Microbial Networks

A major aim of our research is to quantitatively understand the organization and molecular and physiological functions of cellular networks in microorganisms. As model systems, we investigate several networks that regulate key cellular processes and behavior in bacteria and yeast, primarily using E. coli and S. cerevisiae as well-established model organisms. We apply a range of modern fluorescence microscopy techniques, molecular biology, transcriptomics, proteomics, metabolomics, biochemistry to investigate the ability of protein networks to sense and integrate multiple stimuli, to reliably control cell behavior or physiology and to spatially organize within the cell. We further aim to better understand the relationship between the single-cell and collective behavior within microbial communities, including cell differentiation and the roles of chemical communication and physical interactions. Finally, we use experimental evolution to obtain insights into the selection pressure on cellular functions and their evolvability. In most of our work, we combine experiments with theoretical analysis and computational modeling, to elucidate principles that are common to various biological systems and to understand physics behind biology. Finally, we explore how cellular functions might be reengineered for potential applications and aim for an in-vitro reconstitution of essential cellular processes.

Environmental sensing and signal processing by bacterial chemotaxis networks

Most motile bacteria are capable of following gradients of various chemical and physical stimuli, such as nutrients, signaling molecules, pH, temperature, osmolarity and harmful chemical agents in their environment. This tactic behavior is highly beneficial in bacterial survival and competition for nutrients, but it also plays an important role in the formation of multicellular communities and colonization of animal hosts. We study how chemotaxis network of E. coli and other bacteria sense, process and integrate multiple environmental stimuli encountered in animal hosts and in aquatic or terrestrial habitats. Here we use Förster resonance energy transfer (FRET) to map and characterize the pathway response to a wide spectrum of stimuli, and complement it with microscopic analyses of bacterial behavior in chemical gradients established in microfluidic channels. 


Recent publications:
Bi, S., Jin, F., Sourjik, V. (2018) Inverted signaling by bacterial chemotaxis receptors. Nat Commun 9, 2927 [link]
Lopes, J.G., Sourjik, V. (2018) Chemotaxis of Escherichia coli to major hormones and polyamines present in human gut. ISME J 12, 2736-2747 [link]
Paulick, A., Jakovljevic, V., Zhang, S., Erickstad, M., Groisman, A., Meir, Y., Ryu, W.S., Wingreen, N.S., Sourjik, V. (2017) Mechanism of bidirectional thermotaxis in Escherichia coli. Elife [link]
Somavanshi, R., Ghosh, B., Sourjik, V. (2016) Sugar Influx Sensing by the Phosphotransferase System of Escherichia coli. PLoS Biol 14, e2000074 [link]

Bacterial collective behaviors

Collective multicellular behaviors represent an important aspect of microbial lifestyle. We investigate how various types of collective behavior, including biofilms, cellular aggregates and swarms, emerge from chemical and physical interactions between bacteria as well as from bacterial differentiation. We are also interested in the role played by bacterial viruses (bacteriophages) in the dynamics of microbial communities.

<strong>Chemotaxis-mediated autoaggregation of <em>E. coli</em></strong>
<p>Autoaggregation of motile <em>E. coli</em> that perform chemotaxis towards AI-2 secreted by initial seeding aggregates. Interactions between <em>E. coli</em> cells are mediated by autoadhesin antigen 43. Image sequence is acquired at 0.5 frames per second.</p>
Chemotaxis-mediated autoaggregation of E. coli

Autoaggregation of motile E. coli that perform chemotaxis towards AI-2 secreted by initial seeding aggregates. Interactions between E. coli cells are mediated by autoadhesin antigen 43. Image sequence is acquired at 0.5 frames per second.

Recent publications:
Colin, R., Drescher, K., and Sourjik, V. (2019). Chemotactic behaviour of Escherichia coli at high cell density. Nat Commun 10, 5329 [link]
Suchanek, V.M., Esteban-Lopez, M., Colin, R., Besharova, O., Fritz, K., and Sourjik, V. (2019). Chemotaxis and cyclic-di-GMP signalling control surface attachment of Escherichia coli Mol Microbiol, in press [link]
Laganenka, L., Sander, T., Lagonenko, A., Chen, Y., Link, H., and Sourjik, V. (2019). Quorum Sensing and Metabolic State of the Host Control Lysogeny-Lysis Switch of Bacteriophage T1. mBio 10 [link]
Laganenka, L., Sourjik, V. (2018) Autoinducer 2-Dependent Escherichia coli Biofilm Formation Is Enhanced in a Dual-Species Coculture. Appl Environ Microbiol 84 [link] 
Laganenka, L., Colin, R., Sourjik V (2016) Chemotaxis towards autoinducer 2 mediates autoaggregation in Escherichia coli. Nat Commun 7, 12984 [link]
Besharova, O., Suchanek, V.M., Hartmann, R., Drescher, K., Sourjik, V. (2016) Diversification of Gene Expression during Formation of Static Submerged Biofilms by Escherichia coli. Front Microbiol 7, 1568 [link]

Regulation and evolution of bacterial motility network

With their relatively simple cellular networks, bacteria are excellent models for understanding how network functions are adjusted to changing environments. We are interested both in network adaptation on the short time scale that is mediated by changes in gene regulation, and in the slower tuning of the network function in the process of evolutionary selection, using protein network that controls bacterial motility as a well-tractable model. Here we apply genomics, transcriptomics and proteomics combined with experimental evolution and computational analysis, to investigate how the network changes under short-term adaptation and long-term evolutionary selection.

Recent publications:
Ni, B., Colin, R., Link, H., Endres, R.G., and Sourjik, V. (2020). Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis. Proc Natl Acad Sci USA 117, 595-601 [link]
Laganenka, L., Colin, R., and Sourjik, V. (2020). Flagella-mediated mechanosensing and RflP control motility state of pathogenic Escherichia coli. mBio, in press. [link]
Rudenko, I., Ni, B., Glatter, T., Sourjik, V. (2019) Inefficient Secretion of Anti-sigma Factor FlgM Inhibits Bacterial Motility at High Temperature. iScience 16, 145-154 [link]
Ni, B., Ghosh, B., Paldy, F.S., Colin, R., Heimerl, T., Sourjik, V. (2017) Evolutionary Remodeling of Bacterial Motility Checkpoint Control. Cell Rep 18, 866-877 [link]

Biochemical noise in cellular networks

Biochemical reactions in cellular networks are inherently stochastic, which can lead to random variations in the network activity. Such noise can have profound effects on the networks’ ability to reliably perform their functions, but also result in beneficial variability among cells in a population. But whereas the origins and consequences of stochasticity in gene expression in microorganisms have been extensively investigated, the extent and importance of post-translational biochemical noise remain largely unknown. We established FRET assay that enables us to monitor activity of signaling and metabolic networks in individual E. coli cells, revealing surprisingly large fluctuations of the network activity. We aim to combine these measurements with mathematical modeling to elucidate origins and physiological consequences of biochemical fluctuation.

Recent publications:
Colin, R., Rosazza, C., Vaknin, A., Sourjik, V. (2017) Multiple sources of slow activity fluctuations in a bacterial chemosensory network. Elife 6 [link]

Chemotactic bacteriabots

We are exploring the use of chemotactic bacteria for delivery of the microscopic cargo. We developed a system for the rapid coupling of microscopic cargo to motile E. coli cells and demonstrated that resulting biohybrid bacteriabots remain motile and chemotactic, that being able to transport microscopic cargo in chemical gradients. We currently explore various potential application of this system.

<p><strong>Transport of microscopic cargo by a bacteriabot<br /><br /> </strong>Bacteriabot based on elongated (cephalexin-treated) motile <em>E. coli</em> cell, transporting a 2.2-µm polymethyl methacrylate (PMMA) microparticle.</p>

Transport of microscopic cargo by a bacteriabot

Bacteriabot based on elongated (cephalexin-treated) motile E. coli cell, transporting a 2.2-µm polymethyl methacrylate (PMMA) microparticle.


Recent publications:
Senturk, O.I., Schauer, O., Chen, F., Sourjik, V., Wegner, S.V. (2020). Red/Far-Red Light Switchable Cargo Attachment and Release in Bacteria-Driven Microswimmers. Adv Healthc Mater 9, e1900956 [link]
Schauer, O., Mostaghaci, B., Colin, R., Huertgen, D., Kraus, D., Sitti, M., Sourjik, V. (2018) Motility and chemotaxis of bacteria-driven microswimmers fabricated using antigen 43-mediated biotin display. Sci Rep 8, 9801 [link]
Alapan, Y., Yunus, O., Schauer, O., Giltinan, J., Tabak, A., Sourjik, V., Sitti, M. (2018) Erythrocyte-based Bacterial Microswimmers for Personalized Therapeutics. Science Robotics 3, eaar4423 [link]

In-vitro reconstitution and modeling of cellular processes

As a part of the MaxSynBio consortium aiming to construct an artificial protocell, we are working to reconstitute essential cellular processes in artificial membrane-enclosed compartments. Here our research focuses on the replication and segregation of genetic material, DNA and RNA. Besides reconstitution experiments, we also apply mathematical modeling to describe self-organization of cellular processes.

<p><strong>ParM-mediated bead segregation.</strong></p>
<p>Two beads coated by <em>parC </em>DNA and bound by ParR, segregated by a ParM spindle (labeled green with Alexa 488). Image sequence is acquired at 0.05 frames per second.</p>

ParM-mediated bead segregation.

Two beads coated by parC DNA and bound by ParR, segregated by a ParM spindle (labeled green with Alexa 488). Image sequence is acquired at 0.05 frames per second.


Recent publications:
Huertgen, D., Mascarenhas, J., Heymann, M., Murray, S.M., Schwille, P., and Sourjik, V. (2019). Reconstitution and Coupling of DNA Replication and Segregation in a Biomimetic System. Chembiochem 20, 2633-2642 [link]
Murray, S., Sourjik, V. (2017) Self-organisation and positioning of bacterial protein clusters. Nat Phys 13, 1006-1013 [link]

Spatial organization and diffusional properties of the bacterial cell

How proteins move in the highly non-uniform and crowded cellular environment is poorly understood, although this mobility is important in determining kinetics of multiprotein complexes assembly and rates of biochemical reactions. We investigate effects of size, charge and shape of proteins on their diffusion within bacterial cytoplasm, primarily using fluorescence correlation spectroscopy (FCS). We further study using fluorescence recovery after photobleaching (FRAP) how mobility, assembly and function of membrane proteins and their complexes depend on the lipid composition of bacterial inner membrane.  

Recent publications:
Pollard, A.M., Sourjik, V. (2018) Transmembrane region of bacterial chemoreceptor is capable of promoting protein clustering. J Biol Chem 293, 2149-2158 [link]

Pheromone communication in yeast mating

We study principles of pheromone communication during mating of unicellular eukaryotic microbe S. cerevisiae, as a simple model of mating behavior and cellular information processing. We aim to better understand what information is provided by the mating pheromones, and what is the physiological reasons for asymmetry between pheromones secreted by two different mating types of S. cerevisiae. Moreover, we investigate how this information is reliably transmitted by the MAPK signaling pathway in presence of cellular noise.  

Recent publications:

Anders, A., Colin, R., Banderas, A., Sourjik, V. (2021) Asymmetric mating behavior of isogamous budding yeast. Sci Adv, accepted
Banderas, A., Koltai, M., Anders, A., Sourjik, V. (2016) Sensory input attenuation allows predictive sexual response in yeast. Nat Commun 7, 12590 [link]

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