a previous post).. My goal in the current post is to see if we can look at the genetic regulations they describe in bacteria as analogs to neural networks susceptible to learn by evolving (i.e. in evolutionary time, not during the life of a bacterium).

However, Bonnie and Stephan say nothing about learning or adapting. More about that later when we will discuss a recent paper by Saeed Tavazoie and his associates. First let's look if we really have networks. This is the goal of the current post. Later we will see if these networks may adapt and learn.

I am not a cellular biologist nor a chemist. Thus I am not primarily interested in the chemical details of the various mechanisms nor in the exact nature of the various chemicals and genes involved. Moreover, even if I were, I would not understand these details. In what follows, I will just insist on the essentials: what are the behaviors and the general features of the mechanisms involved.

I have not tried to write a summary of Bassler and Schauder's paper. It is so well written that I would have made a abd job of it. Instead, I have collected some of their sentences in a series of excerpts. I have sometimes slightly modified them to suppress technical details and lists of references. I also changed a few words. I believe that I have not destroyed the meaning they intended to convey. I refer you to the original paper for further details.

You will find my modified excerpts in a sequence of blue panels hereafter. I link these panels by a few lines of text to tell you what I infer from them. I have also redrawn and slightly modified their figures. I believe that my panels and figures give to non specialists like myself a nice view of the current state of the art on bactorgs internal mechanisms. Try to imagine what we will know in ten years.

An introduction to quorum sensing:

Bonnie and Stephan start by summarizing what is quorum sensing:

THE LESSONS FOR WE SHARE: coordinated control of genetic expression in a multicellular community, control of many global behaviors, intra and interspecies communication, communication with eukaryotic cells, fight of other species against quorum sensing bacteria. All the basics I need for my Bactorgs are there.

Then Bonnie and Stephan give us a view of the early stages of the research on quorum sensing:


Thus, the evolutionary, functional importance of quorum sensing in V. Fischeri is clearly stated: to avoid the metabolical cost of producing light when it would be ineffective and only produce light it when it brings to the bacteria the clear advantage of protection in the host. It is also important to note that far from being limited to this species, quorum sensing is now known to be widely used by bacteria. It is clear from the beginning that if quorum sensing brings with it a large evolutionry advantage, it must have evolved in many bacterial species.

A network view of the basic mechanism of quorum sensing:

Here is a figure showing you the mechanism of quorum sensing in V fischeri.

LEGEND: The quorum sensing system of Gram negative bacteria. The LuxI protein makes the autoinducers (green pentagons) which then diffuse freely outside. Each bacterium doing the same, the concentration of external autoinducer is a measure of the size of the population(quorum). When the autoinducer concentration is high, it binds to a cognate receptor LuxR (cognate means having the same form and ad hoc characteristics to bind specifically to the molecule it receives). This is quorum sensing.
The complex auto inducer-Lux R then binds at target gene promoters and activate their effect (transcription) which has behavioral consequences.

The Lux-I Lux-R-gene expression pathway indicated here is just an example of the neural network-like pathways we discussed when we saw the work of Hellingwerf. If a bacterium has several quorum sensing pathways like the one above and if they share signals and communicate together, we have an Hellingwerf neural network analogue.

Quorum sensing in Gram positive bacteria

V fischeri is a Gram negative bacteria. As you know, Gram positive and negative bacteria have very different membrane properties (if you don't know this, look in Wikipedia). Hence the mechanism of quorum sensing in gram positive bacteria has to be a little bit different. However, it tells us very much the same story: signal, quorum, high density detection, gene activation. This confirms that quorum sensing gives an important evolutionary advantage to the bacteria using it. Indeed, it exists in almost all bacteria. Each species devised its own way to implement it (convergent evolution). Here is the mechanism in Gram positive bacteria.
Legend: A precursor peptide (the linked red pentagons) is produced by expressing a precursor locus on a gene. It is modified and an ATP-binding cassette (ABC) exporter secretes the end product peptide autoinducer (single red pentagons). It accumulates as a function of the size of the population. At high density (quorum sensing), the autoinducer is detected by a two component S-R system (acronym meaning signal –regulator or signal –response, take your pick).
As the name implies, this signal transduction system has two parts. A sensor protein (the little black bar S) recognizes and autophosphorylates (p) at a specific site (H). The phosphoryl group is transferred to a cognate response or regulator protein R which is then phosphorylated (D). The phosphorylated D binds to specific promoter genes (targets) to modulate the expression of the regulated genes.

Again, the similarity with Hellingwer's views are striking

Going further than the basic mechanisms: layered networks

Remember what Klaas Hellingwerf told us: in a single bacterium, several mechanisms are linked together to form a complex signal processing network, what he calls by analogy a phospho-neural network... Bassler and Schauder tell us very much the same story. Read the following excerpt:

They describe what is the beginning of a network: sequential steps, response to several signals (here from various species and even eukariots...), behavioral complexity. Bacteria can think in the same primitive sense that simple artificial neural network (Mc Culloch Pitts or PDP) can think (admittedly a rather limited definition of thinking but, as a starting point, it is not bad!). To read more about Mc Culloch and Pitts neuronal networks, click here, to know more about neural networks using Rumelhart's PDP approach, click here).

Remark that, like in Hellingwer's paper, thet do not say a lot about "crosstalk".

Speaking with prokariot and eukariot friends and foes: interspecies communication

One more step: it is nice to speak with your own kind but life is more complex than that. You need to dialog with ennemies and potential friends from other species (bacteria or eukariots). For instance, in a biofilm, many species of bacteria coexist. They cooperate or compete. They have to exchange all sort of signals like "I am a friend, I can give you this.." or "Beware, I can kill you.., look at this toxin". How do our bacteria achieve this?
Remark that they describe an exchange of signals at the community level or even among species. Moreover,their signals are what I called "tagged" a specific signal can only be seen by the bacteria having the proper receptors for it. It is all I required to build a "fluid neural network" or a "collective ant-like brain".

Here is a view of the mechanism, Bonnie and Stephan propose for V. harveyi. We will see the answer to the mystery question (see end of the blue panel) just afterwards.

Legend: The hybrid quorum sensing circuit of V. harveyi. .Elements characteristic of both Gram-negative and Gram-positive bacterial quorum sensing systems are combined.
An acyl-HSL autoinducer (AI-1, green pentagons) is produced by the activity of LuxLM. This is typical of Gram negative circuits. A second autoinducer (AI-2, red pentagons) is synthesized by the enzyme LuxS. AI-2 is proposed to be a furanone. Both autoinducers accumulate as a function of cell density. The sensor for AI-1 is LuxN, and two proteins, LuxP and LuxQ, function together to detect AI-2.
LuxN and LuxQ are regulator proteins that transduce information to a shared integrator protein called LuxU. LuxU sends the signal to the response regulator protein LuxO. The mechanism of signal transduction is a phosphorelay (denoted P). LuxO controls the transcription of a putative repressor protein (denoted X), and a transcriptional activator protein called LuxR is also required for expression of the luciferase structural operon (luxCDABE). The conserved phosphorylation sites on the two-component proteins are indicated as H (histidine) and D (aspartate).

This become more complex. I do not pretend to understand all of this but the message is clear: we see emerging a network associating the red and green messages. The node LuxU has all the connection characteristics of a two input logic processing node in a neural network. The exact nature of the computation done by that circuit is still a bit unclear.

Remember the pathways in Hellingwerf's paper. Some of them were associating several signals at some logical computing non linear nodes. Here they are.

Bonnie has thus found the perfect test system: V. harveyi. Why did this bacterium evolve such a complex network? How do other bacteria do? Here is what Bonnie says: A NOTE: Remark that, for "WE SHARE", another point should be developed: communication in biofilms. I will have to study a paper by Nadell and colleagues entitled "The evolution of quorum sensing in biofilms" (PLOS biology, January 2008, vol 6, Issue 1, p. 171 - 179). This is for another post.

A special case: communication with higher species

And finally, communication with higher species! Remember, bactorgs will infect humans and animals in order to defend themwelves against what they perceive as threats. However, the spectrum of infection will be wide, from lethal (no discussion between species) up to soft attacks, subtle influences on the brain (mainly the temporal lobe) and the reward/penalty system, lethal attacks, compromises and truces. This will need sophisticated two way communication between bacteria and higher species. Am I entitled to extrapolate in that direction?

Another reason for communications with the so-called higher species: I told you that, during their eons of evolution, bactorgs have enslaved many insects and small mammals just like the collective brains of ants and termites enslave some aphids. Bactorgs will use their slaves as messengers, weapons and spies in the outside world. Again, this will need a two way communication system between bacteria and the so-called higher species.

So, what does Bonnie tell us about communication with higher species?

First from higher species to bacteria:

And now from bacteria to their competitors (other bacteria) or to their hosts and preys (higher animals);Here are a few examples of bacterial strategies
What about eukariots
NOTE: I will have to write a post on toxin-antitoxin plasmid addiction systems (they are called "addiction modules", to see a paper on them, click here).

NOTE: One more points to look at: prisonners dilemma in bacteria (they have been documented in viruses...?) and more generally cheaters. I think that this might develop as an important theme in" WE SHARE".

Conclusion of the Schauder - Bassler paper

Really, I have all I need to say that Bactorgs are a valid hard sci fi extrapolation of what is currently known about bacterial multicellular systems. Considering that this kind of research is about ten years old, I feel entitled to extrapolate quite a bit. In WE SHARE, bactorgs will be alive, fit and kicking, thinking and speaking.

Here is the conclusion of Bonnie and Stephan's paper
Bonnie and one of her colleagues, Richard Losick, have written a more complete review of bacterial languages. It is mind boggling. I invite you to read it(it is in "Cell 125, April 21, 2006", click here to get it). I will certainly come back to it later, for the moment, the above excerpts should give you the essentials of what I think is needed to justify the bactorg idea. Here is a photo of the title of Bassler's and Losick review... You see, bacterial languages are with us to stay. Bactorgs are not unplausible. It is just a matter of knowing where I can place the limit.
A FEW MORE NOTES: I have now to make a list of all the extrapolations I envision for bactorgs in "WE SHARE". I have also to read more about Ben Jacob's work who studies isolated but wild cultures and put forward some highly speculative hypotheses about advanced communication and intelligence in bacteria. I have to make a synthesis of Hellingwerf, Bassler and Ben Jacob's work. What could be the language underlying Ben Jacob's organizations? Do we find fractal organizations in wild colonies and in biofilms? What is the true extent of the meanings conveyed by bacterial languages?

A NOTE ABOUT SIMULATING BACTERIAL COLONIES: Last but not least, at least from my own viewpoint as a researcher: over the last few years, I have developed a graphical modelling language for general kinetic systems at a population level (not at what is called an agent or individual-based level). I call my language "Kinetic Graphs or KG". I have implemented KG in a simulation package called 20 SIM which is a standard in electrical and mechanical engineering. I have adapted the 20-SIM graphical language which is called "bond graphs" to kinetic systems.
When, above, I said "generic" I was meaning that kinetic models are used in fields as diverse as chemistry, biology, ecology and even in resource modelling in management. My language, being generic, covers all these cases and I have developed demonstrators in each. I have taught KG at several universities (Technion Haifa, Ecole Polytechnique Fédérale de Lausanne, University of Lille and Kings College London).
I think that it is a very good language (but I am not neutral), forcing you to be accurate and rigorous while staying very intuitive and simple. Yet, as a generic language, it is not specifically optimised for genetic regulation although it may cover it. I think that, for circuits like those described above, it could be very nice and I intend to publish at some stage a few posts on it.

Just one more point on KG: They may, under some constraints, cover the case of networks which change their connections due to adaptation or learning. This is not easily done by other methods. Considering adaptive evolutionary learning in bacterial communities (Tavazoie, paper), we are led to networks like those described above but more complex and with adaptive connections. It could be a nice feature to have in modeling bacterial communication.

I am going to bed, I wish you a happy time.

Jack