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

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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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Parallel evolution on a Petri dish

How predictable can evolution be? This question is surely hard to answer. Even the smallest bacterial genomes have hundred of thousands of nucleotides that can be mutated and the genotypic space to explore can be huge. Moreover, even in a well controlled environment like a laboratory evolutionary experiment the selective pressures applied can be quite complex. Can we expect evolution to follow always the same path if we repeat experiments?

Recent work from our lab shows that the answer can be yes. We carried out experimental evolution using swarming motility in the bacterium Pseudomonas aeruginosa. Our experiments started with the wild-type strain PA14, which is mono-flagellated – it has a single polar flagellum. When we grow a colony of PA14 for 24 h on a Petri dish prepared with a minimal medium soft agar recipe, the colony forms a neat shape with a characteristic branched pattern. We passaged the bacteria to a new plate every day, and after only a few days we saw the bacteria evolve a different phenotype where they cover the entire plate. We call this phenotype “hyperswarming”.

hyperswarming_evolution

The hyperswarmer evolution experiment

P. aeruginosa becomes a hyperswarmer by getting a point mutation in certain residues of a gene called fleN. The mutation makes the bacterium, which is normally mono-flagellated, become multi-flagellated.

Perhaps even more interesting, when we repeated the experiment several times we always found hyperswarmers with point mutations in the same gene. This is surprising because there are many genes and pathways known to affect swarming motility and therefore there were potentially many paths open for evolutionary adaptation.

Our experiments show that evolution can be, to some extent, predictable. The swarming experiments are based on a simple, well-established, microbiology protocol. In a recent article in Trends in Microbiology we encourage microbiologists to think about the evolutionary implications of their experiments.

Read also the piece by Carl Zimmer in his column “Matter” at the New York Times, which includes videos of swarming colonies.

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Modeling microbiome dynamics

Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome, and revealing novel associations between microbes, health and disease. But most microbiome studies are cross-sectional and lack a mechanistic understanding of this ecosystem.

We developed a method to analyze dynamics of microbiome composition which accounts for time-dependent external perturbations such as antibiotics. The new method combines Lotka–Volterra models of population dynamics with regression techniques and can be used to predict ecosystem dynamics.

We demonstrate the model using data from mouse experiments and we show that we can recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions. The model suggests that a small group of commensal microbes protects against infection by the pathogen Clostridium difficile and explains how the antibiotic makes the host more susceptible to infection by perturbing the protective consortium.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota
Richard R. Stein*, Vanni Bucci*, Nora C. Toussaint, Charlie G. Buffie, Gunnar Rätsch, Eric G. Pamer, Chris Sander, João B. Xavier. PLoS Computational Biology
[Article: open access]

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Spatial structure in tumor microenvironment

Lactic acid accumulation in the core of solid tumors can be harmful to macrophages

Lactic acid accumulation in the core of solid tumors can be harmful to macrophages

Cancer cells have dramatic metabolic alterations that can give growth advantages but also cause changes in the extracellular environment, i.e. the tumor microenvironment. We used a multidisciplinary combination of computational and experimental methods to show that lactic acid accumulation can impair the survival of tumor-associated macrophages. We show using in vitro models that the decreased survival can lead to spatial patterns of macrophage localization resembling how tumor-associated macrophages distribute in real tumors. Spatial patterns can potentiate tumor growth, and thus understanding how they are formed may bring therapeutic insights.

Emergence of spatial structure in the tumor microenvironment due to the Warburg effect
Carlos Carmona-Fontaine, Vanni Buccia, Leila Akkari, Maxime Deforet, J. A. Joyce, and Joao B. Xavier. PNAS [PDF]

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Cooperation in microbes

Darwin’s theory of natural selection relies on the selfish survival of the fittest. Still, there are numerous examples of adaptive cooperative traits in nature. How can selection favor individuals that carry out costly actions for the benefit of others?
This question – how can cooperative traits evolve – is an old problem in evolutionary biology. Experimental microbiology is a growing source of models to tackle this problem.

P. aeruginosa swarming: a model for the evolution of cooperation

P. aeruginosa swarming: a model for the evolution of cooperation

In a recent article (Roditi et al, Molecular Systems Biology) we used swarming motility in the opportunistic pathogen Pseudomonas aeruginosa as a model for the evolution of cooperation. Laura Roditi, the first author, was the first PhD to graduate from our group and is now a postdoc with Manfred Claasen at the ETH in Zurich. The paper was also featured in a popular science article in the Brazilian science Instituto Ciencia Hoje (it’s in Portuguese, but a two year old can learn Portuguese).

Besides the fundamental implications to the evolution of cooperation, unveiling the mechanisms by which microbes stabilize cooperation can have implications to therapy – see this review:

Exploiting social evolution in biofilms
Boyle KE, Heilmann S, van Ditmarsch D, Xavier JB. Current Opinion in Microbiology.

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Systems Biology of Cell-Cell Interactions

Cells and organisms cope with the task of maintaining their phenotypes in the face of numerous challenges. Much attention has recently been paid to questions of how cells control molecular processes to ensure robustness. However, many biological functions are multicellular and depend on interactions, both physical and chemical, between cells. How do multicellular behaviors emerge from interactions among individual cells? What makes multicellular systems robust to the many challenges that they face?

Our lab investigates these and other questions using quantitative wet lab experiments and mathematical modeling. Our goal is to identify the underlying physical, biological, and evolutionary principles that are common among, and confer robustness to, multicellular systems.

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