Stochastic proton pumping ATPases: from single molecules to diverse physiological roles

We discuss the most recent reports on two proton pumps, F-ATPase (ATP synthase) and V-ATPase (endomembrane proton pump). They are formed from similar extrinsic (F1 or V1) and intrinsic (Fo or Vo) membrane sectors, and couple chemistry and proton transport through subunit rotation for apparently different physiological roles. Emphasis is placed on the stochastic rotational catalysis of F-ATPase and isoforms of V-ATPase.     

Measuring gene expression by quantitative proteome analysis

Proteome analysis is most commonly accomplished by the combination of two-dimensional gel electrophoresis for protein separation, visualization, and quantification and mass spectrometry for protein identification. Over the past year, exceptional progress has been made towards developing a new technology base for the precise quantification and identification of proteins in complex mixtures, that is, quantitative proteomics.     

Stochastic gene expression: bacterial elites in chemotaxis

Even in the absence of genetic or environmental differences, cells differ from each other in their molecular make‐up. The consequences of these phenotypic differences are often not well understood. New work by Waite et al ( 2016 ) directly links variation in the molecular composition of individual bacterial cells to their population‐level performance.     

The proteome of the presynaptic active zone: from docked synaptic vesicles to adhesion molecules and maxi-channels

The presynaptic proteome controls neurotransmitter release and the short and long term structural and functional dynamics of the nerve terminal. Using a monoclonal antibody against synaptic vesicle protein 2 we immunopurified a presynaptic compartment containing the active zone with synaptic vesicles docked to the presynaptic plasma membrane as well as elements of the presynaptic cytomatrix. Individual protein bands separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis were subjected to nanoscale-liquid chromatography electrospray ionization-tandem mass spectrometry. Combining this method with 2-dimensional benzyldimethyl- n -hexadecylammonium chloride/sodium dodecyl sulfate-polyacrylamide gel electrophoresis and matrix-assisted laser desorption ionization time of flight and immunodetection we identified 240 proteins comprising synaptic vesicle proteins, components of the presynaptic fusion and retrieval machinery, proteins involved in intracellular signal transduction, a large variety of adhesion molecules and proteins potentially involved in regulating the functional and structural dynamics of the pre-synapse. Four maxi-channels, three isoforms of voltage-dependent anion channels and the tweety homolog 1 were co-isolated with the docked synaptic vesicles. As revealed by in situ hybridization, tweety homolog 1 reveals a distinct expression pattern in the rodent brain. Our results add novel information to the proteome of the presynaptic active zone and suggest that in particular proteins potentially involved in the short and long term structural modulation of the mature presynaptic compartment deserve further detailed analysis.     

From global proteome profiling to single targeted molecules of follicular fluid and oocyte: contribution to embryo development and IVF outcome

The development of in vitro fertilization (IVF) techniques for infertility management has led to the investigation of the proteome of follicular fluid and oocyte. In addition, different markers contributing to oocyte maturation and embryo development potential have been reported in the literature. Different techniques were utilized to analyze whole proteome or single protein markers in follicular fluid and oocytes, particularly in animal models. Data from several studies have generated large amounts of information, however, an ideal profile to predict the best oocytes and embryos suitable for implantation are still to be uncovered. The identification of such profiles and markers from follicular fluid, oocytes and endometrium should help scientists and clinicians develop better strategies to improving clinical outcome of IVF cycles.     

Dynamic covariation between gene expression and proteome characteristics

Background  

Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to changes in gene and protein expression. Since the majority of genes encode proteins, we investigated possible correlations between protein parameters and gene expression patterns to identify proteome-wide characteristics indicative of trends common to expressed proteins.     

Stochastic gene expression in switching environments

Organisms are known to adapt to regularly varying environments. However, in most cases, the fluctuations of the environment are irregular and stochastic, alternating between favorable and unfavorable regimes, so that cells must cope with an uncertain future. A possible response is population diversification. We assume here that the cell population is divided into two groups, corresponding to two phenotypes, having distinct growth rates, and that cells can switch randomly their phenotypes. In static environments, the net growth rate is maximized when the population is homogeneously composed of cells having the largest growth rate. In random environments, growth rates fluctuate and observations reveal that sometimes heterogeneous populations have a larger net growth rate than homogeneous ones, a fact illustrated recently through Monte-Carlo simulations based on a birth and migration process in a random environment. We study this process mathematically by focusing on the proportion f(t) of cells having the largest growth rate at time t, and give explicitly the related steady state distribution π. We also prove the convergence of empirical averages along trajectories to the first moment , and provide efficient numerical methods for computing .      

Stochastic gene expression in fluctuating environments

Stochastic mechanisms can cause a group of isogenic bacteria, each subject to identical environmental conditions, to nevertheless exhibit diverse patterns of gene expression. The resulting phenotypic subpopulations will typically have distinct growth rates. This behavior has been observed in several contexts, including sugar metabolism and pili phase variation. Under fixed environmental conditions, the net growth rate of the population is maximized when all cells are of the fastest growing phenotype, so it is unclear what fitness advantage is conferred by population heterogeneity. However, unlike ideal laboratory conditions, natural environments tend to fluctuate, either periodically or randomly. Here we use a stochastic population model to show that, during growth in such fluctuating environments, a dynamically heterogenous bacterial population can sometimes achieve a higher net growth rate than a homogenous one. By using stochastic mechanisms to sample several distinct phenotypes, the bacteria are able to anticipate and take advantage of sudden changes in their environment. However, this heterogeneity is beneficial only if the bacterial response rate is sufficiently low. Our results could be useful in the design of artificial evolution experiments and in the optimization of fermentation processes.     

Stochastic dynamic study of optical transition properties of single GFP-like molecules

Due to high fluctuations and quantum uncertainty, the processes of single-molecules should be treated by stochastic methods. To study fluorescence time series and their statistical properties, we have applied two stochastic methods, one of which is an analytic method to study the off-time distributions of certain fluorescence transitions and the other is Gillespie’s method of stochastic simulations. These methods have been applied to study the optical transition properties of two single-molecule systems, GFPmut2 and a Dronpa-like molecule, to yield results in approximate agreement with experimental observations on these systems. Rigorous oscillatory time series of GFPmut2 before it unfolds in the presence of denaturants have not been obtained based on the stochastic method used, but, on the other hand, the stochastic treatment puts constraints on the conditions under which such oscillatory behavior is possible. Furthermore, a sensitivity analysis is carried out on GFPmut2 to assess the effects of transition rates on the observables, such as fluorescence intensities.     

Stochastic models for inferring genetic regulation from microarray gene expression data

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information.     

Stochastic modeling of gene positive autoregulation networks involving signal molecules

Positive autoregulation in gene regulation networks has been shown in the past to exhibit stochastic behavior, including stochastic bistability, in which an initially uniform cell population develops into two distinct subpopulations. However, positive autoregulation is often mediated by signal molecules, which have not been considered in prior stochastic analysis of these networks. Here we propose both a full model of such a network that includes a signal molecule, and a simplified model in which the signal molecules have been eliminated through the use of two simplifications. The simplified model is amenable to direct mathematical analysis that shows that stochastic bistability is possible. We use stochastic Petri networks for simulating both types of models. The simulation results show that 1), the stochastic behavior of the two models is similar; and 2), that the analytical steady-state distribution of the simplified model matches well the transient results at times equal to that of a cell generation. A discussion of the simplifications we used in the context of the results indicates the importance of the signal molecule number as a factor determining the presence of bistability. This is further supported from a deterministic steady-state analysis of the full model that is shown to be a useful indicator of potential stochastic bistability. We use the regulation of SdiA in Escherichia coli as an example, due to the importance of this protein and of the signal molecule, a bacterial autoinducer, that is involved. However, the use of kinetic parameter values representing typical cellular activities make the conclusions applicable to other signal-mediated positive autoregulation networks as well.