In nature, bacteria primarily live in communities, specifically biofilms, which are surface-attached communities of cells embedded in an extracellular matrix. There are many advantages for the bacteria that adopt this communal lifestyle including adhesion to surfaces, resistance to antibiotics and predation, and collective processing of nutrient sources. From a human perspective, biofilms can be beneficial, e.g. in the context of waste-water processing and bioremediation. However, biofilms can also be problematic, e.g. biofilms cause major problems in medicine as they lead to chronic infections, and in industry biofilms foul surfaces and clog filtration devices. Because biofilms are three dimensional, heterogeneous, and rearrange over time, to date investigations have been limited to optical studies of biofilm formation when only a few cells are present or to gross characterization of the entire structure. We recently made a breakthrough, resolving individual cells in living, growing biofilms up to a depth of 30 microns, using customized spinning-disk confocal microscopy, fluorescent reporters, and automated cell-segmentation software. Biofilms can form clonally from a founder cell or by aggregation of many independent cells. In the first case, our analysis of a mature biofilm, grown from a single founder cell of the model pathogen Vibrio cholerae, revealed a striking transition during biofilm development from disordered cells to an orientationally ordered nematic state. In the second case, we found that autoaggregation of Escherichia coli relies on chemotaxis to a quorum-sensing signal produced, detected, and consumed by the cells themselves. Understanding these contrasting developmental processes and their ramifications for health and industry requires deeper mechanistic understanding. To this end, we will combine biophysical modeling with experiments to explore the role of cell-cell interactions, both physical and chemical, in the development of microbial communities. Experimentally, we will extend our studies of both V. cholerae and E. coli to include engineered signal and matrix-production mutants, and we will explore cellular heterogeneity within colonies using antibody labeling and fluorescent-reporter strains. On the theoretical side, our approach will combine agent- based and continuum models. Agent-based modeling will focus on single-cell behavior during ordering and aggregation processes. Continuum modeling, including a substantial extension of nematodynamics theory to describe 3D biofilm growth, will capture behavior over long distances and times. We expect the insights gained from this study and the modeling tools we develop to be applicable to bacterial community development over a wide range of organisms and conditions.

Public Health Relevance

We will investigate how cells of the model commensal bacterium Escherichia coli and the model pathogenic bacterium Vibrio cholerae form multicellular communities, the former by autoaggregation, and the latter by biofilm growth from a single founder cell. In each case, cell- cell interactions, both physical and chemical, play critical roles in community development. We expect the results of our study on these tractable model systems to reveal general principles of bacterial self-organization and to apply to a wide range of bacterial species, including major human pathogens.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM082938-09
Application #
9237580
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Resat, Haluk
Project Start
2008-09-01
Project End
2021-07-31
Budget Start
2017-09-01
Budget End
2018-07-31
Support Year
9
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Princeton University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08543
Tareen, Ammar; Wingreen, Ned S; Mukhopadhyay, Ranjan (2018) Modeling evolution of crosstalk in noisy signal transduction networks. Phys Rev E 97:020402
Ali, Md Zulfikar; Wingreen, Ned S; Mukhopadhyay, Ranjan (2018) Hidden long evolutionary memory in a model biochemical network. Phys Rev E 97:040401
Li, Sophia Hsin-Jung; Li, Zhiyuan; Park, Junyoung O et al. (2018) Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions. Nat Microbiol 3:939-947
Wasnik, Vaibhav; Wang, Hui; Wingreen, Ned S et al. (2017) Physical model of protein cluster positioning in growing bacteria. New J Phys 19:
Paulick, Anja; Jakovljevic, Vladimir; Zhang, SiMing et al. (2017) Mechanism of bidirectional thermotaxis in Escherichia coli. Elife 6:
Taillefumier, Thibaud; Posfai, Anna; Meir, Yigal et al. (2017) Microbial consortia at steady supply. Elife 6:
Ni, Bin; Ghosh, Bhaswar; Paldy, Ferencz S et al. (2017) Evolutionary Remodeling of Bacterial Motility Checkpoint Control. Cell Rep 18:866-877
Posfai, Anna; Taillefumier, Thibaud; Wingreen, Ned S (2017) Metabolic Trade-Offs Promote Diversity in a Model Ecosystem. Phys Rev Lett 118:028103
Bitbol, Anne-Florence; Dwyer, Robert S; Colwell, Lucy J et al. (2016) Inferring interaction partners from protein sequences. Proc Natl Acad Sci U S A 113:12180-12185
Laganenka, Leanid; Colin, Remy; Sourjik, Victor (2016) Chemotaxis towards autoinducer 2 mediates autoaggregation in Escherichia coli. Nat Commun 7:12984

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