Advances in protein turnover analysis at the global level and biological insights

The Metabolic Flux Probe (MFP)-Secreted Protein as a Non-Disruptive Information Carrier for 13C-Based Metabolic Flux Analysis

Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for ¹³C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This “metabolic flux probe” (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for ¹³C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.

Degradation of protein translation machinery by amino acid starvation-induced macroautophagy

Macroautophagy is regarded as a nonspecific bulk degradation process of cytoplasmic material within the lysosome. However, the process has mainly been studied by nonspecific bulk degradation assays using radiolabeling. In the present study we monitor protein turnover and degradation by global, unbiased approaches relying on quantitative mass spectrometry-based proteomics. Macroautophagy is induced by rapamycin treatment, and by amino acid and glucose starvation in differentially, metabolically labeled cells. Protein dynamics are linked to image-based models of autophagosome turnover. Depending on the inducing stimulus, protein as well as organelle turnover differ. Amino acid starvation-induced macroautophagy leads to selective degradation of proteins important for protein translation. Thus, protein dynamics reflect cellular conditions in the respective treatment indicating stimulus-specific pathways in stress-induced macroautophagy.

Mechanisms of protein balance in skeletal muscle

Increased global demand for adequate protein nutrition against a backdrop of climate change and concern for animal agriculture sustainability necessitates new and more efficient approaches to livestock growth and production. Anabolic growth is achieved when rates of new synthesis exceed turnover, producing a positive net protein balance. Conversely, deterioration or atrophy of lean mass is a consequence of a net negative protein balance. During early life and periods of growth, muscle mass is driven by increases in protein synthesis at the level of mRNA translation. Throughout life, muscle mass is further influenced by degradative processes such as autophagy and the ubiquitin proteasome pathway. Multiple signal transduction networks guide and coordinate these processes alongside quality control mechanisms to maintain protein homeostasis (proteostasis). Genetics, hormones, and environmental stimuli each influence proteostasis control, altering capacity and/or efficiency of muscle growth. An overview of recent findings and current methods to assess muscle protein balance and proteostasis is presented. Current efforts to identify novel control points have the potential through selective breeding design or development of hormetic strategies to better promote growth and health span during environmental stress.

Gaussian Process Modeling of Protein Turnover

We describe a stochastic model to compute in vivo protein turnover rate constants from stable-isotope labeling and high-throughput liquid chromatography-mass spectrometry experiments. We show that the often-used one- and two-compartment nonstochastic models allow explicit solutions from the corresponding stochastic differential equations. The resulting stochastic process is a Gaussian processes with Ornstein-Uhlenbeck covariance matrix. We applied the stochastic model to a large-scale data set from (15)N labeling and compared its performance metrics with those of the nonstochastic curve fitting. The comparison showed that for more than 99% of proteins, the stochastic model produced better fits to the experimental data (based on residual sum of squares). The model was used for extracting protein-decay rate constants from mouse brain (slow turnover) and liver (fast turnover) samples. We found that the most affected (compared to two-exponent curve fitting) results were those for liver proteins. The ratio of the median of degradation rate constants of liver proteins to those of brain proteins increased 4-fold in stochastic modeling compared to the two-exponent fitting. Stochastic modeling predicted stronger differences of protein turnover processes between mouse liver and brain than previously estimated. The model is independent of the labeling isotope. To show this, we also applied the model to protein turnover studied in induced heart failure in rats, in which metabolic labeling was achieved by administering heavy water. No changes in the model were necessary for adapting to heavy-water labeling. The approach has been implemented in a freely available R code.

Degradation Parameters from Pulse-Chase Experiments

Pulse-chase experiments are often used to study the degradation of macromolecules such as proteins or mRNA. Considerations for the choice of pulse length include the toxicity of the pulse to the cell and maximization of labeling. In the general case of non-exponential decay, varying the length of the pulse results in decay patterns that look different. Analysis of these patterns without consideration to pulse length would yield incorrect degradation parameters. Here we propose a method that constructively includes pulse length in the analysis of decay patterns and extracts the parameters of the underlying degradation process. We also show how to extract decay parameters reliably from measurements taken during the pulse phase.

Elements of the cellular metabolic structure

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell.

Protein turnover in plant biology

The protein content of plant cells is constantly being updated. This process is driven by the opposing actions of protein degradation, which defines the half-life of each polypeptide, and protein synthesis. Our understanding of the processes that regulate protein synthesis and degradation in plants has advanced significantly over the past decade. Post-transcriptional modifications that influence features of the mRNA populations, such as poly(A) tail length and secondary structure, contribute to the regulation of protein synthesis. Post-translational modifications such as phosphorylation, ubiquitination and non-enzymatic processes such as nitrosylation and carbonylation, govern the rate of degradation. Regulators such as the plant TOR kinase, and effectors such as the E3 ligases, allow plants to balance protein synthesis and degradation under developmental and environmental change. Establishing an integrated understanding of the processes that underpin changes in protein abundance under various physiological and developmental scenarios will accelerate our ability to model and rationally engineer plants.

Stressor-induced proteome alterations in zebrafish: a meta-analysis of response patterns

Proteomics approaches are being increasingly applied in ecotoxicology on the premise that the identification of specific protein expression changes in response to a particular chemical would allow elucidation of the underlying molecular pathways leading to an adverse effect. This in turn is expected to promote the development of focused testing strategies for specific groups of toxicants. Although both gel-based and gel-free global characterization techniques provide limited proteome coverage, the conclusions regarding the cellular processes affected are still being drawn based on the few changes detected. To investigate how specific the detected responses are, we analyzed a set of studies that characterized proteome alterations induced by various physiological, chemical and biological stressors in zebrafish, a popular model organism. Our analysis highlights several proteins and protein groups, including heat shock and oxidative stress defense proteins, energy metabolism enzymes and cytoskeletal proteins, to be most frequently identified as responding to diverse stressors. In contrast, other potentially more specifically responding protein groups are detected much less frequently. Thus, zebrafish proteome responses to stress reported by different studies appear to depend mostly on the level of stress rather than on the specific stressor itself. This suggests that the most broadly used current proteomics technologies do not provide sufficient proteome coverage to allow in-depth investigation of specific mechanisms of toxicant action. We suggest that the results of any differential proteomics experiment performed with zebrafish should be interpreted keeping in mind the list of the most frequent responders that we have identified. Similar reservations should apply to any other species where proteome responses are analyzed by global proteomics methods. Careful consideration of the reliability and significance of observed changes is necessary in order not to over-interpret the experimental results and to prevent the proliferation of false positive linkages between the chemical and the cellular functions it perturbs. We further discuss the implications of the identified "top lists" of frequently responding proteins and protein families, and suggest further directions for proteomics research in ecotoxicology. Apart from improving the proteome coverage, further research should focus on defining the significance of the observed stress response patterns for organism phenotypes and on searching for common upstream regulators that can be targeted by specific assays.

Quantitation of human peptides and proteins via MS: review of analytically validated assays

Since the development of monoclonal antibodies in the 1970s, antibody-based assays have been used for the quantitation of proteins and peptides and, today, they are the most widely used technology in routine laboratory medicine and bioanalysis. However, in the last couple of decades, liquid chromatography-mass spectrometry/mass spectrometry (LC–MS/MS) techniques have been adopted in the quantitation of small molecules, and more recently have made significant contributions in the quantitation of proteins and peptides. In this article, we will review clinical MS-based assays for endogenous peptides, proteins, and therapeutic antibodies, for which validated methods exist. We will also cover the measurement of protein turnover and the unique solutions that MS can offer in this field.

Recent advances in the development of single cell analysis--a review

Development of techniques for the analysis of the content of individual cells represents an important direction in modern bioanalytical chemistry. While the analysis of chromosomes, organelles, or location of selected proteins has been traditionally the domain of microscopic techniques, the advances in miniaturized analytical systems bring new possibilities for separations and detections of molecules inside the individual cells including smaller molecules such as hormones or metabolites. It should be stressed that the field of single cell analysis is very broad, covering advanced optical, electrochemical and mass spectrometry instrumentation, sensor technology and separation techniques. The number of papers published on single cell analysis has reached several hundred in recent years. Thus a complete literature coverage is beyond the limits of a journal article. The following text provides a critical overview of some of the latest developments with the main focus on mass spectrometry, microseparation methods, electrophoresis in capillaries and microfluidic devices and respective detection techniques for performing single cell analyses.

Measuring the dynamics of E. coli ribosome biogenesis using pulse-labeling and quantitative mass spectrometry

The ribosome is an essential organelle responsible for cellular protein synthesis. Until recently, the study of ribosome assembly has been largely limited to in vitro assays, with few attempts to reconcile these results with the more complex ribosome biogenesis process inside the living cell. Here, we characterize the ribosome synthesis and assembly pathway for each of the E. coli ribosomal protein (r-protein) in vivo using a stable isotope pulse-labeling timecourse. Isotope incorporation into assembled ribosomes was measured by quantitative mass spectrometry (qMS) and fit using steady-state flux models. Most r-proteins exhibit precursor pools ranging in size from 0% to 7% of completed ribosomes, and the sizes of these individual r-protein pools correlate well with the order of r-protein binding in vitro. Additionally, we observe anomalously large precursor pools for specific r-proteins with known extra-ribosomal functions, as well as three r-proteins that apparently turnover during steady-state growth. Taken together, this highly precise, time-dependent proteomic qMS approach should prove useful in future studies of ribosome biogenesis and could be easily extended to explore other complex biological processes in a cellular context.

Determining degradation and synthesis rates of arabidopsis proteins using the kinetics of progressive 15N labeling of two-dimensional gel-separated protein spots

The growth and development of plant tissues is associated with an ordered succession of cellular processes that are reflected in the appearance and disappearance of proteins. The control of the kinetics of protein turnover is central to how plants can rapidly and specifically alter protein abundance and thus molecular function in response to environmental or developmental cues. However, the processes of turnover are largely hidden during periods of apparent steady-state protein abundance, and even when proteins accumulate it is unclear whether enhanced synthesis or decreased degradation is responsible. We have used a (15)N labeling strategy with inorganic nitrogen sources coupled to a two-dimensional fluorescence difference gel electrophoresis and mass spectrometry analysis of two-dimensional IEF/SDS-PAGE gel spots to define the rate of protein synthesis (K(S)) and degradation (K(D)) of Arabidopsis cell culture proteins. Through analysis of MALDI-TOF/TOF mass spectra from 120 protein spots, we were able to quantify K(S) and K(D) for 84 proteins across six functional groups and observe over 65-fold variation in protein degradation rates. K(S) and K(D) correlate with functional roles of the proteins in the cell and the time in the cell culture cycle. This approach is based on progressive (15)N labeling that is innocuous for the plant cells and, because it can be used to target analysis of proteins through the use of specific gel spots, it has broad applicability.

Measurement of human plasma proteome dynamics with (2)H(2)O and liquid chromatography tandem mass spectrometry

Dysfunction of protein turnover is a feature of many human diseases, and proteins are substrates in important biological processes. Currently, no method exists for the measurement of global protein turnover (i.e., proteome dynamics) that can be applied in humans. Here we describe the use of metabolic labeling with deuterium ((2)H) from (2)H(2)O and liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of mass isotopomer patterns to measure protein turnover. We show that the positions available for (2)H label incorporation in vivo can be calculated using peptide sequence. The isotopic incorporation values calculated by combinatorial analysis of mass isotopomer patterns in peptides correlate very closely with values established for individual amino acids. Inpatient and outpatient heavy water labeling protocols resulted in (2)H label incorporation sufficient for reproducible quantitation in humans. Replacement rates were similar for peptides deriving from the same protein. Using a kinetic model to account for the time course of each individual's (2)H(2)O enrichment curves, dynamics of approximately 100 proteins with half-lives ranging from 0.4 to 40 days were measured using 8 μl of plasma. The measured rates were consistent with literature values. This method can be used to measure in vivo proteome homeostasis in humans in disease and during therapeutic interventions.

The dynamic state of protein turnover: It's about time

The continual destruction and renewal of proteins that maintain cellular homeostasis has been rigorously studied since the late 1930s. Experimental techniques for measuring protein turnover have evolved to measure the dynamic regulation of key proteins and now, entire proteomes. In the past decade, the proteomics field has aimed to discover how cells adjust their proteomes to execute numerous regulatory programs in response to specific cellular and environmental cues. By combining classical biochemical techniques with modern, high-throughput technologies, researchers have begun to reveal the synthesis and degradation mechanisms that shape protein turnover on a global scale. This review examines several recent developments in protein turnover research, emphasizing the combination of metabolic labeling and mass spectrometry.

Measuring the turnover rates of Arabidopsis proteins using deuterium oxide: an auxin signaling case study

Rapid environmental responses in plants rely on endogenous signaling mechanisms, which in many cases are mediated by changes in protein turnover rates. It is therefore necessary to develop methods for measuring protein dynamics that monitor large sets of plant proteins to begin to apply a systems biology approach to the study of plant behavior. The use of stable isotope labeling strategies that are adaptable to proteomic methods is particularly attractive for this purpose. Here, we explore one example of such methods that is particularly suitable for plants at the seedling stage, where measurement of amino acid and protein turnover rates is accomplished using a heavy water labeling strategy. The method is backed by microarray evaluation to define its feasibility for specific experimental approaches, and the CULLIN-ASSOCIATED AND NEDDYLATION DISSOCIATED 1 (CAND1) and TRANSPORT INHIBITOR RESPONSE 1 (TIR1) proteins are used to illustrate the potential utility in understanding hormonal signaling regulation. These studies provide insight not only into the potential utility of the method, but also address possible areas of concern regarding the use of heavy water labeling during plant growth. These considerations suggest a prescription for specific experimental designs that minimize interference resulting from the induction of treatment-specific gene expression in the results obtained.

Protein turnover in mycobacterial proteomics

Understanding the biology of Mycobacterium tuberculosis is one of the primary challenges in current tuberculosis research. Investigation of mycobacterial biology using the systems biology approach has deciphered much information with regard to the bacilli and tuberculosis pathogenesis. The modulation of its environment and the ability to enter a dormant phase are the hallmarks of M. tuberculosis. Until now, proteome studies have been able to understand much about the role of various proteins, mostly in growing M. tuberculosis cells. It has been difficult to study dormant M. tuberculosis by conventional proteomic techniques with very few proteins being found to be differentially expressed. Discrepancy between proteome and transcriptome studies lead to the conclusion that a certain aspect of the mycobacterial proteome is not being explored. Analysis of protein turnover may be the answer to this dilemma. This review, while giving a gist of the proteome response of mycobacteria to various stresses, analyzes the data obtained from abundance studies versus data from protein turnover studies in M. tuberculosis. This review brings forth the point that protein turnover analysis is capable of discerning more subtle changes in protein synthesis, degradation, and secretion activities. Thus, turnover studies could be incorporated to provide a more in-depth view into the proteome, especially in dormant or persistent cells. Turnover analysis might prove helpful in drug discovery and a better understanding of the dynamic nature of the proteome of mycobacteria.