Ubiquitin-Dependent and Independent Proteasomal Degradation in Host-Pathogen Interactions

Ubiquitin, a small protein, is well known for tagging target proteins through a cascade of enzymatic reactions that lead to protein degradation. The ubiquitin tag, apart from its signaling role, is paramount in destabilizing the modified protein. Here, we explore the complex role of ubiquitin-mediated protein destabilization in the intricate proteolysis process by the 26S proteasome. In addition, the significance of the so-called ubiquitin-independent pathway and the role of the 20S proteasome are considered. Next, we discuss the ubiquitin-proteasome system's interplay with pathogenic microorganisms and how the microorganisms manipulate this system to establish infection by a range of elaborate pathways to evade or counteract host responses. Finally, we focus on the mechanisms that rely either on (i) hijacking the host and on delivering pathogenic E3 ligases and deubiquitinases that promote the degradation of host proteins, or (ii) counteracting host responses through the stabilization of pathogenic effector proteins.

Beyond K48 and K63: non-canonical protein ubiquitination

Protein ubiquitination has become one of the most extensively studied post-translational modifications. Originally discovered as a critical element in highly regulated proteolysis, ubiquitination is now regarded as essential for many other cellular processes. This results from the unique features of ubiquitin (Ub) and its ability to form various homo- and heterotypic linkage types involving one of the seven different lysine residues or the free amino group located at its N-terminus. While K48- and K63-linked chains are broadly covered in the literature, the other types of chains assembled through K6, K11, K27, K29, and K33 residues deserve equal attention in the light of the latest discoveries. Here, we provide a concise summary of recent advances in the field of these poorly understood Ub linkages and their possible roles in vivo.

Identification of the Elusive Pyruvate Reductase of Chlamydomonas reinhardtii Chloroplasts

Under anoxic conditions the green alga Chlamydomonas reinhardtii activates various fermentation pathways leading to the creation of formate, acetate, ethanol and small amounts of other metabolites including d-lactate and hydrogen. Progress has been made in identifying the enzymes involved in these pathways and their subcellular locations; however, the identity of the enzyme involved in reducing pyruvate to d-lactate has remained unclear. Based on sequence comparisons, enzyme activity measurements, X-ray crystallography, biochemical fractionation and analysis of knock-down mutants, we conclude that pyruvate reduction in the chloroplast is catalyzed by a tetrameric NAD(+)-dependent d-lactate dehydrogenase encoded by Cre07.g324550. Its expression during aerobic growth supports a possible function as a 'lactate valve' for the export of lactate to the mitochondrion for oxidation by cytochrome-dependent d-lactate dehydrogenases and by glycolate dehydrogenase. We also present a revised spatial model of fermentation based on our immunochemical detection of the likely pyruvate decarboxylase, PDC3, in the cytoplasm.

Insights into the relationship between the haem-binding pocket and the redox potential ofc6cytochromes: four atomic resolution structures ofc6andc6-like proteins fromSynechococcussp. PCC 7002

The structure of cytochromec6Cfrom the mesophilic cyanobacteriumSynechococcussp. PCC 7002 has been determined at 1.03 Å resolution. This is the first structural report on the recently discovered cyanobacterial cytochromec6-like proteins found in marine and nitrogen-fixing cyanobacteria. Despite high similarity in the overall three-dimensional fold between cytochromesc6andc6C, the latter shows saliently different electrostatic properties in terms of surface charge distribution and dipole moments. Its midpoint redox potential is less than half of the value for typicalc6cytochromes and results mainly from the substitution of one residue in the haem pocket. Here, high-resolution crystal structures of mutants of both cytochromesc6andc6Care presented, and the impact of the mutation of specific residues in the haem-binding pocket on the redox potential is discussed. These findings contribute to the elucidation of the structure–function relationship ofc6-like cytochromes.

Crystal structure of CyanoQ from the thermophilic cyanobacterium Thermosynechococcus elongatus and detection in isolated photosystem II complexes

The PsbQ-like protein, termed CyanoQ, found in the cyanobacterium Synechocystis sp. PCC 6803 is thought to bind to the lumenal surface of photosystem II (PSII), helping to shield the Mn4CaO5 oxygen-evolving cluster. CyanoQ is, however, absent from the crystal structures of PSII isolated from thermophilic cyanobacteria raising the possibility that the association of CyanoQ with PSII might not be a conserved feature. Here, we show that CyanoQ (encoded by tll2057) is indeed expressed in the thermophilic cyanobacterium Thermosynechococcus elongatus and provide evidence in support of its assignment as a lipoprotein. Using an immunochemical approach, we show that CyanoQ co-purifies with PSII and is actually present in highly pure PSII samples used to generate PSII crystals. The absence of CyanoQ in the final crystal structure is possibly due to detachment of CyanoQ during crystallisation or its presence in sub-stoichiometric amounts. In contrast, the PsbP homologue, CyanoP, is severely depleted in isolated PSII complexes. We have also determined the crystal structure of CyanoQ from T. elongatus to a resolution of 1.6 Å. It lacks bound metal ions and contains a four-helix up-down bundle similar to the ones found in Synechocystis CyanoQ and spinach PsbQ. However, the N-terminal region and extensive lysine patch that are thought to be important for binding of PsbQ to PSII are not conserved in T. elongatus CyanoQ.

Anchoring a plant cytochrome P450 via PsaM to the thylakoids in Synechococcus sp. PCC 7002: evidence for light-driven biosynthesis

Plants produce an immense variety of specialized metabolites, many of which are of high value as their bioactive properties make them useful as for instance pharmaceuticals. The compounds are often produced at low levels in the plant, and due to their complex structures, chemical synthesis may not be feasible. Here, we take advantage of the reducing equivalents generated in photosynthesis in developing an approach for producing plant bioactive natural compounds in a photosynthetic microorganism by functionally coupling a biosynthetic enzyme to photosystem I. This enables driving of the enzymatic reactions with electrons extracted from the photosynthetic electron transport chain. As a proof of concept, we have genetically fused the soluble catalytic domain of the cytochrome P450 CYP79A1, originating from the endoplasmic reticulum membranes of Sorghum bicolor, to a photosystem I subunit in the cyanobacterium Synechococcus sp. PCC 7002, thereby targeting it to the thylakoids. The engineered enzyme showed light-driven activity both in vivo and in vitro, demonstrating the possibility to achieve light-driven biosynthesis of high-value plant specialized metabolites in cyanobacteria.

Crystal structure of the Psb28 accessory factor of Thermosynechococcus elongatus photosystem II at 2.3 Å

Members of the Psb28 family of proteins are accessory factors implicated in the assembly and repair of the photosystem II complex. We present here the crystal structure of the Psb28 protein (Tlr0493) found in the thermophilic cyanobacterium Thermosynechococcus elongatus at a resolution of 2.3 Å. Overall the crystal structure of the Psb28 monomer is similar to the solution structures of C-terminally His-tagged Psb28-1 from Synechocystis sp. PCC 6803 obtained previously by nuclear magnetic resonance spectroscopy. One new aspect is that Escherichia coli-expressed T. elongatus Psb28 is able to form dimers in solution and packs as a dimer of dimers in the crystal. Analysis of wild type and mutant strains of Synechocystis 6803 by blue native-polyacrylamide gel electrophoresis suggests that Psb28-1, the closest homologue to T. elongatus Psb28 in this organism, also exists as an oligomer in vivo, most likely a dimer. In line with the prediction based on the crystal structure of T. elongatus Psb28, the addition of a 3× Flag-tag to the C-terminus of Synechocystis 6803 Psb28-1 interferes with the accumulation of the Psb28-1 oligomer in vivo. In contrast, the more distantly related Psb28-2 protein found in Synechocystis 6803 lacks the residues that stabilize dimer formation in the T. elongatus Psb28 crystal and is detected as a monomer in vivo. Overall our data suggest that the dimer interface in the Psb28 crystal might be physiologically relevant.

Structure of the RuBisCO chaperone RbcX from the thermophilic cyanobacterium Thermosynechococcus elongatus

The crystal structure of TeRbcX, a RuBisCO assembly chaperone from the cyanobacterium Thermosynechococcus elongatus, a thermophilic organism, has been determined at 1.7 Å resolution. TeRbcX has an unusual cysteine residue at position 103 that is not found in RbcX proteins from mesophilic organisms. Unlike wild-type TeRbcX, a mutant protein with Cys103 replaced by Ala (TeRbcX-C103A) could be readily crystallized. The structure revealed that the overall fold of the TeRbcX homodimer is similar to those of previously crystallized RbcX proteins. Normal-mode analysis suggested that TeRbcX might adopt an open or closed conformation through a hinge movement pivoted on a kink in two long α4 helices. This type of conformational transition is presumably connected to RbcL (the large RuBisCO subunit) binding during the chaperone function of the RuBisCO assembly.

Predictability, complexity, and learning

We define predictive information I(pred)(T) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T:I(pred)(T) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then I(pred)(T) grows logarithmically with a coefficient that counts the dimensionality of the model space. In contrast, power-law growth is associated, for example, with the learning of infinite parameter (or nonparametric) models such as continuous functions with smoothness constraints. There are connections between the predictive information and measures of complexity that have been defined both in learning theory and the analysis of physical systems through statistical mechanics and dynamical systems theory. Furthermore, in the same way that entropy provides the unique measure of available information consistent with some simple and plausible conditions, we argue that the divergent part of I(pred)(T) provides the unique measure for the complexity of dynamics underlying a time series. Finally, we discuss how these ideas may be useful in problems in physics, statistics, and biology.

Efficiency and ambiguity in an adaptive neural code

We examine the dynamics of a neural code in the context of stimuli whose statistical properties are themselves evolving dynamically. Adaptation to these statistics occurs over a wide range of timescales-from tens of milliseconds to minutes. Rapid components of adaptation serve to optimize the information that action potentials carry about rapid stimulus variations within the local statistical ensemble, while changes in the rate and statistics of action-potential firing encode information about the ensemble itself, thus resolving potential ambiguities. The speed with which information is optimized and ambiguities are resolved approaches the physical limit imposed by statistical sampling and noise.

Neural coding of naturalistic motion stimuli

We study a wide-field motion-sensitive neuron in the visual system of the blowfly Calliphora vicina. By rotating the fly on a stepper motor outside in a wooded area, and along an angular motion trajectory representative of natural flight, we stimulate the fly's visual system with input that approaches the natural situation. The neural response is analysed in the framework of information theory, using methods that are free from assumptions. We demonstrate that information about the motion trajectory increases as the light level increases over a natural range. This indicates that the fly's brain utilizes the increase in photon flux to extract more information from the photoreceptor array, suggesting that imprecision in neural signals is dominated by photon shot noise in the physical input, rather than by noise generated within the nervous system itself.

Synergy in a neural code

We show that the information carried by compound events in neural spike trains-patterns of spikes across time or across a population of cells-can be measured, independent of assumptions about what these patterns might represent. By comparing the information carried by a compound pattern with the information carried independently by its parts, we directly measure the synergy among these parts. We illustrate the use of these methods by applying them to experiments on the motion-sensitive neuron H1 of the fly's visual system, where we confirm that two spikes close together in time carry far more than twice the information carried by a single spike. We analyze the sources of this synergy and provide evidence that pairs of spikes close together in time may be especially important patterns in the code of H1.

Adaptive rescaling maximizes information transmission

Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Here, we relate an adaptive property of a sensory system directly to its function as a carrier of information about input signals. We show that the input/output relation of a sensory system in a dynamic environment changes with the statistical properties of the environment. Specifically, when the dynamic range of inputs changes, the input/output relation rescales so as to match the dynamic range of responses to that of the inputs. We give direct evidence that the scaling of the input/output relation is set to maximize information transmission for each distribution of signals. This adaptive behavior should be particularly useful in dealing with the intermittent statistics of natural signals.

On the application of information theory to neural spike trains

The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes are identical so that information is carried only in the spike arrival times. We show how to quantify this information, in bits, free from any assumptions about which features of the spike train or input waveform are most important. We apply this approach to the analysis of experiments on a variety of systems, including some where we confront severe sampling problems, and discuss some to the results obtained and hopes for future extensions.

Reproducibility and variability in neural spike trains

To provide information about dynamic sensory stimuli, the pattern of action potentials in spiking neurons must be variable. To ensure reliability these variations must be related, reproducibly, to the stimulus. For H1, a motion-sensitive neuron in the fly's visual system, constant-velocity motion produces irregular spike firing patterns, and spike counts typically have a variance comparable to the mean, for cells in the mammalian cortex. But more natural, time-dependent input signals yield patterns of spikes that are much more reproducible, both in terms of timing and of counting precision. Variability and reproducibility are quantified with ideas from information theory, and measured spike sequences in H1 carry more than twice the amount of information they would if they followed the variance-mean relation seen with constant inputs. Thus, models that may accurately account for the neural response to static stimuli can significantly underestimate the reliability of signal transfer under more natural conditions.

Adaptation of retinal processing to image contrast and spatial scale

Owing to the limited dynamic range of a neuron's output, neural circuits are faced with a trade-off between encoding the full range of their inputs and resolving gradations among those inputs. For example, the ambient light level varies daily over more than nine orders of magnitude, whereas the firing rate of optic nerve fibres spans less than two. This discrepancy is alleviated by light adaptation: as the mean intensity increases, the retina becomes proportionately less sensitive. However, image statistics other than the mean intensity also vary drastically during routine visual processing. Theory predicts that an efficient visual encoder should adapt its strategy not only to the mean, but to the full shape of the intensity distribution. Here we report that retinal ganglion cells, the output neurons of the retina, adapt to both image contrast-the range of light intensities-and to spatial correlations within the scene, even at constant mean intensity. The adaptation occurs on a scale of seconds, one hundred times more slowly than the immediate light response, and involves 2-5-fold changes in the firing rate. It is mediated within the retinal network: two independent sites of modulation after the photoreceptor cells appear to be involved. Our results demonstrate a remarkable plasticity in retinal processing that may contribute to the contrast adaptation of human vision.

Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents

Natural sounds, especially communication sounds, have highly structured amplitude and phase spectra. We have quantified how structure in the amplitude spectrum of natural sounds affects coding in primary auditory afferents. Auditory afferents encode stimuli with naturalistic amplitude spectra dramatically better than broad-band stimuli (approximating white noise); the rate at which the spike train carries information about the stimulus is 2-6 times higher for naturalistic sounds. Furthermore, the information rates can reach 90% of the fundamental limit to information transmission set by the statistics of the spike response. These results indicate that the coding strategy of the auditory nerve is matched to the structure of natural sounds; this 'tuning' allows afferent spike trains to provide higher processing centres with a more complete description of the sensory world.

Reliability and information transmission in spiking neurons

Spiking neurons encode continuous, time-varying signals in sequences of identical action potentials. Relatively simple algorithms allow one to 'decode' this neural representation of sensory data to estimate the input signals. Decoding experiments provide a quantitative characterization of information transmission and computational reliability under real-time conditions. The results of these studies show that neural coding and computation in several systems approach fundamental physical and informational theoretic limits to performance.

Vibrationally enhanced tunneling as a mechanism for enzymatic hydrogen transfer

We present a theory of enzymatic hydrogen transfer in which hydrogen tunneling is mediated by thermal fluctuations of the enzyme's active site. These fluctuations greatly increase the tunneling rate by shortening the distance the hydrogen must tunnel. The average tunneling distance is shown to decrease when heavier isotopes are substituted for the hydrogen or when the temperature is increased, leading to kinetic isotope effects (KIEs)--defined as the factor by which the reaction slows down when isotopically substituted substrates are used--that need be no larger than KIEs for nontunneling mechanisms. Within this theory we derive a simple KIE expression for vibrationally enhanced ground state tunneling that is able to fit the data for the bovine serum amine oxidase (BSAO) system, correctly predicting the large temperature dependence of the KIEs. Because the KIEs in this theory can resemble those for nontunneling dynamics, distinguishing the two possibilities requires careful measurements over a range of temperatures, as has been done for BSAO.

Virtual intermediates in photosynthetic electron transfer

We explore the possibility of virtual transfer in the primary charge separation of photosynthetic bacteria within the context of several types of experimental data. We show that the peak that might be expected in the virtual rate as electric fields vary the intermediate state energy is severely broadened by coupling to high-frequency modes. The Stark absorption kinetics data are thus consistent with virtual transfer in the primary charge separation. High-frequency coupling also makes the temperature dependence weak over a wide range of parameters. We demonstrate that Stark fluorescence anisotropy data, usually taken as evidence of virtual transfer, can in fact be consistent with two-step transfer. We suggest a two-pulse excitation experiment to quantify the contributions from two-step and virtual transfer. We show that virtual absorption into a charge transfer state can make a substantial contribution to the Stark absorption spectrum in a way that is not related to any derivative of the absorption spectrum.

Reading a neural code

Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task--extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train. These methods were applied to an identified movement-sensitive neuron in the fly visual system. Such decoding experiments determined the effective noise level and fault tolerance of neural computation, and the structure of the decoding algorithms suggested a simple model for real-time analog signal processing with spiking neurons.

Temporal filtering in retinal bipolar cells. Elements of an optimal computation?

Recent experiments indicate that the dark-adapted vertebrate visual system can count photons with a reliability limited by dark noise in the rod photoreceptors themselves. This suggests that subsequent layers of the retina, responsible for signal processing, add little if any excess noise and extract all the available information. Given the signal and noise characteristics of the photoreceptors, what is the structure of such an optimal processor? We show that optimal estimates of time-varying light intensity can be accomplished by a two-stage filter, and we suggest that the first stage should be identified with the filtering which occurs at the first anatomical stage in retinal signal processing, signal transfer from the rod photoreceptor to the bipolar cell. This leads to parameter-free predictions of the bipolar cell response, which are in excellent agreement with experiments comparing rod and bipolar cell dynamics in the same retina. As far as we know this is the first case in which the computationally significant dynamics of a neuron could be predicted rather than modeled.

Quantum and classical dynamics in biochemical reactions

The classic experiment of deVault and Chance touched off a long series of theoretical and experimental studies of the interplay between quantum and classical dynamics in photosynthetic electron transfer. More recently these issues have also been addressed in experiments on ligand binding reactions in heme proteins and through the study of kinetic isotope effects in enzymatic proton transfer. Theoretical effort has focused on a class of relatively simple models which display a surprisingly rich spectrum of dynamical behavior. Much less attention has been paid to a very important issue: Why are we allowed to use such simple models to describe such obviously complex molecules? Here we provide some tentative answers to this question, contrasting the cases of electron and proton transfer. We suggest that ideas based on simple models can inspire novel strategies for 'realistic' simulations, and that we can begin to think about the general problems of enzymatic catalysis in terms of dynamical pictures that previously have been applied only to the simpler case of electron transfer.

Protein dynamics and reaction rates: mode-specific chemistry in large molecules?

Reactive events in proteins may be strongly coupled to a few specific modes of protein motion or they may couple nonspecifically to the dense continuum of protein and solvent modes. We summarize the evidence that at least some biologically important reactions can be described in terms of a few specific modes, and we propose experiments to quantify the strength of coupling to the continuum. We also show that large entropic effects--solvent ordering, for example--can be rigorously incorporated in few-mode models without losing mode specificity. Within our description, the dynamics that determine chemical reaction rates can be summarized by a small number of parameters directly related to spectroscopic and thermodynamic data. Mode specificity allows protein dynamics to contribute directly to the control and specificity of biochemical reaction rates.

Do vibrational spectroscopies uniquely describe protein dynamics? The case for myoglobin

We develop a quasi-harmonic description of protein dynamics and apply this description to the anomalous Mössbauer, infrared, x-ray diffraction, and EXAFS (extended x-ray absorption fine structure spectroscopy) data that are available for myoglobin (Mb) and its interactions with carbon monoxide (CO). In the quasi-harmonic approximation the dynamical parameters derived from these spectroscopic data are relevant in the calculation of reaction rates, and we give a quantitative description of the nonexponential kinetics of Mb-CO binding observed at low temperatures. All these data have previously been interpreted in terms of the more complex conformational substates model for protein dynamics. We point out several problems with this model and propose experiments that can provide detailed tests of the quasi-harmonic theory proposed here.

Contraction of glycerinated muscle fibers as a function of the ATP concentration

We have measured the force-velocity curves of glycerinated rabbit psoas fibers over a range of ATP concentration from 2.5 microM to 5 mM. As the ATP concentration is increased, the isometric tension increases to a maximum around 50 microM, then decreases to a plateau at 70% of the maximum by 1 mM ATP. At low ATP concentrations the maximum velocity of contraction is low and increases with increasing ATP, reaching a plateau at approximately 2 lengths per second by 1 mM ATP. Our studies suggest that the binding of ATP dissociates the myosin head from actin in the contracting muscle, a reaction similar to that seen in solution. We have constructed models of the actin-myosin-nucleotide interactions based on a kinetic scheme derived from solution studies. The fit of these models to the data shows that the rates of some reactions in the fiber must be considerably different from the rates of the analogous reactions in solution. The data is best fit by models in which head attachment occurs rapidly at the beginning of a power stroke, head detachment occurs rapidly at the end of the power stroke, and the force produced by a myosin head in a power stroke is independent of velocity.