Single-Molecule and Nanoscale Approaches to Biological Signaling

2.11 Single-Molecule and Nanoscale Approaches to Biological Signaling W D Hoff, Oklahoma State University, Stillwater, OK, USA ª 2011 Elsevier B.V. All rights reserved.

2.11.1 2.11.1.1 2.11.1.2 2.11.1.3 2.11.1.4 2.11.2 2.11.2.1 2.11.2.2 2.11.2.2.1 2.11.2.2.2 2.11.2.2.3 2.11.2.3 2.11.2.3.1 2.11.2.3.2 2.11.2.3.3 2.11.3 2.11.3.1 2.11.3.2 2.11.3.2.1 2.11.3.2.2 2.11.3.3 2.11.3.3.1 2.11.3.3.2 2.11.3.3.3 2.11.3.4 2.11.3.4.1 2.11.3.4.2 2.11.3.4.3 2.11.3.4.4 2.11.4 2.11.4.1 2.11.4.2 2.11.4.2.1 2.11.4.2.2 2.11.4.2.3 2.11.4.3 References

Proteins and Cells from a Nanomaterials Perspective Proteins as Nanomaterials Robustness of Proteins against Mutation Cells as Nanostructured Materials Biological Signal Transduction Single-Molecule Studies of Conformational Dynamics and Protein–Protein Interactions in Signaling Introduction to Single-Molecule Force Spectroscopy Single-Molecule Force Spectroscopy of Photoactive Yellow Protein: Anisotropy and Functional Conformational Changes Introduction to PYP Force spectroscopy of conformational changes during PYP signaling Force spectroscopy of anisotropy in the structural stability of PYP Single-Molecule Force Spectroscopy of the Transmembrane Signaling Complex of Sensory Rhodopsin II Introduction to SR Force spectroscopy of a transmembrane signaling complex Conclusions and general implications for the use of single-molecule force spectroscopy in studying the structural and functional properties of proteins Fluorescence Resonance Energy Transfer and Fluorescence Correlation Spectroscopy Approaches of In Vivo Signaling Introduction to FRET and FCS Using FRET to Probe Protein–Protein Interactions in Chemotactic E. coli Cells Introduction to chemotaxis signaling in E. coli Probing in vivo chemotactic signaling in E. coli by FRET FCS Approaches to Biological Signaling Using FCS to measure the concentration of signaling proteins in a single cell Correlating signaling protein concentration and responses of a single cell Conclusions and general implications for signal transduction Consequences of Thermal Noise for Biological Signaling Robustness of cellular behavior against thermal noise Molecular noise as a key element in chemotactic signaling Exploiting thermal noise for biological signaling: Competence in Bacillus subtilis Conclusions and general implications on the role of noise in biological signaling Subcellular Nanoscale Protein Clusters in Biological Signaling The Cytoplasm and Cytoskeleton of Bacteria Nanoclusters for Signaling in Bacterial Chemotaxis Nanoscale protein clusters in E. coli chemotaxis Introduction to chemotaxis in Rb. sphaeroides Nanoscale complexes of signaling proteins in Rb. sphaeroides Conclusions and Implications of Nanoscale Protein Clusters for Biological Signaling

288 289 292 292 293 294 294 296 296 298 299 301 301 303 304 305 305 306 306 308 309 310 310 311 311 311 312 313 315 315 315 316 316 317 318 319 319

287

288 Single-Molecule and Nanoscale Approaches to Biological Signaling

2.11.1 Proteins and Cells from a Nanomaterials Perspective This chapter considers biological signal transduction based on proteins and subcellular structures from a nanomaterials perspective. The majority of literature deals with proteins and cells from either a chemical/ biochemical or a cell biology standpoint. What would be the key aspects that a nanomaterials viewpoint would bring to biological signaling? Nanomaterials are generally defined as materials that have structured components at a length scale of 1–100 nm [1], and, as such, are intermediate between bulk materials and molecular structures. In films/coatings, the nanoscale structures are in a single dimension, nanowires are two-dimensionally nanostructured, and quantum dots are an example of a material that is nanoscale in all three dimensions. The structural features in the nanoscale length range cause the material to exhibit properties that are significantly different from the same material in its bulk form. Two key aspects of nanomaterials that can result in novel properties are a vastly increased surface/volume ratio and quantum effects. These properties depend on the size of the nanoparticles. As a result of the near-molecular size of nanoparticles and their vast surface/volume ratio, diffusion and thermal noise become important factors. In addition, the shape of nanoparticles and inter-particle distance and geometry can also affect the properties of the nanomaterials [2]. As discussed subsequently, nanometer-scale shape, diffusion, thermal noise, and quantum effects are all of great importance for the behavior of proteins and cells. This chapter aims to develop the following main themes. Science often progresses by developing testable predictions of phenomena based on extrapolations from directly observable events to the process under study. It is when these predictions fail that novel insights and concepts emerge, which then allow a deeper understanding. This chapter examines such failed expectations for the mechanisms that govern the function of proteins and cells, with emphasis on biological signal transduction. A natural basis for understanding the intricate mechanisms that operate in proteins and cells is to use macroscopic man-made machines, such as a car or watch, as a starting point. Based on this premise, cells and proteins are envisioned as highly miniaturized versions of such devices. It is directly relevant to this chapter that this is often the framework in which

the mechanisms that operate in cellular chemotaxis or motor proteins are explained, both in textbooks and, also to a large extent, in current literature. This viewpoint results in a number of strong and testable predictions regarding biological processes such as chemotaxis and the functioning of motor proteins. A first outstanding prediction is as follows. Like man-made devices, biological mechanisms are likely to fail catastrophically when fairly small modifications are made to the system. Such changes include changing the stoichiometry of the components in the system or altering the specifications of one of the components. In the example of a car, these two changes would translate into the following: introduce a random change in the number of wheels, axles, pistons, or fuel lines and examine if the car still runs; or change the size of some of the wheels or drive shafts; or make random changes in the electric wiring system. In the case of cells and proteins, the above changes would involve the following (using chemotaxis as an example): randomly alter the relative amount of the components in a signaling pathway, or introduce point mutations at random positions in the proteins that constitute the signal transduction pathway. Based on man-made devices, the prediction would be that in almost all cases the cellular mechanism would be greatly impaired or fully abolished. Remarkably, this prediction fails dramatically. In most cases, introducing mutations in a protein allows its function to proceed. Similarly, cells perform chemotaxis even when the concentrations of proteins in the signaling pathway vary significantly. Robustness of protein function against mutations is briefly summarized in Section 2.11.1.2, and robustness of chemotactic signaling is examined at greater length in Section 2.11.3.4. The second prediction that is directly relevant here is as follows. Like man-made devices, biological mechanisms function deterministically. Highly specific protein–protein interactions, signaling and regulatory mechanisms, enzymes, and bioenergetic pathways interact to achieve a specific functional process such as movement, growth, or cell division. Fundamental problems with this viewpoint surface when one considers the small number of molecules that perform key functions in the cell. This introduces a fundamental (intrinsic) noise in all cellular and subcellular processes (Section 2.11.3.4.1). In addition, a specific case is described of how a longstanding biological mystery in cell biology was solved when such stochastic events are taken into

Single-Molecule and Nanoscale Approaches to Biological Signaling

consideration (Section 2.11.3.4.3). In the case of molecular motors, a debate regarding the degree to which molecular motors function, using deterministic or stochastic mechanisms, is still ongoing (Section 2.11.1.1). The failure of these two predictions to describe biological systems at the cellular and molecular level indicates that these systems cannot (at least not fully) be understood using man-made deterministic devices as a framework of thought. Cells and proteins are not miniature versions of devices that we know. It appears that biological systems have evolved a fundamentally different way of assembling complex functional devices. The mechanisms (and their robustness) that govern biological biochemical networks, genetic regulatory circuits, and signaling pathways are currently an active and exciting area of research. The field of design and fabrication of man-made nanomaterials and nanodevices is in its infancy. Can we learn from biological nanoengineering principles? If yes, what useful insights in this area have been obtained from studies of biological systems? An examination of the mechanism of chemotaxis will reveal how cells use quite a small number of different components to obtain a highly sophisticated sensory and regulatory mechanism (Section 2.11.4.2.1). A key element will turn out to be the integration of functional interactions over many length scales, from 1A˚ to 100 nm. A highly influential recent development in biology is that tools have become available to quantitatively study single protein molecules and single cells. In a growing number of cases, this allows the direct observation of biological processes on the nanoscale, revealing an unsuspected richness of phenomena. Two of such key experimental approaches are discussed: single-molecule force spectroscopy (Section 2.11.3.2.1) and fluorescence techniques that allow protein concentrations and protein– protein interactions in single cells to be detected (Section 2.11.4.2). 2.11.1.1

Proteins as Nanomaterials

Proteins are unbranched linear polymers consisting of amino acids covalently linked together by peptide bonds. The length of proteins typically varies from 50 to 1000 amino acid residues. Twenty different amino acids are universally used by all organisms, from viruses to bacteria to humans, and it is the genetically encoded sequence of amino acids from

289

this 20 letter biochemical alphabet that determines the structural and functional properties of proteins. While the backbone of a protein is a repetitive, highly polar, and flexible chain, each of the 20 amino acids is characterized by a unique side chain. Proteins are synthesized by ribosomes in the cell, and emerge from the ribosome as an unstructured linear polymer. Subsequently, each protein molecule typically undergoes a protein folding process in which the polypeptide chain spontaneously folds into a unique three- dimensional structure. A typical range for the size of folded proteins is from 3 nm in diameter for small water-soluble proteins to 25 nm for the ribosome. The typical building blocks of proteins (-helices and -strands) have dimensions in the 0.5  1.5 nm range (Figure 1). However, the placement of groups, such as hydrogen-bonding donors/acceptors and proton donors/acceptors in the active site of proteins to an accuracy of within 0.01 nm, is of great functional importance. Thus, structurally important features of proteins extend from the nanoscale range to the molecular range. The physical chemical properties of the 20-aminoacid side chains vary strongly, and can be roughly grouped into three types: charged (Glu, Asp, Lys, Arg, and His), polar (Gln, Asn, Ser, Thr, Cys, and Tyr), and hydrophobic (Ile, Leu, Val, Ala, Gly, Phe, Trp, Met, and Pro). The properties of the side chains are of extraordinary importance in determining the structure and function of proteins. This is vividly illustrated by the two main types of proteins: watersoluble and transmembrane proteins. Water-soluble proteins expose mostly polar and charged side chains to the highly polar water solvent, while transmembrane proteins expose mostly hydrophobic side chains to the hydrophobic membrane lipid bilayer. From the viewpoint of nanostructured materials, proteins exhibit a number of remarkable properties. First, they undergo a self-assembly process in which the freshly synthesized extended, unstructured protein folds into a well-defined three-dimensional structure. During the folding process, the covalent structure of the protein remains unchanged; only its conformation is changed due to rotations over single (and sometimes double) bonds. The folded or native state of the protein usually is structurally highly defined. As a result, many proteins have been crystallized and their three-dimensional structure determined to atomic resolution. Subsequently, many proteins undergo a second stage of self-assembly with other protein molecules or RNA molecules

290 Single-Molecule and Nanoscale Approaches to Biological Signaling

(a)

4.4 Å

16.3 Å

(b)

4.3 Å

39.5 Å

(c)

Figure 1 Protein secondary structure: -helices and -sheets are the (sub)nanometer-scale building blocks of proteins. (a) A typical -helix in a protein (one of the -helices in photoactive yellow protein, see Figure 4). (b) Part of the -sheet in the porin from Rhobobacter capsulatus. (c) Side view of structure in (b). The structure of the -helices and -strands is shown schematically; the structure of elected side chains are shown in stick representation. Selected molecular dimensions of these structures are indicated in angstroms.

to form macromolecular complexes. The ribosome is a key example of such a complex, containing both RNA and protein molecules. In addition, proteins often form complexes that are involved in signaling, which is discussed in more detail in Section 2.11.4. While folded proteins have a well-defined molecular structure, their amino acid sequence is essentially aperiodic. This led Peter Privalov to state that

. . . A protein molecule, consisting of many thousands of atoms, participating in thermal motion is a macroscopic system. . . . The spatial organization of this macromolecular system is unusually well ordered. Every atom in the native protein occupies a definite place as in a crystal; but in contrast to a crystal a protein has no symmetry and no periodicity. . . Such an ordered aperiodic macroscopic system has never been dealt with before. . .[3]

Single-Molecule and Nanoscale Approaches to Biological Signaling

Thus, proteins are three-dimensional aperiodic nanostructured materials. The anisotropic nature of protein structure has recently become experimentally accessible through developments in single-molecule force spectroscopy, as discussed in Section 2.11.2.2.3. Most knowledge on protein structure and folding has been obtained on water-soluble proteins, resulting in the following view. While the solvent-exposed surface of the protein consists mainly of polar and charged residues, most hydrophobic groups are desolvated, and clustered in its highly hydrophobic interior. Thus, proteins can be viewed as exhibiting a nanoscale phase separation with a polar exterior and hydrophobic interior. To achieve this phase separation, a significant number of highly polar backbone atoms need to be placed in the highly hydrophobic protein interior. The resulting loss of many backbone–water hydrogen bonds would result in a large energy penalty that would prohibit protein folding. To solve this thermodynamic problem, essentially all backbone–water hydrogen bonds that are lost upon protein folding are recovered as intramolecular hydrogen bonds. This is largely achieved by the formation of backbone–backbone hydrogen bonds. It is these hydrogen bonds that form the two secondary structure elements of proteins (-helices and -strands). The resulting folded state of proteins turns out to be only marginally stable. A typical value of the free energy between the folded and unfolded state of a protein under physiological conditions is 10 kcal mol1. This corresponds to the free energy represented by two or three hydrogen bonds. Thus, while a typical folded protein contains a large number of intramolecular hydrogen bonds, its net stability is quite small. The hydrophobic effect caused by the desolvation of many hydrophobic side chains is widely viewed as the major factor contributing to the stability of proteins. On its own, however, this effect would result in proteins with an interior resembling an oil droplet [4]. It is specific hydrogen bonding and ionic interactions that cause the native state of a protein to adopt a well-defined three-dimensional structure of maximum stability. The marginal stability of proteins stands in contrast to the generally much higher stability of most man-made nanomaterials. The low stability of proteins may well be of great functional importance in allowing functionally important structural changes, and can also limit the lifetime of the functional state of the protein in applications. In line with the relatively low stability of the folded

291

conformation of proteins, the energy scale of most of biochemistry is low. The free energy of hydrolysis of adenosine triphosphate (ATP; the universal energy currency of biochemistry) under standard conditions is –7.3 kcal mol1. In addition, thermal fluctuations can be of great importance for understanding proteins. The conformational changes that occur during these thermal fluctuations have been described as equilibrium fluctuations, to distinguish them from functionally important motions, the conformational changes that occur during protein function [5]. While the function of many proteins critically depends on structural changes, the utilization of structural changes in man-made nanomaterials has only recently been initiated [6], and represents an exciting opportunity. The above description of proteins involves a series of hierarchical structural levels: primary structure (¼amino acid sequence), secondary structure (¼-helices and -strands), tertiary structure (¼packing together of -helices and -strands to create a hydrophilic surface and hydrophobic interior), and quaternary structure (¼multimerization of multiple folded macromolecules). These hierarchical levels reflect the assembly of proteins as nanomaterials: first, their primary structure is assembled by the ribosome; next, protein folding leads to the formation of secondary and tertiary structures; and, finally, multiple proteins (and RNA molecules) can assemble into a large, functionally active complex. The first key difference of nanomaterials when compared to bulk materials is the vastly increased surface/volume ratio. At nanometer length scales, diffusion is very rapid. This is directly relevant for biological signaling. Receptor proteins find the signaling molecules that they respond to through diffusion. Allosteric effectors find the enzymes whose activity they regulate through diffusion. In addition, the activated state of a receptor protein finds its downstream interacting protein through diffusion. Thus, diffusion is a key part of biological signaling. Biological signaling can involve factors that reduce the extent of diffusion process of interacting signaling partners. For example, the attachment of a signaling protein to the membrane by lipidation can facilitate its diffusional search for a transmembrane receptor protein that it interacts with. In Section 2.11.4, the role of clusters of signaling proteins is discussed. The second key factor in nanomaterials is that quantum effects can play an important role; quantum effects can play a crucial role in protein function as

292 Single-Molecule and Nanoscale Approaches to Biological Signaling

well. A well-understood and important example involves long-range electron transfer in proteins involved in photosynthesis and energy transduction [7]. Efficient tunneling of electrons through the protein matrix occurs over distances up to 1.4 nm [8]. A role for quantum effects in protein involved in biological signaling has not been extensively investigated. In view of the small dimensions of nanoscale objects, and, particularly, the relatively low-energy scale of most molecular processes in biology and the importance of diffusion, thermal noise has recently emerged as a key factor in understanding biological systems (Section 2.11.3.4). One instructive and important example deals with the mechanisms that molecular motors use to generate force. Biological molecular motors are proteins that utilize ATP hydrolysis (or the transmembrane proton gradient) to generate force. In muscle tissue, the protein myosin functions as the molecular motor that generates force by hydrolyzing ATP. Two quite different models for understanding force generation in such molecular motors have been proposed. The first model views myosin essentially as a traditional engine with nanoscale dimensions [9]. This nanoengine then uses ATP hydrolysis to drive the movement of -helices that function as pistons. The alternative view is based on diffusion in an anisotropic environment, and thus places great importance on the thermal noise aspect of molecular motors [10]. In this model, molecular motors function quite differently from traditional macroscopic motors and are much less deterministic. In the next section, an approach to the functioning of cells is discussed, indicating that a better understanding of the functioning of proteins and cells requires that we abandon the view that the mechanisms functioning in proteins and cells are miniaturized versions of macroscopic devices. 2.11.1.2 Robustness of Proteins against Mutation Man-made devices generally contain many interacting components that are precisely engineered to allow functional interactions between the different parts of the device. When one of these components is mechanically altered or damaged, in general, this will greatly reduce or completely abolish the overall function of the device. In addition, a device will only function if the correct number of components has been used to assemble the device. Recent results

have revealed that biological systems do not exhibit this high sensitivity. In contrast, biological systems exhibit robustness on a number of different levels [11]. Section 2.11.3.4 discusses robustness of cells against (and the functional exploitation of) thermal noise in the copy number of the components that they contain. Proteins exhibit a different type of robustness. Site-directed mutagenesis allows studies on proteins in which one amino acid has been deleted or substituted with a different residue. For a small number of proteins, extensive mutagenesis studies have been performed to evaluate the effects of a mutation at each of the positions of the protein. For lysozyme from bacteriophage T4, only 16% of 2015 different single-site substitution mutants significantly reduce the ability of the phages to form plaques [12]. Similarly, for -lactamase from Escherichia coli, substitutions at only 16% of the residues significantly impair the ability of this enzyme to confer resistance to antibiotics [13]. Such robustness is a general property of proteins [11]. Thus, the function of a protein, generally, will likely proceed unperturbed if one of the amino acids in the protein is altered. Apparently, a folded and functional protein has a high degree of built-in structural redundancy, in which almost all highly similar protein sequences will retain function. This robustness has important implications for the molecular evolution of protein function. 2.11.1.3

Cells as Nanostructured Materials

The size of typical cells is in the 1–100 mm range, placing it just outside the range of nanomaterials; however, ultramicrobacteria with cell diameters down to 0.2 mm have been well characterized and grow in the laboratory [14]. Functionally important structural features at the length scale between individual proteins and the entire cell are currently attracting strong attention [15,16]. This length scale is difficult to study in vivo by microscopy due to the diffraction limit of visible light. In the second half of this chapter, the role of subcellular protein aggregates in biological signaling is reviewed and discussed. The classical view of subcellular structure is that the cytoplasm in eukaryotes is highly structured, while the cytoplasm of bacteria and archaea is largely unstructured. Eukaryotic cells typically contain many membrane-enclosed compartments, including the nucleus, mitochondria, and the endoplasmic reticulum. The cytoplasm of eukaryotic cells contains a

Single-Molecule and Nanoscale Approaches to Biological Signaling

complex and dynamic cytoskeleton that provides mechanical strength and allows the directed movement of components (chromosomes, membrane vesicles, and organelles) within the cell. Recent results have shown that bacteria, in fact, also contain a complex and dynamic cytoskeleton [17,18,19], and that their proteins can be arranged in organized cytoplasmic clusters or membrane arrays, as discussed in Section 2.11.4. An important recent development in understanding cells is the realization that thermal fluctuations play an important role, both at the level of gene expression and cellular signal transduction. Since the small number of transcription events for most genes in a single cell are subject to thermal noise, significant variation in cell-to-cell concentration of specific proteins would be expected and has indeed been observed, even for genetically identical cells grown under identical conditions (Sections 2.11.3.3.1 and 2.11.3.4.3). The implication is that the molecular mechanisms of the cell need to be able to function in a range of different component stoichiometries. Similarly, the number of activated signaling molecules will vary stochastically, but will still need to trigger a biologically meaningful response. Such robustness is usually not present in man-made devices. These issues are explored in Section 2.11.3.4. 2.11.1.4

Biological Signal Transduction

Biological signal transduction occurs via signaling pathways (Figure 2). At the start of these pathways, a receptor protein detects the stimulus. This stimulus can be extracellular (e.g., a hormone or chemoattractant or light) or intracellular (e.g., a change in the

293

redox state of a key component [20,21,22] or guanosine 59-diphosphate 39-diphosphate (ppGpp) produced by ribosomes [23,24]). The receptor can be a transmembrane protein or a cytoplasmic protein; some extracellular signals (such as light or hydrophobic molecules) can traverse the cell envelop to reach the receptor. Upon interaction with the stimulus, the receptor is activated, and is thus converted to its signaling state. The activated receptor then interacts with one or more downstream interaction partners (or, signal transducers) to modulate their activity. A number of key activities that are thus modulated are a change in protein kinase activity and a change in the production of (or, for channels, permeability for) second messengers, such as Ca2þ or cyclic adenosine monophosphate (cAMP). These second messengers then diffuse through the cell and can allosterically affect the activity of proteins downstream in the signaling pathway. On a length scale of 5 mm, a signaling protein will diffuse rapidly, within 1 s. In the case of protein kinases, the phosphate group attached to the protein is then transferred from one protein to the next via a shorter or longer chain of proteins. When the phosphate group reaches the final component of the signaling pathway, this generates the output of the pathway, resulting in a cellular response. In some steps of the signaling pathway, two interacting components form a permanent protein complex (see, e.g., Section 2.11.2.3.2). In other cases, a protein (or second messenger) needs to diffuse through the cell to reach its downstream interaction partner. An example of this is the diffusion of CheY-P to the FliM protein in the flagellar motor (Section 2.11.3.2.1). In some cases, this diffusion

Signal Receptor

Receptor Flagellum Transducer

Regulator

DNA Figure 2 Typical elements in biological signaling. Depicted are the initial signal, its interaction with transmembrane and cytoplasmic receptors, signal relay to its transducer that diffuses through the cell and recognizes a downstream regulator protein by molecular recognition, and, finally, the interaction of this protein with the flagellar motor to affect motility or with DNA to affect gene expression.

294 Single-Molecule and Nanoscale Approaches to Biological Signaling

process occurs in limited compartments, for example, two-dimensional diffusion of components in the membrane. The formation and importance of nanoscale clusters of signaling proteins are discussed in Section 2.11.4. The two diffusing signaling components specifically interact through molecular recognition. These interactions then cause protein conformational changes that result in signal relay. In many cases, a single activated component in the signaling network can cause the activation of many downstream components. For example, an activated protein kinase can phosphorylate a large number of downstream proteins, which results in the amplification of the signal. In many cases, the signal transduction cascade does not form a single linear chain, but is branched, has feedback loops, and/or consists of multiple parallel signaling pathways. The transmission of a signal through such a network may be likened to the fluxes of metabolites through traditional biochemical pathways. Signaling by such networks, their emerging properties, and the effect of molecular noise in these networks are important areas of current research (Section 2.11.3.4).

2.11.2 Single-Molecule Studies of Conformational Dynamics and Protein–Protein Interactions in Signaling During the last decade, a veritable explosion has occurred in studies of macromolecules, such as DNA, RNA, and protein at the single-molecule level. These studies can be divided into singlemolecule detection (often by fluorescence) and single-molecule manipulation. A powerful class of molecular manipulation experiments measures the elongation caused by the unfolding of a single protein molecule upon the application of an external force. Such single-molecule force spectroscopy measurements offer unique advantages for probing complex biomolecules. Classical techniques in structural biology on bulk samples, such as X-ray diffraction and nuclear magnetic resonance (NMR) spectroscopy, yield rich structural information. Single-molecule force spectroscopy offers a combination of structural insights, obtained by mapping of the observed unfolding lengths onto known crystal structures, with energetic information derived from the force measurements.

2.11.2.1 Introduction to Single-Molecule Force Spectroscopy Single-molecule force spectroscopy involves the measurement of forces that are generated either by single molecules or by the detection of the response of proteins upon the application of an external force. Here, we focus on the latter type of experiments. Two important approaches to single-molecule force spectroscopy are the application of laser tweezers to trap single particles in the focal point of a laser beam, and atomic force microscopy (AFM), which involves immobilization of a sample on a surface and its investigation with an AFM tip. Optical tweezers have been used for studies of both protein folding and protein function [25,26,27,28], whereas single-molecule force spectroscopy based on an AFM has been largely limited to the process of unfolding in proteins [29,30,31,32]. More recently, AFM-based singlemolecule force spectroscopy has been used to probe receptor activation, as discussed in Section 2.11.2.2.2. Importantly, the AFM approach allows studies on the unfolding of both water-soluble and membrane proteins. The approach used in these experiments is outlined below, with an emphasis on AFM-based measurements. The forces needed to unfold a protein are in the piconewton domain [31], and those generated by molecular motors usually in the low piconewton range [28]. Accurate measurements in this force regime can be obtained by using devices that have correspondingly low spring constants. In the case of laser tweezers (also referred to as optical traps), the focal point of the laser beam provides a trap with a typical depth corresponding to 25 pN. In most experiments, a microbead with an appropriate index of refraction is used. The change in momentum transferred to the bead by the diffracted stream of photons in the focal point keeps the bead trapped. Thus, measurements of this type require the attachment of the biological molecule that is being investigated to a bead. The other end of the molecule is usually also attached to a bead, which is trapped either by a second optical trap or by a micropipette. The actual force measurement is performed by the detection of the movement of the bead away from the center of the trap. Instruments have been obtained that have a spatial resolution of 1 nm. In the case of AFM measurements, a cantilever with a piconewton force constant is used for the force measurements. In a typical experiment, the protein sample is attached to a clean flat surface, such as glass.

Single-Molecule and Nanoscale Approaches to Biological Signaling

Attachment of a protein to the surface often involves nonspecific association. The AFM tip is then moved toward the surface, and, subsequently, retracted by piezo elements. The force needed to move the ATM tip away from the surface is then measured by interferometry, using a laser beam reflected from the AFM tip. The stronger the force exerted on the AFM tip, the more it bends while it is moved away from the surface. When the spring constant of the tip is known from a calibration measurement, the extent of bending of the AFM tip can be converted into an actual force. This measurement therefore yields force–extension curves. When a protein is bound to the surface, and the AFM tip is also bound to this protein, retraction of the tip can yield information on the unfolding of the protein. In many cases, the association between the AFM tip and the protein is caused by nonspecific associations. Another approach is to coat the AFM tip with molecules that specifically bind to the molecule under investigation. Since the chance of association of the AFM tip is fairly low, an experiment typically involves many repeated approaches to/retractions from the surface. Force profiles that contain data on the molecule under

295

study then need to be selected. A complication in these measurements is that the AFM tip may be associated to either the protein or the glass surface. A second issue is that the orientation of the proteins that are bound to the surface is unknown and likely heterogeneous. These two problems have been fully addressed by the use of proteins with a repetitive, multimeric covalent structure. The first studies on the force-induced unfolding of proteins were performed with the protein titin from muscle tissue, with a naturally occurring multimeric protein consisting of a repetitive pattern of covalently linked subunits [29,25,26]. As a result of its repeating structure, force–extension curves of titin contain a series of force peaks. Each peak corresponds to the unfolding of a single protein domain, where the height of the peak corresponds to the force needed to unfold the domain (the unfolding force FU) and the distance between two consecutive peaks corresponds to the length of the fully unfolded domain (the unfolding length LU). This causes a characteristic sawtooth pattern in the force–extension curve (Figure 3). While the orientation of attachment of one domain in the titin molecule to the glass surface and another

4

3

2

4

1 2 3

200 pN

1

20 nm Figure 3 Measurement of a force–extension curve for the unfolding of a single protein molecule using an atomic force microscope (AFM). The individually folded domains in the protein are indicated, separated by short protein linkers. The molecule is bound to the glass surface on the left and to the AFM tip on the right. As the AFM tip is moved away from the glass surface, the tip will bend, allowing force measurements as a function of distance. Such measurements on single protein molecules typically involve a length scale of nanometers and force scale of piconewtons. The conformation of the protein molecule and the corresponding part of the force–extension curve are indicated at four specific stages of the measurement. Reproduced from Fisher TE, Oberhauser AF, Carrion-Vazquez M, Marszalek PE, and Fernandez JM (1999) The study of protein mechanics with the atomic force microscope. Trends in Biochemical Sciences 24: 379–384.

296 Single-Molecule and Nanoscale Approaches to Biological Signaling

domain to the AFM tip is unknown, the orientation of all intervening domains is highly defined: the force is applied from the N-terminal end of a domain to its Cterminal end. This largely resolves the problem of unknown tip and surface attachment geometry. In addition, the unfolding length LU usually corresponds directly to the number of amino acids in the domain that is being unfolded, with one residue contributing 0.38 nm to LU. This characteristic sawtooth pattern, with inter-peak distances corresponding to the number of residues in the protein domain under study, ensures that force–extension curves from protein unfolding events are observed, and essentially exclude contributions from glass–tip dissociation events. Most singlemolecule force spectroscopy measurements involve the detection of a large number of individual unfolding events. For each of these events, the FU and LU are extracted from the data, and the resulting values are plotted as histograms. The sawtooth force–extension curves consist of two basic phases (Figure 3). Initially, the multimeric protein attached to both the glass surface and the AFM is in a relatively compact conformation. The distance between the glass surface and the AFM tip is then increased, and the protein chain becomes fully extended. The force exerted on the protein then gradually increases to the point where one of the domains in the chain is unfolded and the force undergoes a sudden drop. Upon further tip movement, the entire domain then becomes fully extended again as the force gradually increases until the next domain unfolds. The last peak in such force–extension curves often occurs at significantly higher force, which is interpreted as the disruption of the attachment between the protein molecule and the AFM tip (or the surface). Thus, this final rupture event is usually excluded from the data analysis. To allow single-molecule measurements on unfolding of a broader range of proteins, it is proved to be possible to genetically engineer proteins with a multimeric beads-on-a-string structure. This has allowed measurements on a range of different proteins. In each of these proteins, the link between two consecutive domains consists of a link between the C-terminal residue of the first domain and the N-terminal residue of the next domain. An interesting additional degree of freedom was obtained by obtaining polyproteins through the introduction of two Cys residues at solvent-exposed positions through site-directed mutagenesis [33,34,35]. As explained in Section 2.11.2.2.3, this offers the

opportunity to exert a force over the protein along multiple different and well-defined axes. While various force regimes have been applied in single-molecule force measurements, most experiments employ one of two approaches. Many AFMbased single-molecule force measurements employ a constant rate of movement of the AFM tip while measuring the resulting change in force. In the second approach, a constant force is applied to the protein and the distance is changed in order to maintain this constant force. In such force-clamp measurements, changes in chain length during unfolding/refolding events can be observed in real time [36,37]. 2.11.2.2 Single-Molecule Force Spectroscopy of Photoactive Yellow Protein: Anisotropy and Functional Conformational Changes Photoactive yellow protein (PYP) has been used to obtain insights into biological signaling and the anisotropy of protein structure and energetics based on single-molecule force spectroscopy [34,38]. These results are discussed next. 2.11.2.2.1

Introduction to PYP PYP is a blue light receptor that was first identified in the halophilic photosynthetic proteobacterium Halorhodospira halophila [39,40]. It is regarded to be a prototype Per Arnt Sim (PAS) domain [41,42], a large and diverse family of sensory and regulatory proteins [43] that share a common three-dimensional structure [44]. PYP was the first PAS domain for which the three-dimensional structure was reported [45], and is the PAS domain for which the biochemistry and biophysics at the protein level are best understood [46]. The 125 residues that make up PYP are divided into two regions (Figure 4): (1) a typical PAS domain fold [44] with a central antiparallel six-stranded -sheet flanked by three -helices (residues 28–125; referred to below as the PAS domain core of PYP); and (2) two N-terminal -helices (residues 1–27) not present in most other PAS domains (referred to as the N-terminal region). The two N-terminal helices of PYP are packed against its central -sheet, forming a second, small hydrophobic core in PYP [45]. PYP functions as the photoreceptor for negative phototaxis in H. halophila [48]. H. halophila thrives in highly saline desert lakes that are often exposed to intense solar irradiation. Thus, H. halophila is at risk of

Single-Molecule and Nanoscale Approaches to Biological Signaling

297

Y42

(a)

pCA

E46

C5

(b) O

O2

C7

C1 C4

Cys69 S

C6

C9

C8

O– O1

C3

C2

(c) pG

(d) pB

Figure 4 Schematic representation of the structure of photoactive yellow protein (PYP). (a) The p-coumaric acid (pCA) chromophore of PYP in PYP is covalently bound to Cys69 via a thioester linkage. In the initial pG dark state of PYP the pCA is in the trans-configuration and its deprotonated phenolic oxygen forms two functionally critical active site hydrogen bonds to residues Tyr42 and Glu46. (b) The covalent structure of the pCA chromophore. PYP photoexcitation causes the trans- to cisphotoisomerization of the C7¼C8 double bond. (c) Overview of the three-dimensional structure of the pG state of PYP as determined by X-ray crystallography [45] with -helices indicated in red and -strands in yellow. The pCA and Glu46 are shown in stick representation. (d) Family of nuclear magnetic resonance (NMR) structures of the long-lived blue-shifted pB intermediate in PYP lacking its N-terminal 25 residues [47]. The pCA chromophore is indicated in stick representation. The pB intermediate is the presumed signaling state of PYP, and its formation involves a significant degree of partial protein unfolding. Bernard C, Houben K, Derix NM, Marks D, van der Horst MA, Hellingwerf KJ, Boelens R, Kaptein R and van Nuland NAJ (2005) The solution structure of a transient photoreceptor intermediate. Delta 25 photoactive yellow protein. Structure 13: 953–962.

298 Single-Molecule and Nanoscale Approaches to Biological Signaling

Figure 5 The photocycle of photoactive yellow protein. The configuration of the conformation and protonation state of the p-coumaric acid (pCA) chromophore, Tyr42, and Glu46 are schematically indicated. The approximate timescale of each thermal transition in the photocycle is indicated, together with the key molecular events that occur during each transition. Photoexcitation of the initial pG state results in the rotation of the pCA carbonyl group, and causes the ultrafast formation of the initial I0 photoproduct. This state proceeds through a number of successive intermediates: pR, pB9, and pB, before decaying back to the initial pG state. The absorbance maximum of each state is shown as subscripts, the isomerization state of the pCA as superscripts.

sustaining photochemical damage. Its negative phototaxis response may reduce this risk. PYP exhibits a photocycle [40,49,50] that is driven by its p-coumaric acid (pCA) chromophore [51,52] (Figure 5). The pCA is covalently attached to Cys69 [53] via a thioester bond [54]. In the initial pG dark state of PYP, the pCA is in the trans-configuration, while its phenolic oxygen is deprotonated due to its low pKa of 2.8 [39,55], down-shifted from the pKa of 8.8 for pCA in solution [56]. Light initiates the PYP photocycle by photoisomerization of the pCA [57,50], followed by pCA protonation from active site residue Glu46 [58,59]. The changes in electrostatic interactions caused by proton transfer from Glu46 to the pCA have been identified as a key mechanism in triggering large conformational changes [60,61,62,63,64,65,47] (Figure 4) during the protein quake that results in pB formation [59,66]). Spectroscopic evidence indicates that upon pB formation the N-terminal region of PYP dissociates from its PAS domain core, and becomes largely

unfolded [67,65,68]. The pB state is the likely signaling state of PYP. It spontaneously decays to the initial pG dark state of PYP in a few hundred milliseconds. The signal transduction chain leading from PYP to the flagellar motor has not yet been identified, and it is not known how partial unfolding during the photocycle helps signaling by PYP. More specifically, the signaling protein that interacts with PYP in H. halophila, and responds to the formation of the pB state, is unknown. 2.11.2.2.2 Force spectroscopy of conformational changes during PYP signaling

To convert the PYP from H. halophila, a small watersoluble protein, to a form that is amenable to singlemolecule force spectroscopy, two Cys residues were introduced at solvent-exposed residues. Two different biCys mutants were created to this end: the 45C/85C double mutant and the 36/128 mutant. These will be referred to as 45/85 PYP and 36/128

Single-Molecule and Nanoscale Approaches to Biological Signaling

PYP below. In the 36/128 mutant, a small C-terminal tail consisting of Ala–Ala–Cys was genetically added to the protein to ensure the solvent accessibility of the Cys residue. In wtPYP, only a single Cys is present (at position 69). The pCA chromophore is attached to this Cys residue, and it is fully buried in the native protein. The apoproteins of the two biCys mutants were prepared and the pCA chromophore was attached by incubation with the anhydride of pCA. Selective attachment of the pCA chromophore to Cys69 was observed, leaving the two introduced Cys residues free. Exposure of these protein samples to air resulted in their Cys-directed multimerization into polyPYP through intermolecular disulfide bond formation [34]. The multimeric nature of the proteins was readily detected both by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDSPAGE) (in the absence of reducing agents in the sample buffer) and by electrospray mass spectrometry. Multimers of up to 10 PYP subunits were observed. The resulting protein samples were subjected to force spectroscopy using a constant pulling speed. This yielded the characteristic sawtooth force– extension curves (Figure 6). In order to obtain information on structural changes during receptor activation in PYP, force–extension curves were measured for samples in the dark and samples exposed to blue light to photoaccumulate the pB intermediate. To allow this, the AFM was mounted on an inverted microscope and the optical path of the microscope was used to apply light from a diode laser to the protein sample in the AFM. Light intensities were used that convert the majority of the PYP molecule to their pB signaling state. The conversion of PYP to its pB state has two effects on the force–extension curves. First, the distribution of FU values was decreased by 30% for both pulling axes upon conversion of PYP from the pG to the pB state. Second, for both protein axes, photoexcitation results in an unexpected reduction in the LU by about 3 nm. These observations were interpreted as follows. The reduced FU of the pB intermediate matches its partially unfolded nature: since it is already partially unfolded, a smaller external force is sufficient to fully unfold it. A number of publications have focused on the unfolding of the N-terminal region of PYP (residues 1–27) [67,65,68]. Since all four of the Cys residues introduced in the two biCys PYP mutants are outside this N-terminal region, the AFM results indicate that the PAS core of PYP is also destabilized in the pB intermediate.

299

The 3-nm reduction in LU also matches the partially unfolded nature of the pB state, and indicates that the effective size of PYP in the pB state is increased by 3 nm compared to the native pG dark state. Thus, the change in length upon the unfolding of the pB state is reduced by 3 nm compared to the unfolding of the pG state. Since the diameter of the pG state of PYP is 3 nm, this indicates a twofold increase in the effective size of PYP in the pB state (Figure 6). It should be noted that the AFM measurements report the maximal size of PYP just before reaching the barrier for force-induced unfolding. It is expected that flexibility in the pB state is significantly increased so that the range of accessible conformations is larger. The interpretation of the reduction in LU is then that the accessible conformations with the largest dimensions along the pulling axis are longer than the pG state by 3 nm. Thus, the pB state resembles a rubber band: it can be stretched to twice its length before breaking. A similar reduction in LU was found for both the 36/128 axis and the 48/84 axis. Thus, the increase in flexibility in the pB state is isotropic. These results show that substantial loss of structural integrity occurs in the PAS domain core of PYP upon pB formation, complementing spectroscopic data indicating the partial unfolding of the N-terminal region of PYP. In general, the approach of using Cys-directed polyproteins allows the localization of conformational changes during function within a protein at the single-molecule level [45]. 2.11.2.2.3 Force spectroscopy of anisotropy in the structural stability of PYP

The use of Cys-directed polyproteins for force spectroscopy offers a powerful strategy to study the anisotropy of protein structural stability. This has important implications for increasing our understanding of protein folding. Computational approaches on simplified models of proteins have indicated that protein folding occurs on an energy landscape that resembles a rugged funnel [69,70,71]. However, it has been difficult to experimentally test this important computational result. What would be needed is an experimental approach that provides multiple reaction coordinates that are structurally well defined and that provide information on the free energy along each axis. Force spectroscopy of Cys-directed polyproteins can provide such an approach. The two Cys residues used to multimerize the protein precisely determine the axis along which

(a)

(c) 0.20

36/128

Dark Probability

Force (pN)

300 200 100

0.15 0.10 0.05

Light

0

0.00 0

150 200 250 300 350 Tip–sample distance (nm) (b)

(d)

80

48/85

48/85 0.20

Dark

40 20

Light

Probability

60 Force (pN)

10 20 30 40 Unfolding length ΔLdark (nm)

36/128

0.15 0.10 0.05

0 0.00 0

0

20 40 60 80 Tip–sample distance (nm)

(e)

10 20 30 40 Unfolding length ΔLlight (nm) ΔLdark

ΔL*

ΔLlight

48/85

36/128

Figure 6 Axis-dependent analysis of structural changes during receptor activation in photoactive yellow protein (PYP) by atomic force microscope (AFM)-based force spectroscopy. (a) Two different bi-Cys mutants of PYP were used to generate long polyPYP chains. The Cys residues were introduced at positions 36 and 128 in the first mutant, and at residues 48 and 85 in the second mutant. These two mutants were used to obtain force–extension curves in the dark (black lines) and under continuous excitation with blue light (blue lines). The dark measurements report on the pG dark state of PYP, while the measurements in the light detect the pB signaling state. The locations of these Cys residues define two different pulling axes. (b) Histograms of the unfolding length measured for 36/128 PYP and 48/85 PYP in the dark (black bars) and in the light (blue bars). Photoexcitation results in a reduction of the unfolding length by 3 nm for both pulling axes. (c) Interpretation of the reduced unfolding length upon pB formation. Since the pB state is partially unfolded, it is significantly more flexible than the pG state. The pB state needs to be extended by an additional 3 nm before it is ruptured and a force peak is detected in the force–extension curves. Reproduced from Zhao JM, Lee H, Nome RA, Majid S, Scherer NF, and Hoff WD (2006) Single molecule detection of structural changes during PAS domain activation. Proceedings of the National Academy of Sciences of the United States of America 103: 11561–11566.

Single-Molecule and Nanoscale Approaches to Biological Signaling

the elongation of the protein occurs. When the crystal structure of the protein under study is known, the initial state of the protein and the pulling reaction coordinate are well defined. For the two studied pulling axes in PYP, a significant difference in FU was found: 86 pN for the 36/128 axis and 66 pN for the 48/85 axis [34]. This reveals a significant degree of anisotropy in the mechanical stability of PYP. The measurements yield the force as a function of distance along these well-defined reaction coordinates. The integration of this force over the pulling distance provides the (nonequilibrium) work done on the system. The Jarzynski equality states that it is possible to obtain information on the free energy changes from the experimental nonequilibrium work data by averaging the nonequilibrium work over many randomly selected instances [72]. The implementation of the Jarzynski equality for single-molecule force spectroscopy [73] allows the conversion of this nonequilibrium work into a free energy profile [74]. This approach was successfully applied to force spectroscopy on the two PYP multimers described above [38]. The free-energy profiles for pulling along the two distinct axes in PYP, as derived from the force-extension curves, were found to be distinctly different. For the 36/128 axis, a transition state feature was detected in the free-energy landscape, while such a feature was absent for the 48/85 axis [38]. This reveals a strong anisotropy in the protein unfolding mechanism along these two axes. For the 36/128 axis, an all-or-none cooperative unfolding transition occurs, while unfolding along the 48/85 axis is a noncooperative, gradual process. To understand the molecular basis of this anisotropy, steered molecular dynamics (MD) calculations were performed. In steered MD, a classical all-atom MD calculation is performed [75,30]; however, in addition, a force is placed on two parts of the protein, in this case the two residues in PYP that were mutated to Cys. These calculations yielded three main results on PYP [38]. First, the calculated force–extension profile for 36/128 PYP reveals a clear force peak corresponding to the transition state for unfolding, while the force profile of 48/85 PYP is essentially flat. This corresponds well to the free-energy profiles extracted from the experimental data. Second, the unfolding trajectory along the 36/128 axis contains an abrupt transition at the force peak, both in terms of the root mean square deviation from the initial pG crystal structure and in terms of a sudden loss of

301

intramolecular hydrogen bonds. For 48/85 PYP, a gradual loss of structure and intramolecular hydrogen bonds is observed. Third, a structural origin for this difference in behavior can be identified. For 36/128 PYP, the pulling direction is along two adjacent -strands in the central -sheet of PYP. At the force peak, a further increase in distance requires these two -strands to slide, necessitating the rupture of all six hydrogen bonds between the two -strands. For 48/85 PYP, the pulling direction is essentially perpendicular to the strands in the -sheet, and force-induced unfolding proceeds via a gradual loss of intramolecular hydrogen bonds. This provides a molecular interpretation for the origin of the cooperative unfolding along the 36/128 axis. Anisotropy in three different types of properties has been reported in literature. First, significant anisotropy in the value of FU appears to be a general property of proteins [76,77,34]. Second, for green fluorescent protein (GFP), it has been found that unfolding along one axis involves a partially unfolded intermediate, while along the other axis no such intermediate is observed [78]. Thus, there is strong anisotropy in the unfolding pathway. Finally, for PYP, we have demonstrated that unfolding along one axis is cooperative, while along another axis it is not [38]. Thus, the fundamental nature of the unfolding transition can display anisotropy. 2.11.2.3 Single-Molecule Force Spectroscopy of the Transmembrane Signaling Complex of Sensory Rhodopsin II While PYP is the first water-soluble receptor protein for which the activation process has been studied by single-molecule force spectroscopy, sensory rhodopsin II (SRII) from the archaeon Halobacterium salinarum was used for the first force spectroscopic measurements of a transmembrane receptor protein [79]. In addition, these studies were performed on the complex of SRII bound to its signal transducer. These experiments also highlight fundamental differences between the folding of water-soluble proteins and transmembrane proteins [80]. 2.11.2.3.1

Introduction to SR H. salinarum is a halophilic archaeon that contains four distinct transmembrane retinal proteins. It lives in extremely saline lakes that are highly irradiated by sunlight. Its four retinal proteins allow H. salinarum to utilize the sunlight while avoiding its potentially damaging effects. The four retinal proteins in this

302 Single-Molecule and Nanoscale Approaches to Biological Signaling

SRI

Htrl

SRII

Htrll

Membrane Cytoplasm

HR

Methylation sites

CI–

his-kinase

his-kinase

BR H+

Phosphoregulator

Flagellar motor switch

Figure 7 The phototactic archaebacterium Halobacterium salinarum contains four different retinylidene proteins. Bacteriorhodopsin (BR) and halorhodopsin (HR) function as light-driven transmembrane ion pumps: BR pumps protons out of the cell and HR pumps chloride into the cell. In addition, H. salinarum contains two photoreceptors, sensory rhodopsin I (SRI) and sensory rhodopsin II (SRII). These signaling proteins regulate the rotation direction of the flagellar motor via associated signal transduction chains. SRI forms a complex in the membrane with its transducer HtrI, and similarly SRII interacts with its dedicated transducer HtrII. Interestingly, SRI and SRII are homologous to the methyl-accepting chemotaxis receptors in Escherichia coli (see Figure 9). HtrI and HtrII in their turn control the activity of a His kinase (CheA), which transfers phosphate groups to a phosophoregulator protein (CheY) that interacts with the flagellar motor upon its phosphorylation. Thus, the cell exhibits phototaxis responses to light absorbed by SRI and SRII. Note that the four retinylidene proteins share the same structure consisting of seven transmembrane -helices that enclose a retinal chromophore which is covalently bound to the protein via a protonated Schiff base. The two transducer proteins contain two transmembrane -helices and a large cytoplasmic domain that interacts with the His kinase. Reproduced from Hoff WD, Jung KH, and Spudich JL (1997) Molecular mechanism of photosignaling by archaeal sensory rhodopsins. Annual Review of Biophysics and Biomolecular Structure 26: 223–258.

organism are bacteriorhodopsin (BR), halorhodopsin (HR), SRI, and SRII [81] (Figure 7). BR and HR act as light-driven ion pumps that harvest light energy to drive the bioenergetics of the cell, while SRI and SRII are photoreceptors that control the swimming behavior of the cell. More specifically, BR is a lightdriven transmembrane proton pump and HR a lightdriven transmembrane chloride pump. SRI is the photoreceptor for an attractant response to orange light and a repellent response to near-ultraviolet (UV) light, and SRII triggers a repellent response to blue light. The signal transduction pathway leading from the sensory rhodopsins to the flagellar motor has been elucidated. SRI forms a complex with its dedicated transmembrane signal transducer HtrI, while SRII is complexed with HtrII. HtrI and II are homologous to the chemosensory methyl-accepting chemotaxis proteins (MCPs) that function in E. coli chemotaxis (see Section 2.11.3.2.1), and, like these MCPs, they control the activity of the associated water-soluble His autokinase CheA, which transfers phosphate groups to a conserved Asp in its associated response regulator CheY. Thus, light stimuli cause

changes in the phosphorylation level of CheY, which control the interaction between CheY and the flagellar motor switch, thereby controlling cell motility. The photochemical activity of all four retinylidene proteins is caused by the photoisomerization of a retinal chromophore. This retinal molecule is covalently attached to a conserved Lys side chain in the protein, forming a protonated Schiff-base linkage. The retinal is fully embedded in the seven-transmembrane helical structure that the four proteins share. These helices are labeled A–G. The mechanism by which retinal isomerization triggers transmembrane ion transport in BR and HR and receptor activation in SRI and SRII is understood to a high level of molecular detail [82,81,83]. In summary, retinal isomerization causes an internal proton transfer process that results in the transient deprotonation of the Schiff base during the formation of the S373 photointermediate. This is associated with significant structural changes in the protein (see below). In the final stages of the photocycle, thermal re-isomerization of the retinal, reprotonation of the Schiff base, and resetting of the protein conformation take place.

Single-Molecule and Nanoscale Approaches to Biological Signaling

A key step in the photocycle of SRII is proton transfer from the protonated Schiff base to the negatively charged side chain of active site residue Asp73. These residues form a salt bridge in the dark state of the protein, and this salt bridge is thought to be a critical factor in restraining the conformation of the protein. Light-induced retinal isomerization triggers proton transfer from the Schiff base to Asp73, which neutralizes both groups, thus abolishing the salt bridge that links them together. This contributes significantly to causing the protein structural changes that activate the receptor protein. In the D73N mutant of SRII, the salt bridge is permanently disrupted, and this causes the receptor to be constitutively active [84]. Interestingly, the corresponding mutation in the visual pigments of the human eye causes impaired vision. Photoexcitation of SRI causes an attractant cellular response. This is thought to be beneficial to the cell by resulting in the accumulation of cells in illuminated areas where BR can perform transmembrane proton pumping. In addition, when the S373 intermediate of SRI is photoexcited by near-UV light, the cell exhibits a repellent response. Thus, cells avoid illuminated areas that also contain a substantial amount of near-UV light, minimizing photodamage. This mechanism allows a single photoreceptor protein (SRI) to trigger two distinct photoresponses that provide the cell with a color discrimination mechanism [85]. SRII is a repellent photoreceptor that is expressed when BR and HR are not present in the cell; under these conditions, cells avoid illuminated areas. A series of experiments have allowed the identification of the photocycle intermediates in the photocycles of SRI and SRII that act as signaling states in the living cell. These experiments involved the use of H. salinarum cells lacking the capability of retinal synthesis. These cells, therefore, do not exhibit photoresponses. The addition of retinal to these cells restores phototaxis. By adding a series of different retinal analogs, the kinetics of various photocycle steps can be differentially altered, as revealed by flash photolysis. In addition, the light sensitivity of the cells reconstituted with these analogs was altered. By correlating the lifetime of the various photocycle intermediate with the sensitivity of the cells, those intermediates that generate phototactic signals in the cell were identified for both SRI and SRII [86,87]. A number of mutants have been discovered in SRI and HtrI that result in responses to light that are inverted compared to those observed in the wild-

303

type system [88,89,90]. The current interpretation of these inverted signaling mutants is that in the wild-type protein a thermal equilibrium exists between two conformations of the protein. The formation of the S373 state shifts the equilibrium in the direction of the attractant protein conformation, while the two-photon photocycle causes a repellent response by shifting the conformational equilibrium in the opposite direction. Important parallels in the mechanism of proton pumping and receptor activation have been uncovered. Interestingly, removal of HtrI uncovers transmembrane proton pumping activity in the now-uncomplexed SRI [91]. In all four proteins, retinal isomerization triggers deprotonation of the retinal Schiff base and movement of -helices, particularly helices F and G. These helix movements have been studied by a range of experimental techniques, including site-directed spin labeling combined with electron paramagnetic resonance (EPR) spectroscopy [92], X-ray crystallography [93], and a range of spectroscopic methods [83]. In addition, many aspects of the structural basis for the interactions between SRII and its signal transducer have been revealed [94,95,93]. Recently, the SRII/ HtrII complex was used for the first single-molecule force spectroscopy study of the interactions within this signaling complex [79], as discussed in the following. 2.11.2.3.2 Force spectroscopy of a transmembrane signaling complex

For single-molecule force measurements, purified SRII/HtrII complexes or purified SRII were adsorbed to mica, and the AFM tip was pushed onto the membrane [79]. This causes nonspecific adsorption of the tip to parts of the protein that are solvent exposed. In the case of the SRII/HtrII complex, a 23-residue extension at the C-terminus of SRII was found to be the most common tip attachment point. Subsequent retraction of the AFM tip, away from the surface, yields force–extension curves containing five consecutive peaks separated by characteristic distances (Figure 8). These experiments reveal that the unfolding of a single transmembrane protein occurs in distinct phases. This is in line with earlier single-molecule force spectroscopy measurements on BR [96]. It proved possible to map the observed force peaks on the structure of SRII based on the distance between these peaks, revealing a clear correlation with the secondary structure of the protein (Figure 8).

304 Single-Molecule and Nanoscale Approaches to Biological Signaling

AFM tip COOH Transducer

G

F

E

D

C

B

Membrane

Sensory rhodopsin

A

II

I NH

Co ile coil d

NH COOH

Force

Single-molecule force–distance curve

Length Figure 8 Analysis of interactions in the SRII–HTRII transmembrane signaling complex by force spectroscopy. Interactions between single molecules of SRII and its transducer HtrII were examined by force spectroscopy [79]. Differences in the force– extension curves for SRII in the presence and absence of HtrII were measured. The seven transmembrane -helices of SRII (A–G) and the two transmembrane -helices of HtrII are indicated. The atomic force microscope (AFM) tip preferentially attaches to the C-terminal extension of SRII. Peaks in the single-molecule force–extension curves were found to correspond to specific secondary structure elements in SRII, as indicated in color. The two force peaks that were increased in the presence of StrII are indicated by the two vertical arrows. Interestingly, these same two regions of SRII (helices F and G) undergo light-induced conformational changes. This indicates that HtrII strongly interacts specifically with those regions of SRII that undergo large light-induced conformational changes, thus allowing signal transfer from SRII to HtrII. Reproduced from Hoff WD and Spudich JL (2008) Single molecule tour de force: Teasing apart a signaling complex. Structure 16: 1149–1150.

By comparing the force–extension curves of SRII in the absence and presence of its signal transducer, HtrII information on the interactions between these two signaling partners was obtained. Interestingly, the most significant changes in the mechanical properties of SRII were observed in helices F and G (Figure 8). This matches X-ray crystallographic information on the SRII/HtrII complex [93], and supports the notion that light-induced movement of these helices is involved in signal relay from SRII to HtrII. 2.11.2.3.3 Conclusions and general implications for the use of single-molecule force spectroscopy in studying the structural and functional properties of proteins

Single-molecule force spectroscopy based on AFM or optical tweezer technology has rapidly developed into a rich strategy for investigating the (sub-)nanoscale mechanical and energetic properties of proteins. It allows studies on the mechanical unfolding of

proteins with piconewton sensitivity and sub-nanometer resolution. The use of Cys-directed monomers permits single-molecule force spectroscopy on proteins that are normally monomeric, and provides a structurally well-defined reaction coordinate. Analysis of the resulting data, based on the Jarzynski equality, can provide information on the free-energy surface for unfolding along this reaction coordinate, and steered MD simulations can provide structural insights on key events during mechanical unfolding at near-atomic resolution. Measurements on a series of polyproteins, in which the Cys residues are placed at different positions, allow measurements on the structural anisotropy of proteins. These measurements have revealed that the mechanical properties of proteins are complex and can be highly anisotropic. By performing single-molecule force spectroscopy on a set of biCys mutants of a protein in different functional states, conformational changes can be mapped to different regions of a protein. For PYP, this approach was used to demonstrate that light activation results

Single-Molecule and Nanoscale Approaches to Biological Signaling

in partial unfolding in its PAS domain core. Measurements of a protein in different functional complexes can provide information on the location of protein–protein interactions. This approach was used to study the SRII–HtrII signaling complex. Comparison of the force–extension curves of the water-soluble protein PYP and the transmembrane protein SRII reveals fundamental differences in the energetics of folding for these two classes of proteins. In PYP, a single (cooperative or noncooperative) transition is observed, while in SRII the extraction of various transmembrane -helices from the membrane causes distinct force peaks for the unfolding of a single protein.

2.11.3 Fluorescence Resonance Energy Transfer and Fluorescence Correlation Spectroscopy Approaches of In Vivo Signaling While biological mechanisms and phenomena are driven by events at the molecular scale, until recently, the detection of these events in living cells has largely remained inaccessible due to the diffraction limit of visible light, which is approximately two orders of magnitude coarser than the molecular scale. Thus, many questions and phenomena that occur in the 3–300 nm length scale are difficult to study in vivo. This is exactly the length scale of nanoscience. Many novel phenomena occurring at these length scales are being discovered (e.g., see Ref. [16]). Recent developments in microscopic techniques are beginning to break down these limitations [97,98], and increasingly allow rapid imaging of living cells with a spatial resolution of 25–75 nm. What follows is a discussion of the approaches based on fluorescence resonance energy transfer (FRET) and fluorescence correlation spectroscopy (FCS) for studying signal transduction in living cells with high spatial and temporal resolution. 2.11.3.1

Introduction to FRET and FCS

In FRET, a pair of distinct fluorophores is involved in which the fluorescence emission spectrum of the donor molecule has significant spectral overlap with the absorbance spectrum of the acceptor molecule. The primary use of FRET in biochemistry is to measure the distance between the donor and acceptor molecules. If the distance is short, the efficiency of Fo¨rster energy transfer from the donor to the

305

acceptor will be high. In this case, excitation of the donor molecule will result in strong fluorescence emission by the acceptor molecule. If the distance between the donor and acceptor dyes is large, fluorescence emission from the donor molecule will dominate. The efficiency of energy transfer provides a quantitative measure for the distance between the donor and acceptor molecules. FRET is discussed in more detail in chapter (2.05) of this volume. To allow in vivo measurements on the distance between two selected proteins in the cell, these proteins need to be labeled with an FRET pair of fluorophores. A highly effective way of achieving this is to use two mutants of GFP [99]: cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP); see Section 2.11.3.2.2. CFP and YFP form a good FRET pair of fluorophores. Thus, to study interactions between two different proteins, genetic engineering can be used to fuse one interaction partner with CFP and the other with YFP. The CFP-toYFP FRET efficiency can then be determined in the living cell by fluorescence microscopy, providing information on the distance between the two proteins in the cell. FCS utilizes diffusion to obtain information on the fluorescent molecules under study. In this case, a double-pinhole setup is used in order to measure fluorescence emission from a very small confocal volume. In FCS, the changes in intensity of fluorescence emission from the fluorophores in the confocal volume are measured as a function of time. When the confocal volume and concentration of fluorophores are well matched, the number of fluorescent molecules in the confocal volume is fairly small, typically 100 or less. In a typical experiment, the fluorophores are freely diffusible, and will thus diffuse into and out of the confocal volume. If the number of fluorophores in the confocal volume is very large, the diffusion of one molecule into (or out of) the confocal volume will cause a very small change in the observed fluorescence intensity. However, if the fluorescence originates from a small number of molecules, stochastic changes in the number of fluorescing molecules in the confocal volume caused by diffusion will result in significant changes in observed fluorescence intensity. Thus, it is intuitively clear that the ratio between the absolute fluorescence intensity and the amplitude of the time-dependent changes in fluorescence intensity provides a measure for the number of fluorophores in the confocal volume, that is, the concentration of the fluorophore. Since the confocal volume can be reduced to volumes

306 Single-Molecule and Nanoscale Approaches to Biological Signaling

smaller than a single bacterial cell, FCS is highly attractive for performing quantitative measurements on single cells. In addition to fluorophore concentration in the confocal volume, FCS measurements provide a second type of information. If the fluorophore is large, or if it is diffusing through a viscous medium, the timescale of the changes in fluorescence intensity will be relatively long. Conversely, small fluorophores diffusing through media with low viscosity will cause very rapid changes in fluorescence intensity. Thus, FCS allows measurements of the viscosity of a cell compartment when the hydrodynamic radius of the fluorophore is known. In most applications, both the residence time in the confocal volume and the number of fluorophores in the confocal volume are obtained by calculating the autocorrelation function for the observed time-dependent fluorescence signals. It should be noted that other effects can affect the FCS signal. For example, a fluorophore may undergo a conformational change that alters its fluorescence emission. In this case, the kinetics of these conformational changes will be reflected in the autocorrelation function. Another example is that a fluorescent protein may become bound (to a large protein complex, to the membrane, or to DNA or RNA). This will alter its diffusion rate and thus the autocorrelation function. Thus, FCS provides a rich tool for obtaining information on the properties of fluorophores from a volume that can be smaller than a single bacterial cell. 2.11.3.2 Using FRET to Probe Protein– Protein Interactions in Chemotactic E. coli Cells 2.11.3.2.1 Introduction to chemotaxis signaling in E. coli

The chemotactic response of E. coli is one of the best understood cellular responses in biology [100,101,102,103] (Figure 9). These responses allow the bacterial cell to modulate its swimming behavior so that it swims toward attractant molecules and away from repellent molecules. The main attractants are amino acids, sugars, and oxygen; acetate, glycerol, and nickel are repellents. These molecules bind to transmembrane receptor proteins. Since these receptors can be methylated (see below), they are referred to as MCPs. Five distinct MCPs (the high-abundance receptors Tsr and Tar, and the low-abundance receptors Trg, Tap, and Aer) allow E. coli to respond to a diverse set of chemostimuli. The MCPs function

as stable dimers. The binding of attractants and repellents triggers conformational changes in the MCP. As is the case for phototaxis in H. salinarum (Section 2.11.2.3.1), the His autokinase CheA is permanently complexed to the cytoplasmic part of the MCP. The CheW protein is involved in coupling CheY to the cytoplasmic region of the MCP. The autokinase activity of CheA is controlled by the conformational state of the MCP. The nanomechanical transmembrane relay mechanism that converts the binding of an attractant or repellent to the extracellular region of the MCP to a structural change in its cytoplasmic region that regulates CheA activity has been studied intensively [104,105]. Upon its autophosphorylation, CheA relays phosphate groups from its conserved His residue to a conserved Asp residue in the cytoplasmic protein CheY, which then diffuses through the cell until it binds to the switch complex of the flagellar motor. This triggers a change in the direction of rotation of the bacterial flagellum. Attractants cause a reduction in the concentration of CheY-P, and thus reduce the probability of a change in cellular swimming direction. E. coli swims by means of approximately four flagella per cell. Each flagellum is driven by a rotary motor at its base, which causes the rotation of the passive flagellar rod [106]. The flagellum and its rotary motor are a striking example of a biological nanomechanical device. The rotation of the flagellar rotor is driven by the electrochemical proton gradient across the bacterial membrane. Protonconducting channels in the flagellar motor harvest the energy of protons moving down their electrochemical gradient to drive the rotary motion of the flagellum. The flagellar motor typically rotates at a speed of 100 Hz. In addition, the motor has a gear: it contains switch components that allow the direction of rotation to be inverted. This gear is controlled by the binding of phosphorylated CheY molecules to the FliM component of the flagellar switch complex. When the flagella rotate in counterclockwise (CCW) direction, the flagella form a flagellar bundle which propels the cell in a fairly straight line. When the flagella switch rotation direction, the flagellar bundle disassembles, and the cell enters its tumbling mode, during which the orientation of the cell is essentially randomized. Upon the subsequent re-formation of the flagellar bundle, the cell then starts to swim in a new direction. This biased random walk results in the accumulation of cells in regions that are high in attractant concentration and low in repellent

Single-Molecule and Nanoscale Approaches to Biological Signaling

307

(a) CheB

Receptors

switch CheW CheA CheW CheA

FliM

CheY

CheZ CheR

(b)

+ Attractant CCW CW

– Attractant

MCPs

+CH3

Flagellar motors

CheR

CheW

CW P

CheA

P

–CH3

CheZ ATP

CheB

ATP

Pi

P

ADP

CheY Pi

Figure 9 The components of the chemotaxis signal transduction pathway in E. coli. The transmembrane chemoreceptors that bind chemoeffector molecules cluster at the tip of the cells. Since these proteins are methylated they are referred to as methyl-accepting chemotaxis proteins (MCPs). As in the case of phototaxis in Halobacterium salinarum (see Figure 7), the MCP proteins interact with the CheA His kinase bound to their intracellular regions. The conformation of the MCP controls the autokinase activity of CheA. Upon its autophosphorylation, CheA transfers the phosphate group to the diffusible protein CheY. In its phosphorylated state, CheY binds to the FliM component of the flagellar motor switch complex and thus reverses its rotation direction. The kinase CheZ removes phosphate groups from CheY-P. The complex structure of the flagellar motor, consisting of multiple rings and motor proteins, is shown schematically. Adaptation of the flagellar output to temporal changes in chemoeffector concentration is achieved by changes in the methylation state of the MCP: CheR adds methyl groups to the MCP, while CheB removes them. (b) More detailed representation of the components in the E. coli chemotaxis signaling pathway and their functional interactions. (a) Reproduced from Bren A and Eisenbach M (2000) How signals are heard during bacterial chemotaxis: Protein–protein interactions in sensory signal propagation. Journal of Bacteriology 182: 6865–6873. (b) Reproduced from Parkinson JS, Ames P, and Studdert C (2005) Collaborative signaling by bacterial chemoreceptors. Current Opinion in Microbiology 8: 116–121.

concentration. Thus, bacterial chemotaxis is achieved by altering the probability for switching the rotation direction of the flagellar motor. The E. coli chemotaxis machinery responds to temporal changes in the concentration of attractants and repellents, and adapts to the continued presence of these stimuli. As a result of this adaptation response, the exposure of a cell population to a sudden increase in repellent concentration will trigger a transient

increase in the frequency of tumbling (within 1 s), followed by an adaptation phase that results in a return (in a few seconds) to the pre-stimulus tumbling frequency, even in the continued presence of the repellent. This allows the cell to find its optimal environment. In this mechanism, cells compute changes in their flagellar rotation bias by comparing the current concentration of chemoeffectors with that experienced during the previous 3 s [107].

308 Single-Molecule and Nanoscale Approaches to Biological Signaling

Adaptation is achieved by changes in methylation of the MCPs, which compensates for the changes in MCP conformation induced by repellent/attractant binding. While CheR acts as a receptor methylase, the function of CheB is its demethylation. CheR is involved in adaptation to increased levels of attractants, while CheB triggers adaptation to decreased attractant levels. An additional level of sophistication is the presence of protein phosphatases. CheZ specifically accelerates the dephosphorylation of CheY-P. In addition, CheA not only phosphorylates CheY, but also phosphorylates the MCP methylesterase CheB, thus regulating its activity. The coordinated response of these chemotaxis signaling components triggers the observed stimulus-induced changes in the swimming behavior of the bacterial cell. The average number of protein molecules per cell of the various chemotaxis signaling components depends on growth conditions and the E. coli strain used. Typically, a single cell contains 20 000 MCP chemotaxis receptor proteins (distributed over the five types of MCPs), 8400 molecules of CheW, 7000 of CheA, 8400 of CheY, 3300 of CheZ, 280 of CheB, and 180 of CheR [108]. While it consists of a relatively small number of different protein components, the sensory properties of the E. coli chemotaxis are impressive. E. coli is chemotactically sensitive to chemoeffector concentrations down to 5 nM, and responds to changes in concentration over at least five orders of magnitude. The mechanisms by which this combined sensitivity and dynamic range is achieved are becoming increasingly clear, and involve a combination of methylation-based adaptation, ultrasensitivity at the level of the flagellar motor (Section 2.11.3.3.2), and allosterically coupled clusters of receptors (Section 2.11.4.2.1). A related remarkable property of this system is its high signal amplification. A 1% change in receptor occupancy can trigger a 50% change in rotational bias of the flagellar motor [107], corresponding to a gain of a factor of 50. The sources of this gain in the signaling pathway are under intense study [109]. 2.11.3.2.2 Probing in vivo chemotactic signaling in E. coli by FRET

In order to allow FRET-based analysis of protein– protein interactions in the E. coli chemotactic signaling pathway, two of its components (the response regulator CheY and its phosphatase CheZ) were selected for fluorescent labeling [109]. Using genetic engineering, E. coli strains were constructed in which

the CheY protein is fused to YFP and CheZ is fused to CFP. The CheZ and CheY fusion proteins are not impaired in their chemotactic signaling functions. The resulting E. coli strain allows the in vivo detection of protein–protein interactions between CheY and its phosphatase CheZ, and changes in the concentration of this protein–protein complex during chemotactic signaling. Chemotactic stimuli that increase the activity of CheA will result in the rapid phosphorylation of CheY, and information on the kinetics of CheA, CheY, and CheZ function indicates that the concentration of the transient CheY-P/ CheZ complex will be proportional to autokinase activity of the MCP/CheA receptor complex. Formation of the CheY-P/CheZ complex will bring the YFP and CFP moieties attached to these two signaling proteins in close proximity, thus resulting in an increase in FRET from CFP to YFP. Thus, changes in FRET signal intensity reflect changes in CheA activity, and therefore of a key function in the chemotaxis signaling pathway. The FRET signals were obtained from 400 cells in the view of a microscope with epifluorescence attachments. The cells were tethered to the surface of a glass coverslip, so that the number of cells is constant during the fluorescence measurements. The coverslip was placed inside a flow cell, allowing the effect of the addition of chemoattractants and repellents on the FRET signal to be measured. Fluorescence signals were measured with a 5-Hz frequency, allowing real-time detection of changes in CheY-P/CheZ complex formation in the cells. The exposure of cells to an increase in chemoattractant concentration was observed to cause a very rapid decrease in FRET signal, followed by a slower adaptation response in which the FRET signal returned to its pre-stimulus values. Thus, the expected transient increase in CheA activity caused by the addition of chemoattractants was directly observed in this experiment. Therefore, this experiment bridges the gap between expected interactions between signaling proteins in the living cell deduced by genetic and in vitro biochemical experiments and signaling as it occurs inside the living cell [109]. Additional experiments, using the same approach, were performed to determine the dependence of the in vivo association between CheY and CheZ on chemoattractant concentration, and the effect of the genetic deletion of the CheR and CheB. Quantitative analysis of these results allowed conclusions on the amplification of the chemotactic signal in the in vivo signal transduction chain [109].

Single-Molecule and Nanoscale Approaches to Biological Signaling

Specifically, the results indicated that interactions at the receptor signaling complex result in the amplification of the chemotaxis signal by a factor of 35. The same strategy was used to study interactions between CheY and the FliM component of flagellar motor switch complex [110]. E. coli strains in which CheY was fused to YFP and FliM was fused to CFP were studied by fluorescence microscopy. This approach allows the direct detection of the binding of CheY-P to the flagellar motor switch during chemotactic signaling in living cells (Figure 10). The cells were immobilized on a glass surface and placed in a flow cell; and the effect of changes in chemoeffector concentration on in vivo FRET signals was measured. In addition, the kinetics of the changes in FRET were examined in response to flash-induced release of caged chemoeffectors. These experiments

309

revealed an in vivo binding constant of CheY-P to FliM of 3.7 mM, very similar to the value detected in studies on individual cells (see Section 2.11.3.3.2). The kinetics of signaling were found to be as fast as allowed by the diffusion of CheY-P [110]. 2.11.3.3 FCS Approaches to Biological Signaling Most measurements on biological responses of cells are performed using large populations of cells. The detected response then reflects an average response for the entire population. However, a growing body of data indicates that genetically identical cells exposed to identical stimuli can exhibit significant cell-to-cell variation in their response. When such variability is observed, measurements on single cells

Figure 10 Fluorescence resonant energy transfer (FRET)-based detection of functional interactions between CheY and the flagellar motor switch protein FliM during chemotactic signaling in living cells. E. coli strains containing two fusion proteins were constructed, in which FliM is fused to cyan fluorescent protein (CFP) and CheY to yellow fluorescent protein (YFP). Since the fluorescence emission spectrum to CFP has considerable spectral overlap with the absorbance spectrum of YFP, the CFP/ YFP pair is well suited for FRET. Binding of CheY to the switch complex of the flagellar motor upon the removal of chemoattractants brings CheY in close proximity to FliM. This results in a significant increase in the efficiency of FRET, in which photoexcitation of CFP results in fluorescence emission from YFP. The addition of attractants reduces the phosphorylation level of CheY, causing it to lose its affinity for FliM. This causes the cells to keep swimming in the same direction (of increasing attractant concentration), and results in a reduced FRET signal in which the intensity of fluorescence emission from CFP is increased and that from YFP is reduced. (b) Real-time detection of changes in the interaction between CheY and FliM upon the addition and removal of chemoattractants. The E. coli cells expressing the FliM–CFP and CheY–YFP fusion proteins were immobilized on a glass surface and placed in a flow cell to allow the addition and removal of chemoeffectors. FRET signals from a few hundred immobilized cells were detected using an epifluorescence microscope. The addition of attractants causes a clear and transient reduction in CheY–FliM interactions, while a reduction in attractant concentration results in a transient increase in their interactions. (c) The effects of the deletion of the CheY phosphatase CheZ on the interactions between CheY and FliM were determined using the same approach. Reproduced from Sourjik V and Berg HC (2002) Binding of the Escherichia coli response regulator CheY to its target measured in vivo by fluorescence resonance energy transfer. Proceedings of the National Academy of Sciences of the United State of America 99: 12669–12674.

310 Single-Molecule and Nanoscale Approaches to Biological Signaling

are vital for understanding the system [111,112]. Since random noise can cause fluctuations in the expression level of proteins in a cell, what is needed are methods that allow the simultaneous measurement of the cellular content of specific signaling proteins and the biological response of this cell. Thus, the response of a cell can be directly correlated to its composition of signaling components. How to measure the concentration of a selected protein within a single living cell? Recent developments have shown that fluorescence correlation spectroscopy is an ideal method to achieve this [113]. 2.11.3.3.1 Using FCS to measure the concentration of signaling proteins in a single cell

To allow the measurement of protein concentration inside a single cell by FCS, the protein is fused to GFP. The protein that was selected (for reasons explained in Section 2.11.3.3.2) for fusion with GFP was the E. coli chemotaxis protein CheY (see Section 2.11.3.2.1). This CheY–GFP fusion protein was placed under control of an isopropyl -D-1-thiogalactopyranoside (IPTG)-inducible promoter and transformed into an E. coli mutant lacking CheY. Thus, the level of CheY–GFP protein expression can be controlled through the concentration of IPTG added to the cells. The FCS measurements were performed on cells that were immobilized by binding to a glass surface. An FCS setup was combined with an inverted microscope, and this apparatus was used to locate the confocal volume in a small part of the cytoplasm of a single E. coli cell. Since the confocal volume used is smaller than the volume of a single cell, the concentration of CheY–GFP can be measured inside this selected cell. Autocorrelation analysis of the resulting FCS data yields the absolute concentration of CheY– GFP inside a single cell [113]. Thus, this is a powerful general approach for determining the absolute concentration of a specific protein in a single living cell. The FCS-based determination of CheY–GFP concentration was performed on many single cells grown in the presence of various IPTG concentrations. Interestingly, for a specific IPTG concentration, significant cell-to-cell variation in CheY–GFP concentration was observed, with a standard deviation of 24% of the mean and a range from 0.8 to 6 mM. Apparently, genetically identical E. coli cells in an identical environment still exhibit significant variation in protein levels [113]. This is an important

emerging theme in cell biology, and can be explained by the small number of molecules involved. A single E. coli cell contains only a small number of genes encoding the CheY–GFP fusion protein, and these genes are transcribed by a fairly small number of RNA polymerase molecules. Because of this, the amount of CheY–GFP proteins in a cell is unavoidably subject to stochastic fluctuations. This is a general issue that applies to all proteins in the cell (Section 2.11.3.4.1). 2.11.3.3.2 Correlating signaling protein concentration and responses of a single cell

An important advantage of choosing CheY for FCSbased measurements of protein concentration inside a single cell is that the activity of CheY controls a cellular response that can be detected microscopically: the direction of rotation of the flagellar motor. To allow the response of an individual flagellar motor, cells are immobilized on a glass surface, while their flagella are still free to rotate. In addition, 0.5-mm latex microbeads are attached to the flagella using antiflagellin antibodies, allowing the visualization of their rotation by light microscopy [113]. Thus, switches in the rotation direction of a single flagellar motor can be detected. In an E coli strain lacking CheY, the cells continuously rotate in CCW direction. This is because phosphorylated CheY is needed to induce the flagellum to convert to the clockwise (CW) rotation direction. When CheY production is induced using the plasmid harboring the IPTG-regulated CheY–GFP fusion protein, recovery of switches in the rotation direction of the flagellar motor was observed [113]. A key aspect of this experiment is that the frequency of switching the rotation of a flagellar motor, as observed by dark-field microscopy, can be combined with the simultaneous FCS-based detection of the CheY content of the same cell. Based on changes in IPTG concentration and the stochastic variation in CheY expression, a broad range of CheY concentrations was observed in different cells. For each of these cells, the frequency of switches in flagellar rotation direction was measured. The E. coli strain used also lacks the CheY phosphokinase CheZ (see Section 2.11.3.2.1), so that essentially all CheY molecules in the cell will be phosphorylated. Thus, the CW/CCW bias of individual flagellar motors could be plotted against the FCS-based concentration of CheY-P. The CW bias is defined as the fraction of time that a motor rotates

Single-Molecule and Nanoscale Approaches to Biological Signaling

in the CW direction. Interestingly, data from all cells grown in all IPTG concentrations were observed to follow the same sigmoidal curve. Thus, while there is significant noise in the CheY concentration, the response of the bacterial motor is highly uniform. The sigmoidal curve could be described by a dissociation constant of 3.1 mM and a very large Hill coefficient of 10.3  1.1. Thus, the flagellar motor exhibits ultrasensitivity to the concentration of CheY-P. A consequence of this unexpectedly steep dependence of flagellar motor bias on CheY-P is that small concentrations in the phosphorylation level during chemotactic signaling can cause strong changes in cellular swimming behavior: the flagellar motor itself acts as an amplifier in chemotactic signaling [113]. A possible explanation of the large Hill coefficient is that multiple CheY-P molecules can bind to the cytoplasmic FliM ring of the flagellar motor, which consists of 30 binding sites. The high Hill coefficient would then reflect strong allosteric coupling between the FliM monomers. Structural constraints in the assembly of the flagellar motor may result in the tight regulation of the number of FliM subunits in the ring, and thus cause the uniformity of the response of the flagellar motor to CheY-P concentration. Experiments on cell populations yield a Hill coefficient of 3.5–5.5. However, in these measurements, the cell-to-cell variation in CheY expression level will smooth out the steep response curve of the flagellar motor. This illustrates the importance of measurements on individual cells.

2.11.3.3.3 Conclusions and general implications for signal transduction

The E. coli chemotaxis signaling pathway has proved to be a highly accessible and powerful model system for understanding in vivo signal transduction, and the role that molecular noise plays in this process, and has recently yielded a number of concepts that are of considerable general importance. FRET approaches have been used to detect protein–protein interactions that relay the chemotaxis signal in intact living cells as they are responding to changes in chemoeffector concentration. Analysis of these data has yielded quantitative insights into the kinetics and amplification of signaling in the cell. Such FRET measurements are generally applicable to a range of systems where genetic tools are available and two interacting proteins in a signal transduction cascade are known.

311

FCS approaches have proved to be capable of measuring the concentration of a specific signaling protein inside a single living cell. The flagellar motor output of chemotaxis can be studied at the single cell level, allowing comparisons of signal output and signaling protein concentration. This has shown that the flagellar motor is highly cooperative for CheY-P binding, making it ultrasensitive to small changes in the phosphorylation level of CheY. These single-cell measurements also revealed that population-averaged measurements significantly underestimate the degree of cooperativity in chemotactic signaling, due to cell-to-cell variations in CheY concentration. Thus, signal transduction in cells is best studied at the single-cell level, where nanoscale effects such as stochastic variability in gene expression can be directly taken into account. 2.11.3.4 Consequences of Thermal Noise for Biological Signaling 2.11.3.4.1 Robustness of cellular behavior against thermal noise

The very low copy number of individual genes and many regulatory factors in a typical single cell implies that thermal noise will result in significant cell-to-cell variation even in populations of genetically identical cells under identical conditions. The relative contributions of two distinct sources of noise have been determined in E. coli cells [114]. First, intrinsic noise from gene expression is caused by the stochastic nature of the events needed for gene expression [115]. This source of noise is even present in cells with identical chemical compositions. Second, extrinsic noise is caused by stochastic cellto-cell variations in the copy numbers of the proteins needed for gene expression. To distinguish these two sources of noise, an E. coli strain was used in which copies of the genes encoding YFP and CFP were placed under the control of the same promoter sequence, and inserted into the chromosome. Fluorescence microscopy of this E. coli strain allowed the expression level of YFP and CFP to be quantified in individual cells. The level of YFP expression in single cells was plotted against the CFP content of the same cell. Variation along the diagonal line of YFP versus CFP content corresponds to the extrinsic noise. Stochastic variations in the concentrations of factors that alter gene expression will have the same effect on the expression of YFP and CFP. Variation perpendicular to this diagonal line represents intrinsic

312 Single-Molecule and Nanoscale Approaches to Biological Signaling

noise. Even when cells are normalized to CFP expression level, reflecting the net effect of all factors affecting gene expression in that cell, variation in YFP expression was observed due to intrinsic noise in the process of gene expression. These measurements were repeated for various E. coli strains. The results show that noise level varies substantially, depending on the genetic context of the gene under study, and that both intrinsic and extrinsic noise contribute significantly. The occurrence of stochastic fluctuations in the copy number of cellular components has important biological implications [116,117]. Cells can employ mechanisms, such as redundancy and feedback loops, to obtain reliable regulatory networks. In addition, cells may exploit noise for regulatory purposes (Section 2.11.3.4.3). More generally, mechanisms in the cell need to function in spite of stochastic variations in the components of these mechanisms: biochemical networks need to be robust [118,119]. A general approach to achieve such robustness is the use of networks with a scale-free topology, in which a relatively small number of components in the network are highly connected, while the majority of components interact with only few other components [120]. 2.11.3.4.2 Molecular noise as a key element in chemotactic signaling

Section 2.11.3.3.2 described a specific example of noise in the expression level of the chemotaxis signaling protein CheY, and how the consequences of this noise masked the ultrasensitivity of the flagellar motor switch to CheY-P concentration. These findings raise important and fundamental questions. Is it valid to use experimental approaches that measure the average response of a large population of cells? How do the signaling mechanisms of cells cope with the random fluctuations in the concentration of signaling proteins? Insights into some of these issues have recently been obtained by studies on signaling in the E. coli chemotaxis pathway [121]. In these experiments, the rotary movements of single latex beads attached to single flagella of immobilized cells (see Section 2.11.3.3.2) were measured using a four-quadrant photomultiplier [121]. The cells were incubated in a medium that maintained cell viability, but did not allow growth. Interestingly, it was found that the time dependence of the output of individual motor proteins in the absence of stimuli (i.e., no change in attractant or repellent concentration) holds important information on the functioning

of the chemotactic signaling pathway in individual cells. Population measurements have yielded the widely accepted view (see Section 2.11.3.2.1) in which the events that result in the switching of the rotation direction of the flagellar motor are independent and can be described as a Poisson process (see, e.g., Ref. [122]). Korobkova and coworkers tested this assumption by measuring long time series (up to 3 h) of the rotational movements of individual flagellar motors. To examine these long time series, the CW bias of the detected single flagellum was derived from the data, and was used to calculate the power spectrum for changes in rotation direction. This power spectrum describes the frequency of flagellar switching at all timescales in the experiment. Since such switching events occur at a timescale of 1 s, the assumption that flagellar switching is an independent Poisson process would predict that the power spectrum is flat on timescales longer than the individual switching events. In striking contrast, the power spectrum observed for individual motors revealed a rise up to timescales of 15 min [121]. Importantly, when the CW bias time series of 200 cells were averaged, the power spectrum indeed exhibited the expected flat profile at timescales longer than a few seconds (Figure 11). This is a striking example of how measurements that use population averaging can mask information on the behavior of individual cells. The temporal variability in switching behavior of individual cells is much larger than that derived from population-averaged measurements. Measurements on two different mutants show that the chemotactic signaling network itself is the main origin of the temporal variability of individual cells. In these mutants, the methyl transferase CheR (see Section 2.11.3.2.1) and a permanently activated mutant of CheY were expressed at near wild-type levels from plasmids containing these genes under the control of inducible promoters. Relatively small changes in the cellular concentration of activated CheY and CheR were found to convert the flagellar switching power spectrum of individual cells at longer timescales to the flat pattern observed in population-averaged data [121]. This indicates that the chemotaxis signaling network in E. coli evolved to maximize temporal variability in single cells. This was confirmed by simulations of the signaling network, which revealed that stochastic fluctuations in cellular CheY-P concentration can cause the experimentally observed power spectrum. The high variability in the switching of flagella in individual

Single-Molecule and Nanoscale Approaches to Biological Signaling

P(ω)

10–2

10–3

10–4 10–2

10–1

1

Frequency ω (s–1) Figure 11 The power spectrum for flagellar rotational bias for unstimulated individual cells reveals unexpected fluctuations in flagellar motor output at long timescales, but this feature is absent in population-averaged data. The flagellar of single immobilized cells were decorated with microbeads, allowing the detection of changes in the rotation direction of single flagellar motors. The amplitude of the detected changes in flagellar motor bias (fraction of time that the motor rotates in the clockwise direction) over different timescales (i.e., the power spectrum) was calculated from such data. The solid line represents data obtained for single E. coli cells, which reveal an unexpected rise at longer timescales. The time series of multiple cells were measured and averaged, and the power series of the resulting averaged data was calculated (dashed line). In this case, the temporal variability of the flagellar motor at longer timescales is no longer seen, matching previously reported population-averaged data. Based on Korobkova E, Emonet T, Vilar JMG, Shimizu TS, and Cluzel P (2004) From molecular noise to behavioural variability in a single bacterium. Nature 428: 574–578.

bacteria may aid the cell in identifying locations with optimal nutrient concentrations in a complex environment. These results indicate that molecular noise in the E. coli chemotaxis signaling network results in an unexpectedly high degree of variability at the single-cell level, which is likely to be important for the behavioral response of the cell, but which cannot be observed in population-averaged measurements. 2.11.3.4.3 Exploiting thermal noise for biological signaling: Competence in Bacillus subtilis

A classic problem in microbiology is the development of competence in the soil bacterium Bacillus subtilis. When B. subtilis is faced with nutrient limitation, the cells activate a complex cellular differentiation program. During this developmental program, a majority of cells undergo sporulation, while a small percentage of the cells develop competence [123,124]. In the competent state, cells are able to take up DNA molecules from the environment,

313

which is thought to be a strategy for exploring genetic diversity that may allow the utilization of alternative nutrient sources. The classic puzzle presented by this phenomenon is: How is it possible that a genetically identical cell population faced with the same environmental challenge can undergo two very different developmental processes? In addition, how is it decided which cells will develop into spores and which cells will become competent? Extensive genetic and biochemical studies have revealed the components (and interactions between these components) in the regulatory network that control gene expression during sporulation and the development of competence [123,124]. However, this information did not provide a solution to the above questions. To obtain further insight into this question, two fluorescent reporter strains proved to be crucial. In these strains, the promoters of three key components of the sporulation pathway were placed in front of YFP and CFP. The three components studied using this approach were the promoter of ComK, a master transcription factor in the sporulation regulatory network, the promoter of ComS, and the promoter of the comG operon, which encodes factors required for the development of competence. The ComS gene product is of importance due to its inhibitory effect on the MecA complex, which degrades ComK. Thus, ComS favors the induction of competence by preventing ComK degradation. These reporter strains allow the quantitative detection of the activity of the comK, comS, and comG promoters in individual cells by fluorescence microscopy. By placing two of these promoters in a single cell, with one promoter controlling YFP and the other controlling CFP, the activity of both promoters can be quantified in single cells using fluorescence microscopy [125]. These reporter strains of B. subtilis allowed the direct detection of two key predictions at the single-cell level. First, the activity of the promoters of ComG and ComK is highly correlated, demonstrating that ComK is the key regulator for the expression of ComG. Second, a striking anticorrelation between the activity of the promoters of ComG and ComS was observed, which is consistent with an inhibitory effect of ComK on the expression of ComS. To understand how a small number of cells decide to activate the developmental program for competence development, a differential equation model for the regulation of its expression was developed based on known interactions in the regulatory network that controls competence development (Figure 12). The

314 Single-Molecule and Nanoscale Approaches to Biological Signaling

CIpP CIpC

(a)

MecA

ComK

ComK PcomK

ComS

ComS PcomS

ComG PcomG

(b) ComS concentration (a.u.)

8

6

4

2

0

0

1

3 2 ComK concentration (a.u.)

4

Figure 12 The role of molecular noise in triggering competence development in Bacillus subtilis. (a) Network of interactions that form the core part of the regulatory circuit that controls competence development. ComK is a master transcription factor for competence development. It activates its own transcription in a positive feedback loop. ComS inhibits the activity of MecA complex that degrades ComK, and the transcription of ComS is inhibited by ComK in a negative-feedback loop. (b) The phase diagram for the regulation of competence development as determined by the cellular concentrations of two of its key components: ComK and ComS. The properties of a cell in vegetative growth are indicated by the black filled circle. Trajectories in phase space were (indicated in pink) obtained from simulation of the regulatory system depicted in (a) that include stochastic variations in the expression level ComS. The noise in the expression level of ComS can cause large excursions in phase space. A single representative trajectory is highlighted in purple. Such excursions correspond to cellular decisions to induce the development of competence. Similar excursions were detected experimentally by fluorescence microscopy using strains of Bacillus subtilis in which ComK and ComS were fused to cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP). Reproduced from Su¨el GM, Garcia-Ojalvo J, Liberman LM, and Elowitz MB (2006) An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440: 545-550.

model contains two feedback loops: a positive one based on the stimulatory effect of ComK on its own transcription, and a negative one based on the effect of ComS on the degradation of ComK by the MecA complex. A key element in this model is that it incorporates noise that results in random fluctuations in the expression level of ComS in a single cell. The consequences of this model were evaluated by

examining the phase space for ComK/ComS expression level (Figure 12). Simulations were performed based on this model, and the resulting calculated concentrations of ComS in a single cell were plotted as a function of ComK concentration. While considerations based on phase space are a common approach in physics, their use in biology has been quite rare. This analysis revealed

Single-Molecule and Nanoscale Approaches to Biological Signaling

that most cells would tend to remain in a quite small range of ComK/ComS values. However, when random fluctuations in the expression level of ComK or ComS reach a threshold level, large excursions in phase space were observed in the simulations. These excursions match the experimental results from fluorescence microscopy, and cause the activation of competence development in that cell [125]. The conclusion from this analysis is that the regulatory system for competence development, with its combined positive and negative feedback loops, results in a situation in which random fluctuations can trigger the activation of competence development. The exact parameters in the system determine the value of this threshold. Thus, B. subtilis utilizes the amplification of random fluctuations to trigger competence development in a small, random subset of cells. The role of random fluctuations in gene expression and signal transduction is an important development for understanding signaling in the living cell. Without the application of this novel concept, the long-standing mystery of the regulation of competence development in B. subtilis could not have been solved. 2.11.3.4.4 Conclusions and general implications on the role of noise in biological signaling

A significant level of noise in gene expression is unavoidable. Thus, evolution has resulted in the development of those cellular mechanisms that are robust in the resulting stochastic cell-to-cell variations in the concentration of the components of the signal transduction system. This robustness is a key property for understanding cellular behavior, and is in striking contrast to man-made devices. In the case of competence development in Bacillus subtilis, the cell in fact takes advantage of the resulting noise in the signaling pathway that controls this developmental program. A small percentage of cells that happen to reach a critical threshold become competent for DNA uptake. The remainder of the cell population develops into dormant spores. Thus, the cells are able to explore different strategies when faced with the same environmental challenge of starvation. In E. coli chemotaxis it has been shown that the temporal variations in the output of the unstimulated flagellar motor hold valuable information on the signaling cascade that controls it, revealing an unexpected diversity in the timescales over which the system is modulated. This information is lost when the signals are averaged over even quite small

315

numbers of cells. Evolution appears to have placed the chemotaxis system at a position of maximum cellular variability in motor output as caused by molecular noise in the concentration of its protein components. This may be an optimal chemotactic strategy in a complex, nonhomogenous environment. These results vividly illustrate that bacterial signaling pathways are to a significant degree nondeterministic. They operate in the face of stochastic fluctuations, and, in some cases, even appear to actively exploit the consequences of these fluctuations. Biology provides highly sophisticated examples of how the effects of noise can be managed, and how they can be exploited. These are likely to be general principles for nanoscale devices in which diffusion and noise are key components.

2.11.4 Subcellular Nanoscale Protein Clusters in Biological Signaling 2.11.4.1 The Cytoplasm and Cytoskeleton of Bacteria Most textbooks on cell biology, microbiology, and biochemistry describe a fundamental dichotomy in nature regarding the cellular structure of all organisms on earth. On the one hand, prokaryotes are organisms in which the cytoplasm is essentially unstructured, with the genetic material floating freely in the cytoplasm. On the other hand, eukaryotes contain a membrane-bound nucleus enclosing the genetic material, and a host of membraneenclosed compartments, including mitochondria, the endoplasmic reticulum, and the Golgi apparatus. In addition, the cytoplasm of eukaryotic cells is structured by the presence of three classes of elongated cytoskeletal elements: microtubules consisting of the tubulin, actin cables composed of actin, and intermediary filaments containing various structural proteins. Two important results pose a fundamental challenge to this view of life. First, molecular taxonomy, largely based on ribosomal RNA sequences, has provided very strong and now almost universally accepted evidence that life consists of three domains of life: Eukaryotes, Bacteria, and Archaea [126]. Thus, the prokaryotes consist of two groups of organisms that are taxonomically equivalent to the Eukaryotes. It has been argued that the continued use of prokaryotes as a unit is detrimental to our understanding of and research on the molecular basis of life [127].

316 Single-Molecule and Nanoscale Approaches to Biological Signaling

Second, it is becoming increasingly clear that Bacteria and Archaea contain cytoskeletal elements and a structured cytoplasm [15]. Until recently, it was thought that bacteria lack a cytoskeleton, and, in particular, the filamentous networks composed of actin and tubulin present in the eukaryotic cytoplasm. However, it has been demonstrated that FtsZ is homologous to tubulin, like tubulin exhibits guanosine triphosphatase (GTPase) activity, and forms ring-shaped structures involved in bacterial division [128,129]. In addition, the bacterial protein MreB assembles into filaments and strongly resembles actin [130,17]. Thus, bacteria contain a complex cytoskeleton that includes homologs of both tubulin and actin [18,19]. Mounting evidence shows that proteins in bacteria can be highly organized into functional nanoscale clusters. The mechanisms responsible for this structuring are largely unresolved and likely to be diverse, but probably include the bacterial cytoskeleton [131]. Recent results indicate that such protein clusters are important in a number of signal transduction pathways, including bacterial chemotaxis. These results are discussed in the following. 2.11.4.2 Nanoclusters for Signaling in Bacterial Chemotaxis The pathways for bacterial chemotaxis show significant diversity [132]. Subsequently, recent insights into chemotaxis in Rhodobacter sphaeroides are discussed, providing an example of this diversity. In addition, bacteria can respond to a range of chemical, light, and other stimuli. Magnetotactic bacteria, such as Megnatospirillum magneticum and Magnetospirillum gryphiswaldense, use intracellular structures called magnetosomes to orient themselves in magnetic fields. Magnetosomes consist of an ordered array of 15–20 magnetite crystals that are 50 nm in size. Recently, it has become evident that the actin-like cytoskeleton is essential for organizing the magnetite crystals into this ordered linear array [133,134]. This is an example of nanoscale structures involved in the signaling systems that regulate bacterial motility. Examples of nanoclusters in the signaling proteins involved in chemotaxis in E. coli and Rb. sphaeroides are described in the following. 2.11.4.2.1 Nanoscale protein clusters in E. coli chemotaxis

In Section 2.11.3.2.1, the impressive chemotactic performance of E. coli was introduced: cells are able to respond to chemotactic stimuli over five orders of magnitude in

concentration. In this section, recent results on the role of nanoscale functional clusters of receptors in achieving this large dynamic range are discussed. Immunogold and immunofluorescence studies on the subcellular localization of MCP chemoreceptors in E. coli revealed that a significant fraction of these receptors is clustered at the pole of the cell [135]. In addition, lateral clusters were observed. The average size of the receptor clusters is 250 nm, which indicates that they contain 7500 receptor dimers [136]. Each cell contains only a few of these clusters. The cytoplasmic chemotaxis signaling proteins CheA and CheW show a similar clustering, which is eliminated when the MCPs are genetically deleted. Similarly, the clustering of the MCPs depends on the presence of CheW and CheA, indicating that these three components form functional clusters [135]. Subsequently, these results were extended to CheA and CheZ using GFP fusion proteins [137]. Thus, the MCPs form large functional arrays of receptors that include the key components of the chemotactic signaling pathway (Figure 13). Mathematical modeling has demonstrated that functional coupling of MCPs in a receptor cluster through allosteric interactions, combined with receptor adaptation through methylation, can quantitatively explain the observed combination of high sensitivity and wide dynamic range [139,140]. In this explanation, the degree of allosteric coupling (or physical aggregation) is regulated in response to the concentration of chemoattractant/repellent molecules. Such functional interactions between arrays of MCPs have been detected by a number of different experimental approaches [141,142,143,144]. These experiments have also shown that the functional receptor arrays can contain different types of MCPs, which functionally interact despite their different ligand specificities. The distance between receptor clusters is typically 1 mm, which helps to ensure that the chemotactic response time of the cell is not limited by the diffusion of CheY-P from the receptor cluster to the flagellar motor [136]. Lateral MCP clusters localize to sites where future cell divisions will occur, which is thought to ensure that each newly divided cell will contain at least one functional cluster for performing chemotaxis [136]. The exact architecture of the receptor clusters is under intense investigation [13,145,146,147]. These results demonstrate the importance of nanoscale clusters of interacting receptors in chemotaxis, and indicate that nature utilizes highly sophisticated arrays of interacting signaling proteins that can be up to 250 nm in size.

Single-Molecule and Nanoscale Approaches to Biological Signaling

317

(a)

(b)

(c)

Figure 13 Structure of functional nanoclusters containing thousands of chemoreceptor proteins in E. coli. (a) An E. coli strain was constructed in which the receptor methylase CheR (see Figure 9) is fused to yellow fluorescent protein (YFP), and examined by fluorescence microscopy. This reveals polar clusters of CheR molecules that can be present in one discrete spot or in double or triple spots. Single lateral clusters are also observed. The bar indicates a length of 2 mm. (b) Schematic representation of structural information on chemoreceptor arrays in E. coli based on cryo-electron tomography of intact cells. The dots in the cytoplasm correspond to ribosomes. (c) Model for the clustering of chemotaxis receptors. Crystallographic information on the structure of an MCP dimer was used to develop a model for interactions within clusters of receptor proteins. (a) Reproduced from Kentner D and Sourjik V (2006) Spatial organization of the bacterial chemotaxis system. Current Opinion in Microbiology 9: 619–624. (b) Zhang PJ, Khursigara CM, Hartnell LM, and Subramaniam S (2007) Direct visualization of Escherichia coli chemotaxis receptor arrays using cryo-electron microscopy. Proceedings of the National Academy of Sciences of the United States of America 104: 3777–3781. (c) Kim SH, Wang WR, and Kim KK (2002) Dynamic and clustering model of bacterial chemotaxis receptors: Structural basis for signaling and high sensitivity. Proceedings of the National Academy of Sciences of the United States of America 99: 11611–11615.

2.11.4.2.2 Introduction to chemotaxis in Rb. sphaeroides

Rb. sphaeroides (previously Rhodopseudomonas sphaeroides) is a metabolically diverse purple nonsulfur photoheterotrophic bacterium. It belongs to the

-Proteobacteria, and can perform photosynthesis, aerobic respiration, and anaerobic respiration. It can fix N2 and CO2, but prefers organic acids as its carbon source. Rb. sphaeroides exhibits chemotaxis toward a range of chemoeffectors [148,102] and is

318 Single-Molecule and Nanoscale Approaches to Biological Signaling

also phototactic [149,150]. The organism modulates its tactic responses to accommodate this metabolic diversity. For aerobic cells, O2 is an attractant, but for anaerobic cells it is a repellent; phototrophically grown cells are phototactic, but chemotrophic cells are not [151]. Thus, Rb. sphaeroides is able to modify its taxis responses to match its current metabolic state. Ongoing research is aimed at how these changes in taxis behavior are regulated. The motility of Rb. sphaeroides cells is based on a single flagellum located in the center of the cell [152]. This flagellum only rotates in the CW direction. Every c. 10 s, cells switch between periods of swimming (CW rotation of the flagellum) and periods during which the flagellum does not rotate, and the cells stop swimming for 1 s. These intermittent stops of Rb. sphaeroides play the same role as tumbling in E. coli. The average duration of the periods of swimming and stalled flagellar motion is altered under the influence of external stimuli. Rb. sphaeroides contains three distinct chemotaxis operons, each of which appears to encode a complete chemosensory pathway. In total, the Rb. sphaeroides genome encodes 13 MCPs, four CheW, four CheA, six CheY, two CheB, and three CheR proteins. Two of the three chemotaxis operons have been experimentally shown to be required for chemotaxis [153]. While nine of the MPCs in Rb. sphaeroides are classic transmembrane MCPs, the remaining four appear to be cytoplasmic chemoreceptors. These cytoplasmic chemoreceptors are thought to trigger chemotaxis responses to cytoplasmic compounds. The observation that chemotaxis responses in Rb. sphaeroides to some chemoeffectors require transport and metabolism is consistent with this hypothesis. A precedent for a functional cytoplasmic chemoreceptor is Car, which senses the cytoplasmic concentration of arginine in H. salinarum [154]. While E. coli contains a single chemotaxis pathway, many organisms deviate from this situation, and contain multiple chemosensory pathways [151]. This leads to a fundamental question in signaling: How can the cell avoid (or exploit) interactions between multiple parallel pathways that contain highly homologous components? Such cross talk could lead to situations where a change in an extracellular signal that normally triggers an adaptive response feeds into a different signaling pathway that would then trigger a maladaptive response.

2.11.4.2.3 Nanoscale complexes of signaling proteins in Rb. sphaeroides

A series of studies on the subcellular localization of components in the chemotaxis signaling pathway of Rb. sphaeroides has revealed an unexpected complexity in their spatial distribution [151]. These studies involved immunogold electron microscopy and fluorescence microscopy studies on mutants of Rb. Sphaeroides, in which a selected chemotaxis protein was fused to GFP. These measurements yielded the key finding that two different chemotaxis signaling pathways are located at two distinct subcellular localizations. The transmembrane chemoreceptors in Rb. shpaeroides are present in patches located at the cell poles. These patches also contain a specific subset of the chemotaxis proteins encoded in the genome of this organism: CheW2, CheW3, CheA2, and CheR2 [155,156]. In contrast, the cytoplasmic chemoreceptors form a discrete cytoplasmic cluster of proteins that also contains CheW4, CheA3, CheA4, and CheR3 [155,157]. Thus, the cell contains two distinct clusters of chemotactic signaling proteins, each consisting of a distinct set of chemotaxis receptors with CheA and CheW proteins. The requirements for the formation of these two signaling clusters are under investigation. The polar clusters require the presence of the CheA and CheW in these clusters, similar to the situation in E. coli (Section 2.11.4.2.1). In contrast, the cytoplasmic cluster is not perturbed by the deletion of its CheA components. It does require the presence of CheW4 and of the cytoplasmic chemoreceptor TlpT [158]. Interestingly, deletion mutant analysis has shown that chemotactic signaling in Rb. sphaeroides requires signals from both of these clusters [159,153]. This indicates that the integration of the signals from these two distinct signaling pathways is essential. The distinct subcellular localization of the two pathways also provides a compelling mechanism for how the cell avoids unwanted cross-talk between these two pathways. The polar localization of the transmembrane chemoreceptor cluster ensures appropriate segregation of these clusters upon cell division. However, this is not the case for segregation of the cytoplasmic cluster. During cell division, cells appear to actively segregate cytoplasmic clusters into the two newly formed cells. Interestingly, the operon encoding the Che components of the cytoplasmic signaling cluster contains a gene encoding PpfA, which belongs to the ParA family of plasmid and chromosome partitioning

Single-Molecule and Nanoscale Approaches to Biological Signaling

factors. Deletion of the gene encoding this protein perturbs segregation of the cytoplasmic chemotaxis cluster. Thus, bacteria appear to contain mechanisms for protein segregation during cell division similar to the known systems for DNA segregation [160].

2.11.4.3 Conclusions and Implications of Nanoscale Protein Clusters for Biological Signaling The E. coli chemotaxis signaling pathway consists of only a handful of different components. However, these few components result in a highly sophisticated sensory system that is able to respond to very small changes in extracellular chemoeffector concentration over at least five orders of magnitude in concentration. Methylation-dependent adaptation allows cells to respond to the time derivative of these concentrations. An emerging theme is that the cell achieves these impressive system properties by integrating functional interactions over a remarkably broad range of length scales, from the angstrom level in the piston-like motions triggered in receptor proteins by chemoeffector binding to 100 nm in the allosteric interactions between membrane patches containing thousands of chemoreceptor protein molecules. The nanoscale clustering of proteins is not limited to these ordered arrays of chemoreceptors. Rb. sphaeroides performs chemotaxis using two parallel signal transduction chains. The protein components of these signaling pathways are spatially segregated in nanoscale protein clusters at different subcellular locations. This is a highly attractive approach to avoid cross talk between different evolutionarily related signaling systems in the same cell.

Acknowledgments The author gratefully acknowledges support from NIH grant GM063805 and OCAST grant HR07135S, and from startup funds provided by Oklahoma State University. In addition, the author thanks Norbert Scherer and Philippe Cluzel for stimulating discussions and for introductions to fascinating novel approaches and concepts in biophysics, and Masato Kumauchi for help with preparing Figures 1 and 4.

319

References 1. Moriarty P (2001) Nanostructured materials. Reports on Progress in Physics 64: 297–381. 2. El-Sayed MA (2001) Some interesting properties of metals confined in time and nanometer space of different shapes. Accounts of Chemical Research 34: 257–264. 3. Privalow PL (1989) Thermodynamic problems in protein structure. Annual Review of Biophysics and Biophysical Chemistry 18: 47–69. 4. Honig B and Yang AS (1995) Free energy balance in protein folding. Advances in Protein Chemistry 46: 27–58. 5. Ansari A, Berendzen J, Bowne SF, et al. (1985) Protein states and protein quakes. Proceedings of the National Academy of Sciences of the United States of America 82: 5000–5004. 6. Kinbara K and Aida T (2005) Toward intelligent molecular machines: Directed motions of biological and artificial molecules and assemblies. Chemical Reviews 105: 1377–1400. 7. Moser CC, Keske JM, Warncke K, Farid RS, and Dutton PL (1992) Nature of biological electron transfer. Nature 355: 796–802. 8. Page CC, Moser CC, Chen XX, and Dutton PL (1999) Natural engineering principles of electron tunnelling in biological oxidation-reduction. Nature 402: 47–52. 9. Vale RD and Milligan RA (2000) The way things move: Looking under the hood of molecular motor proteins. Science 288: 88–95. 10. Astumian RD (1997) Thermodynamics and kinetics of a Brownian motor. Science 276: 917–922. 11. Wagner A (2005) Robustness and Evolvability in Living Systems. Princeton, NJ: Princeton University Press. 12. Rennell D, Bouvier SE, Hardy LW, and Poteete AR (1991) Systematic mutation of bacteriophage T4 lysozyme. Journal of Molecular Biology 222: 67–87. 13. Huang W, Petrosino J, Hirsch M, Shenkin PS, and Palzkill T (1996) Amino acid sequence determinants of -lactamase structure and activity. Journal of Molecular Biology 258: 688–703. 14. Hahn MW, Lunsdorf H, Wu QL, et al. (2003) Isolation of novel ultramicrobacteria classified as actinobacteria from five freshwater habitats in Europe and Asia. Applied and Environmental Microbiology 69: 1442–1451. 15. Lewis PJ (2004) Bacterial subcellular architecture: Recent advances and future prospects. Molecular Microbiology 54: 1135–1150. 16. Gitai Z (2005) The new bacterial cell biology: Moving parts and subcellular architecture. Cell 120: 577–586. 17. Carballido-Lopez R (2006) The bacterial actin-like cytoskeleton. Microbiology and Molecular Biology Reviews 70: 888–909. 18. Shih YL and Rothfield L (2006) The bacterial cytoskeleton. Microbiology and Molecular Biology Reviews 70: 729–754. 19. Michie KA and Lowe J (2006) Dynamic filaments of the bacterial cytoskeleton. Annual Review of Biochemistry 75: 467–492. 20. Masuda S and Bauer CE (2002) AppA is a blue light photoreceptor that antirepresses photosynthesis gene expression in Rhodobacter sphaeroides. Cell 110: 613–623. 21. Braatsch S, Gomelsky M, Kuphal S, and Klug G (2002) A single flavoprotein, AppA, integrates both redox and light signals in Rhodobacter sphaeroides. Molecular Microbiology 45: 827–836. 22. Oh JI and Kaplan S (2000) Redox signaling: Globalization of gene expression. EMBO Journal 19: 4237–4247.

320 Single-Molecule and Nanoscale Approaches to Biological Signaling 23. Gentry DR, Hernandez VJ, Nguyen LH, Jensen DB, and Cashel M (1993) Synthesis of the stationary phase sigma factor is positively regulated by ppGpp. Journal of Bacteriology 175: 7982–7989. 24. Cashel M (1975) Regulation of bacterial ppGpp and pppGpp. Annual Review of Microbiology 29: 301–318. 25. Kellermayer MSZ, Smith SB, Granzier HL, and Bustamante C (1997) Folding-unfolding transitions in single titin molecules characterized with laser tweezers. Science 276: 1112–1116. 26. Tskhovrebova L, Trinick J, Sleep JA, and Simmons RM (1997) Elasticity and unfolding of single molecules of the giant muscle protein titin. Nature 387: 308–312. 27. Mehta AD, Rief M, Spudich JA, Smith DA, and Simmons RM (1999) Single molecule biomechanics with optical methods. Science 283: 1689–1695. 28. Smith DE, Tans SJ, Smith SB, Grimes S, Anderson DL, and Bustamante C (2001) The bacteriophage phi 29 portal motor can package DNA against a large internal force. Nature 413: 748–752. 29. Rief M, Gautel M, Oesterhelt F, Fernandez JM, and Gaub HE (1997) Reversible unfolding of individual titin immunoglobulin domains by AFM. Science 276: 1109–1112. 30. Marszalek PE, Lu H, Li HB, et al. (1999) Mechanical unfolding intermediates in titin modules. Nature 402: 100–103. 31. Fisher TE, Oberhauser AF, Carrion-Vazquez M, Marszalek PE, and Fernandez JM (1999) The study of protein mechanics with the atomic force microscope. Trends in Biochemical Sciences 24: 379–384. 32. Muller DJ, Sapra KT, Scheuring S, et al. (2006) Singlemolecule studies of membrane proteins. Current Opinion in Structural Biology 16: 489–495. 33. Yang GL, Cecconi C, Baase WA, et al. (2000) Solid-state synthesis and mechanical unfolding of polymers of T4 lysozyme. Proceedings of the National Academy of Sciences of the United States of America 97: 139–144. 34. Zhao JM, Lee H, Nome RA, Majid S, Scherer NF, and Hoff WD (2006) Single molecule detection of structural changes during PAS domain activation. Proceedings of the National Academy of Sciences of the United States of America 103: 11561–11566. 35. Dietz H and Rief M (2006) Protein structure by mechanical triangulation. Proceedings of the National Academy of Sciences of the United States of America 103: 1244–1247. 36. Oberhauser AF, Hansma PK, Carrion-Vazquez M, and Fernandez JM (2001) Stepwise unfolding of titin under force-clamp atomic force microscopy. Proceedings of the National Academy of Sciences in the United States of America 98: 468–472. 37. Fernandez JM and Li HB (2004) Force-clamp spectroscopy monitors the folding trajectory of a single protein. Science 303: 1674–1678. 38. Nome RA, Zhao JM, Hoff WD, and Scherer NF (2007) Anisotropy in protein unfolding: Integrated nonequilibrium single molecule experiments, analysis, and simulation. Proceedings of the National Academy of Sciences of the United States of America 104: 20799–20804. 39. Meyer TE (1985) Isolation and characterization of soluble cytochromes, ferredoxins and other chromophoric proteins from the halophilic phototrophic bacterium Ectothiorhodospira halophila. Biochimica Biophysica Acta 806: 75–183. 40. Meyer TE, Yakali E, Cusanovich MA, and Tollin G (1987) Properties of a water soluble, yellow protein isolated from a halophilic phototrophic bacterium that has photochemical activity analogous to sensory rhodopsin. Biochemistry 26: 418–423.

41. Pellequer J-L, Wagner-Smith KA, Kay SA, and Getzoff ED (1998) Photoactive yellow protein: A structural prototype for the three-dimensional fold of the PAS domain. Proceedings of the National Academy of Sciences of the United States of America 95: 5884–5890. 42. Cusanovich MA and Meyer TE (2003) Photoactive yellow protein: A prototypic PAS domain sensory protein and development of a common signaling mechanism. Biochemistry 42: 4759–4770. 43. Taylor BL and Zhulin IB (1999) PAS domains: Internal sensors of oxygen, redox potential, and light. Microbiology and Molecular Biology Reviews 63: 479–506. 44. Hefti MH, Franc¸oijs KJ, de Vries SC, Dixon R, and Vervoort J (2004) The PAS fold. A redefinition of the PAS domain based upon structural prediction. European Journal of Biochemistry 271: 1198–1208. 45. Borgstahl GEO, Williams DR, and Getzoff ED (1995) 1.4 A˚ structure of photoactive yellow protein, a cytosolic photoreceptor: Unusual fold, active site and chromophore. Biochemistry 34: 6278–6287. 46. Hellingwerf KJ, Hendriks J, and Gensch T (2003) Photoactive yellow protein, a new type of photoreceptor protein: Will this ‘yellow lab’ bring us where we want to go? Journal of Physical Chemistry A 107: 1082–1094. 47. Bernard C, Houben K, Derix NM, et al. (2005) The solution structure of a transient photoreceptor intermediate: Delta 25 photoactive yellow protein. Structure 13: 953–962. 48. Sprenger WW, Hoff WD, Armitage JP, and Hellingwerf KJ (1993) The Eubacterium Ectothiorhodospira halophila is negatively phototactic, with a wavelength dependence that fits the absorbance spectrum of the photoactive yellow protein. Journal of Bacteriology 175: 3096–3104. 49. Hoff WD, van Stokkum IHM, van Ramesdonk HJ, et al. (1994) Measurement and global analysis of the absorbance changes in the photocycle of the photoactive yellow protein from Ectothiorhodospira halophila. Biophysical Journal 67: 1691–1705. 50. Larsen DS and van Grondelle R (2005) Initial photoinduced dynamics of the photoactive yellow protein. ChemPhysChem 6: 828–837. 51. Hoff WD, Du¨x P, Ha˚rd K, et al. (1994) Thiol ester-linked p-coumaric acid as a new photoactive prosthetic group in a protein with rhodopsin-like photochemistry. Biochemistry 33: 13959–13962. 52. Baca M, Borgstahl GEO, Boissinot M, et al. (1994) Complete chemical structure of photoactive yellow protein: Novel thioester-linked 4-hydroxycinnamyl chromophore and photocycle chemistry. Biochemistry 33: 14369–14377. 53. van Beeumen J, Devreese B, van Bun S, et al. (1993) The primary structure of a photoactive yellow protein from the phototrophic bacterium, Ectothiorhodospira halophila, with evidence for the mass and the binding site of the chromophore. Protein Science 2: 1114–1125. 54. Hoff WD, Devreese B, Fokkens R, et al. (1996) Chemical reactivity and spectroscopy of the thiol ester-linked p-coumaric acid chromophore in the photoactive yellow protein from Ectothiorhodospira halophila. Biochemistry 35: 1274–1281. 55. Hoff WD, van Stokkum IHM, Gural J, and Hellingwerf KJ (1997) Comparison of acid denaturation and light activation in the eubacterial blue-light photoreceptor photoactive yellow protein. Biochimica et Biophysica Acta 1322: 151–162. 56. Kroon A, Hoff WD, Fennema H, et al. (1996) Spectral tuning, fluorescence, and photoactivity in hybrids of photoactive yellow protein, reconstituted with native or modified chromophores. Journal of Biological Chemistry 271: 31949–31956.

Single-Molecule and Nanoscale Approaches to Biological Signaling 57. Kort R, Vonk H, Xu X, Hoff WD, Crielaard W, and Hellingwerf KJ (1996) Evidence for the trans-cis isomerization of the p-coumaric acid chromophore as the photochemical basis of the photocycle of photoactive yellow protein. FEBS Letters 382: 73–78. 58. Xie A, Hoff WD, Kroon AR, and Hellingwerf KJ (1996) Glu46 donates a proton to the 4-hydroxycinnamate anion chromophore during the photocycle of photoactive yellow protein. Biochemistry 35: 14671–14678. 59. Xie A, Kelemen L, Hendriks J, White BJ, Hellingwerf KJ, and Hoff WD (2001) Formation of a new buried charge drives a large-amplitude protein quake in photoreceptor activation. Biochemistry 40: 1510–1517. 60. Meyer TE, Tollin G, Hazzard JH, and Cusanovich MA (1989) Photoactive yellow protein from the purple phototrophic bacterium, Ectothiorhodospira halophila. Quantum yield of photobleaching and effects of temperature, alcohols, glycerol, and sucrose on kinetics of photobleaching and recovery. Biophysical Journal 56: 559–564. 61. van Brederode ME, Hoff WD, van Stokkum IHM, Groot ML, and Hellingwerf KJ (1996) Protein folding thermodynamics applied to the photocycle of the photoactive yellow protein. Biophysical Journal 71: 365–380. 62. Hoff WD, Xie A, van Stokkum IHM, et al. (1999) Global conformational changes upon receptor stimulation in photoactive yellow protein. Biochemistry 38: 1009–1017. 63. Lee B-C, Croonquist PA, Sosnick TR, and Hoff WD (2001) PAS domain receptor photoactive yellow protein is converted to a molten globule state upon activation. Journal of Biological Chemistry 276: 20821–20823. 64. Lee B-C, Pandit A, Croonquist PA, and Hoff WD (2001) Folding and signaling share the same pathway in a photoreceptor. Proceedings of the National Academy of Sciences of the United States of America 98: 9062–9067. 65. Imamoto Y, Kamikubo H, Harigai M, Shimizu N, and Kataoka M (2002) Light-induced global conformational change of photoactive yellow protein in solution. Biochemistry 41: 13595–13601. 66. Derix NM, Wechselberger RW, van der Horst MA, et al. (2003) Lack of negative charge in the E46Q mutant of photoactive yellow protein prevents partial unfolding of the blue-shifted intermediate. Biochemistry 42: 14501–14506. 67. van der Horst MA, van Stokkum IHM, Crielaard W, and Hellingwerf K (2001) The role of the N-terminal domain of photoactive yellow protein in the transient partial unfolding during signalling state formation. FEBS Letters 497: 26–30. 68. Harigai M, Imamoto Y, Kamikubo H, Yamazaki Y, and Kataoka M (2003) Role of an N-terminal loop in the secondary structural change of photoactive yellow protein. Biochemistry 42: 13893–13900. 69. Bryngelson JD, Onuchic JN, Socci ND, and Wolynes PG (1995) Funnels, pathways, and the energy landscape of protein folding – a synthesis. Proteins – Structure Function and Genetics 21: 167–195. 70. Onuchic JN, Luthey-Schulten Z, and Wolynes PG (1997) Theory of protein folding: The energy landscape perspective. Annual Review of Physical Chemistry 48: 545–600. 71. Dill KA and Chan HS (1997) From Levinthal to pathways to funnels. Nature Structural Biology 4: 10–19. 72. Jarzynski C (1997) Nonequilibrium equality for free energy differences. Physical Review Letters 78: 2690–2693. 73. Liphardt J, Dumont S, Smith SB, Tinoco I, and Bustamante C (2002) Equilibrium information from nonequilibrium measurements in an experimental test of Jarzynski’s equality. Science 296: 1832–1835.

321

74. Hummer G and Szabo A (2001) Free energy reconstruction from nonequilibrium single molecule pulling experiments. Proceedings of the National Academy of Sciences of the United States of America 98: 3658–3661. 75. Lu H, Isralewitz B, Krammer A, Vogel V, and Schulten K (1998) Unfolding of titin immunoglobulin domains by steered molecular dynamics simulation. Biophysical Journal 75: 662–671. 76. Carrion-Vazquez M, Li HB, Lu H, Marszalek PE, Oberhauser AF, and Fernandez JM (2003) The mechanical stability of ubiquitin is linkage dependent. Nature Structural Biology 10: 738–743. 77. Brockwell DJ, Paci E, Zinober RC, et al. (2003) Pulling geometry defines the mechanical resistance of a betasheet protein. Nature Structural Biology 10: 731–737. 78. Dietz H, Berkemeier F, Bertz M, and Rief M (2006) Anisotropic deformation response of single protein molecules. Proceedings of the National Academy of Sciences of the United States of America 103: 12724–12728. 79. Cisneros DA, Oberbarnscheidt L, Pannier A, et al. (2008) Transducer binding establishes localized interactions to tune sensory rhodopsin II. Structure 16: 1206–1213. 80. Hoff WD and Spudich JL (2008) Single molecule tour de force: Teasing apart a signaling complex. Structure 16: 1149–1150. 81. Hoff WD, Jung KH, and Spudich JL (1997) Molecular mechanism of photosignaling by archaeal sensory rhodopsins. Annual Review of Biophysics and Biomolecular Structure 26: 223–258. 82. Spudich JL and Bogomolni RA (1988) Sensory rhodopsins of halobacteria. Annual Review of Biophysics and Biophysical Chemistry 17: 193–215. 83. Sasaki J and Spudich JL (2008) Signal transfer in haloarchaeal sensory rhodopsin-transducer complexes. Photochemistry and Photobiology 84: 863–868. 84. Spudich EN, Zhang WS, Alam M, and Spudich JL (1997) Constitutive signaling by the phototaxis receptor sensory rhodopsin II from disruption of its protonated Schiff base Asp-73 interhelical salt bridge. Proceedings of the National Academy of Sciences of the United States of America 94: 4960–4965. 85. Spudich SL and Bogomolni RA (1984) Mechanism of color discrimination by a bacterial sensory rhodopsin. Nature 312: 509–513. 86. Yan B and Spudich JL (1991) Evidence that the repellent receptor form of sensory rhodopsin I is an attractant signaling state. Photochemistry and Photobiology 54: 1023–1026. 87. Yan B, Takahashi T, Johnson R, and Spudich JL (1991) Identification of signaling states of a sensory receptor by modulation of the lifetimes of stimulus-induced conformations – the case of sensory rhodopsin II. Biochemistry 30: 10686–10692. 88. Olson KD, Zhang XN, and Spudich JL (1995) Residue replacements of buried aspartyl and related residues in sensory rhodopsin I – D201N produces inverted phototaxis signals. Proceedings of the National Academy of Sciences of the United States of America 92: 3185–3189. 89. Jung KH and Spudich JL (1996) Protonatable residues at the cytoplasmic end of transmembrane helix-2 in the signal transducer HtrI control photochemistry and function of sensory rhodopsin I. Proceedings of the National Academy of Sciences of the United States of America 93: 6557–6561. 90. Jung KH and Spudich JL (1998) Suppressor mutation analysis of the sensory rhodopsin I-transducer complex: Insights into the color-sensing mechanism. Journal of Bacteriology 180: 2033–2042.

322 Single-Molecule and Nanoscale Approaches to Biological Signaling 91. Bogomolni RA, Stoeckenius W, Szundi I, Perozo E, Olson KD, and Spudich JL (1994) Removal of transducer HtrI allows electrogenic proton translocation by sensory rhodopsin I. Proceedings of the National Academy of Sciences of the United States of America 91: 10188–10192. 92. Wegener AA, Chizhov I, Engelhard M, and Steinhoff HJ (2000) Time-resolved detection of transient movement of helix F in spin-labelled pharaonis sensory rhodopsin II. Journal of Molecular Biology 301: 881–891. 93. Gordeliy VI, Labahn J, Moukhametzianov R, et al. (2002) Molecular basis of transmembrane signalling by sensory rhodopsin II-transducer complex. Nature 419: 484–487. 94. Zhang XN, Zhu JY, and Spudich JL (1999) The specificity of interaction of archaeal transducers with their cognate sensory rhodopsins is determined by their transmembrane helices. Proceedings of the National Academy of Sciences of the United States of America 96: 857–862. 95. Luecke H, Schobert B, Lanyi JK, Spudich EN, and Spudich JL (2001) Crystal structure of sensory rhodopsin II at 2.4 A˚ngstroms: Insights into color tuning and transducer interaction. Science 293: 1499–1503. 96. Oesterhelt F, Oesterhelt D, Pfeiffer M, Engel A, Gaub HE, and Muller DJ (2000) Unfolding pathways of individual bacteriorhodopsins. Science 288: 143–146. 97. Betzig E, Patterson GH, Sougrat R, et al. (2006) Imaging intracellular fluorescent proteins at nanometer resolution. Science 313: 1642–1645. 98. Westphal V, Rizzoli SO, Lauterbach MA, Kamin D, Jahn R, and Hell SW (2008) Video-rate far-field optical nanoscopy dissects synaptic vesicle movement. Science 320: 246–249. 99. Tsien RY (1998) The green fluorescent protein. Annual Review of Biochemistry 67: 509–544. 100. Falke JJ, Bass RB, Butler SL, Chervitz SA, and Danielson MA (1997) The two-component signaling pathway of bacterial chemotaxis: A molecular view of signal transduction by receptors, kinases, and adaptation enzymes. Annual Review of Cell and Developmental Biology 13: 457–512. 101. Bren A and Eisenbach M (2000) How signals are heard during bacterial chemotaxis: Protein–protein interactions in sensory signal propagation. Journal of Bacteriology 182: 6865–6873. 102. Wadhams GH and Armitage JP (2004) Making sense of it all: Bacterial chemotaxis. Nature Reviews Molecular Cell Biology 5: 1024–1037. 103. Baker MD, Wolanin PM, and Stock JB (2006) Signal transduction in bacterial chemotaxis. BioEssays 28: 9–22. 104. Ottemann KM, Xiao WZ, Shin YK, and Koshland DE (1999) A piston model for transmembrane signaling of the aspartate receptor. Science 285: 1751–1754. 105. Falke JJ and Hazelbauer GL (2001) Transmembrane signaling in bacterial chemoreceptors. Trends in Biochemical Sciences 26: 257–265. 106. Berg HC (2003) The rotary motor of bacterial flagella. Annual Review of Biochemistry 72: 19–54. 107. Segall JE, Block SM, and Berg HC (1986) Temporal comparisons in bacterial chemotaxis. Proceedings of the National Academy of Sciences of the United States of America 83: 8987–8991. 108. Li MS and Hazelbauer GL (2004) Cellular stoichiometry of the components of the chemotaxis signaling complex. Journal of Bacteriology 186: 3687–3694. 109. Sourjik V and Berg HC (2002) Receptor sensitivity in bacterial chemotaxis. Proceedings of the National Academy of Sciences of the United States of America 99: 123–127.

110. Sourjik V and Berg HC (2002) Binding of the Escherichia coli response regulator CheY to its target measured in vivo by fluorescence resonance energy transfer. Proceedings of the National Academy of Sciences of the United States of America 99: 12669–12674. 111. Spudich JL and Koshland DE (1976) Non-genetic individuality – chance in single cell. Nature 262: 467–471. 112. Davidson CJ and Surette MG (2008) Individuality in bacteria. Annual Review of Genetics 42: 253–268. 113. Cluzel P, Surette M, and Leibler S (2000) An ultrasensitive bacterial motor revealed by monitoring signaling proteins in single cells. Science 287: 1652–1655. 114. Elowitz MB, Levine AJ, Siggia ED, and Swain PS (2002) Stochastic gene expression in a single cell. Science 297: 1183–1186. 115. Thattai M and van Oudenaarden A (2001) Intrinsic noise in gene regulatory networks. Proceedings of the National Academy of Sciences of the United States of America 98: 8614–8619. 116. McAdams HH and Arkin A (1999) It’s a noisy business! Genetic regulation at the nanomolar scale. Trends in Genetics 15: 65–69. 117. Rao CV, Wolf DM, and Arkin AP (2002) Control, exploitation and tolerance of intracellular noise. Nature 420: 231–237. 118. Barkai N and Leibler S (1997) Robustness in simple biochemical networks. Nature 387: 913–917. 119. Alon U, Surette MG, Barkai N, and Leibler S (1999) Robustness in bacterial chemotaxis. Nature 397: 168–171. 120. Aldana M and Cluzel P (2003) A natural class of robust networks. Proceedings of the National Academy of Sciences of the United States of America 100: 8710–8714. 121. Korobkova E, Emonet T, Vilar JMG, Shimizu TS, and Cluzel P (2004) From molecular noise to behavioural variability in a single bacterium. Nature 428: 574–578. 122. Block SM, Segall JE, and Berg HC (1983) Adaptation kinetics in bacterial chemotaxis. Journal of Bacteriology 154: 312–323. 123. Dubnau D (1991) Genetic competence in Bacillus subtilis. Microbiological Reviews 55: 395–424. 124. Stragier P and Losick R (1996) Molecular genetics of sporulation in Bacillus subtilis. Annual Review of Genetics 30: 297–341. 125. Su¨el GM, Garcia-Ojalvo J, Liberman LM, and Elowitz MB (2006) An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440: 545–550. 126. Woese CR (1987) Bacterial evolution. Microbiological Reviews 51: 221–271. 127. Pace NR (2009) Problems with ‘‘procaryote’’. Journal of Bacteriology 191: 2008–2010. 128. Lowe J and Amos LA (1998) Crystal structure of the bacterial division protein FtsZ. Nature 391: 203–206. 129. Margolin W (2005) FtsZ and the division of prokaryotic cells and organelles. Nature Reviews Molecular Cell Biology 6: 862–871. 130. van den Ent F, Amos LA, and Lowe J (2001) Prokaryotic origin of the actin cytoskeleton. Nature 413: 39–44. 131. Bardy SL and Maddock JR (2007) Polar explorations: Recent insights into the polarity of bacterial components. Current Opinion in Microbiology 10: 617–623. 132. Szurmant L and Ordal GW (2004) Diversity in chemotaxis mechanisms among the bacteria and archaea. Microbiology and Molecular Biology Reviews 68: 301–319. 133. Komeili A, Li Z, Newman DK, and Jensen GJ (2006) Magnetosomes are cell membrane invaginations organized by the actin-like protein MamK. Science 311: 242–245. 134. Scheffel A, Gruska M, Faivre D, Linaroudis A, Plitzko JM, and Schuler D (2006) An acidic protein aligns

Single-Molecule and Nanoscale Approaches to Biological Signaling

135.

136.

137.

138.

139.

140.

141.

142.

143.

144.

145.

146.

147.

magnetosomes along a filamentous structure in magnetotactic bacteria. Nature 440: 110–114. Maddock JR and Shapiro L (1993) Polar localization of the chemoreceptor complex in the Escherichia coli cell. Science 259: 1717–1723. Thiem S, Kentner D, and Sourjik V (2007) Positioning of chemosensory clusters in E. coli and its relation to cell division. EMBO Journal 26: 1615–1623. Sourjik V and Berg HC (2000) Localization of components of the chemotaxis machinery of Escherichia coli using fluorescent protein fusions. Molecular Microbiology 37: 740–751. Kentner D and Sourjik V (2006) Spatial organization of the bacterial chemotaxis system. Current Opinion in Microbiology 9: 619–624. Bray D, Levin MD, and Morton-Firth CJ (1998) Receptor clustering as a cellular mechanism to control sensitivity. Nature 393: 85–88. Duke TAJ and Bray D (1999) Heightened sensitivity of a lattice of membrane receptors. Proceedings of the National Academy of Sciences of the United States of America 96: 10104–10108. Gestwicki JE and Kiessling LL (2002) Inter-receptor communication through arrays of bacterial chemoreceptors. Nature 415: 81–84. Ames P, Studdert CA, Reiser RH, and Parkinson JS (2002) Collaborative signaling by mixed chemoreceptor teams in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America 99: 7060–7065. Sourjik V and Berg HC (2004) Functional interactions between receptors in bacterial chemotaxis. Nature 428: 437–441. Parkinson JS, Ames P, and Studdert C (2005) Collaborative signaling by bacterial chemoreceptors. Current Opinion in Microbiology 8: 116–121. Kim SH, Wang WR, and Kim KK (2002) Dynamic and clustering model of bacterial chemotaxis receptors: Structural basis for signaling and high sensitivity. Proceedings of the National Academy of Sciences of the United States of America 99: 11611–11615. Hazelbauer GL, Falke JJ, and Parkinson JS (2008) Bacterial chemoreceptors: High-performance signaling in networked arrays. Trends in Biochemical Sciences 33: 9–19. Zhang PJ, Khursigara CM, Hartnell LM, and Subramaniam S (2007) Direct visualization of Escherichia coli chemotaxis receptor arrays using cryo-electron microscopy. Proceedings of the National Academy of Sciences of the United States of America 104: 3777–3781.

323

148. Armitage JP (1999) Bacterial tactic responses. Advances in Microbial Physiology 41: 229–289. 149. Armitage JP and Hellingwerf KJ (2003) Light-induced behavioral responses (‘phototaxis’) in prokaryotes. Photosynthesis Research 76: 145–155. 150. Hoff WD, van der Horst MA, Nudel CB, and Hellingwerf KJ (2009) Prokaryotic phototaxis. In: Jin T and Hereld D (eds.) Methods in Molecular Biology: Chemotaxis, pp. 25–50. Totowa, NJ: Humana Press. 151. Porter SL, Wadhams GH, and Armitage JP (2008) Rhodobacter sphaeroides: Complexity in chemotactic signaling. Trends in Microbiology 16: 251–260. 152. Armitage JP and MacNab RM (1987) Unidirectional intermittent rotation of the flagellum of Rhodobacter sphaeroides. Journal of Bacteriology 169: 514–518. 153. Porter SL, Warren AV, Martin AC, and Armitage JP (2002) The third chemotaxis locus of Rhodobacter sphaeroides is essential for chemotaxis. Molecular Microbiology 46: 1081–1094. 154. Storch KF, Rudolph J, and Oesterhelt D (1999) Car: A cytoplasmic sensor responsible for arginine chemotaxis in the archaeon Halobacterium salinarum. EMBO Journal 18: 1146–1158. 155. Wadhams GH, Martin AC, and Armitage JP (2000) Identification and localization of a methyl-accepting chemotaxis protein in Rhodobacter sphaeroides. Molecular Microbiology 36: 1222–1233. 156. Wadhams GH, Warren AV, Martin AC, and Armitage JP (2003) Targeting of two signal transduction pathways to different regions of the bacterial cell. Molecular Microbiology 50: 763–770. 157. Wadhams GH, Martin AC, Porter SL, et al. (2002) TlpC, a novel chemotaxis protein in Rhodobacter sphaeroides, localizes to a discrete region in the cytoplasm. Molecular Microbiology 46: 1211–1221. 158. Wadhams GH, Martin AC, Warren AV, and Armitage JP (2005) Requirements for chemotaxis protein localization in Rhodobacter sphaeroides. Molecular Microbiology 58: 895–902. 159. Hamblin PA, Maguire BA, Grishanin RN, and Armitage JP (1997) Evidence for two chemosensory pathways in Rhodobacter sphaeroides. Molecular Microbiology 26: 1083–1096. 160. Thompson SR, Wadhams GH, and Armitage JP (2006) The positioning of cytoplasmic protein clusters in bacteria. Proceedings of the National Academy of Sciences of the United States of America 103: 8209–8214.