Hepatocyte lipid and glucose metabolism is regulated not only by major hormones like insulin and glucagon but also by many other factors, including calcium ions. Recently, mitochondria-associated membrane (MAM) dysfunction combined with incorrect IP3-receptor regulation has been shown to result in abnormal calcium signaling in hepatocytes. This dysfunction could further lead to hepatic metabolism pathology. However, the exact contribution of MAM dysfunction, incorrect IP3-receptor regulation and insulin resistance to the calcium-insulin-glucagon interplay is not understood yet. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network with a detailed focus on the model construction details besides the biological aspect. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network. We focus on the model construction details, model validation, and predictions. We describe the dynamic regulation of signaling processes by sigmoid Hill function. In particular, we study the effect of both the Hill function slope and the distance between Hill function extremes on metabolic processes in hepatocytes as a model of nonspecific insulin dysfunction. We also address the significant time difference between characteristic time of glucose hepatic processing and a typical calcium oscillation period in hepatocytes. Our modeling results show that calcium signaling dysfunction results in an abnormal increase in postprandial glucose levels, an abnormal glucose decrease in fasting, and a decreased amount of stored glycogen. An insulin dysfunction of glucose phosphorylation, glucose dephosphorylation, and glycogen breakdown also cause a noticeable effect. We also get some insight into the so-called hepatic insulin resistance paradox, confirming the hypothesis regarding indirect insulin action on hepatocytes via dysfunctional adipocyte lipolysis.

The escalating impact of climate change and ultraviolet (UV) radiation is subjecting plants to unique combinations of UV-B and drought stress. These combined stressors could have additive, synergistic, or antagonistic effects, but the precise nature of these impacts remains uncertain, hampering our ability to predict plant adaptations approach towards stressors. Our analysis of various studies shows that UV-B or drought conditions detrimentally influence plant growth and health metrics by the enhanced generation of reactive oxygen species causing damage to lipids, proteins, carbohydrates and DNA. Further reducing biomass accumulation, plant height, photosynthetic efficiency, leaf area, and water transpiration, while enhancing stress-related symptoms. In response to UV-B radiation and drought stress, plants exhibit a notable up-regulation of specific acclimation-associated metabolites, including proline, flavonoids, anthocyanins, unsaturated fatty acids, and antioxidants. These metabolites play a pivotal role in conferring protection against environmental stresses. Their biosynthesis and functional roles are potentially modulated by signalling molecules such as hydrogen peroxide, abscisic acid, jasmonic acid, salicylic acid, and ethylene, all of which have associated genetic markers that further elucidate their involvement in stress response pathways. In comparison to single stress, the combination of UV-B and drought induces the plant defence responses and growth retardation which are less-than-additive. This sub-additive response, consistent across different study environments, suggests the possibility of a cross-resistance mechanism. Our outlines imply that the adverse effects of increased drought and UV-B could potentially be mitigated by cross-talk between UV-B and drought regimes utilizing a multidimensional approach. This crucial insight could contribute significantly to refining our understanding of stress tolerance in the face of ongoing global climate change.

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In spite of extensive studies of cellular signaling, many fundamental processes such as pathway integration, cross-talk, and feedback remain poorly understood. To enable integrated and quantitative measurements of cellular biochemical activities, we have developed the Quantitative Cell Proteomics Atlas (QCPA). QCPA consists of panels of targeted mass spectrometry assays to determine the abundance and stoichiometry of regulatory post-translational modifications of sentinel proteins from most known physiologic and pathogenic signaling pathways in human cells. QCPA currently profiles 1 913 peptides from 469 effectors of cell surface signaling, apoptosis, stress response, gene expression, quiescence, and proliferation. For each protein, QCPA includes triplets of isotopically labeled peptides covering known post-translational regulatory sites to determine their stoichiometries and unmodified protein regions to measure total protein abundance. The QCPA framework incorporates analytes to control for technical variability of sample preparation and mass spectrometric analysis, including TrypQuant, a synthetic substrate for accurate quantification of proteolysis efficiency for proteins containing chemically modified residues. The ability to precisely and accurately quantify most known signaling pathways should enable improved chemoproteomic approaches for the comprehensive analysis of cell signaling and clinical proteomics of diagnostic specimens. QCPA is openly available at https://qcpa.mskcc.org.

The voltage-dependent anion channels (VDACs) are the most abundant proteins present on the outer mitochondrial membrane. They serve a myriad of functions ranging from energy and metabolite exchange to highly debatable roles in apoptosis. Their role in molecular transport puts them on the center stage as communicators between cytoplasmic and mitochondrial signaling events. Beyond their general role as interchangeable pores, members of this family may exhibit specific functions. Even after nearly five decades of their discovery, their role in plant systems is still a new and rapidly emerging field. The information on biochemical regulation of VDACs is limited. Various interacting proteins and post-translational modifications (PTMs) modulate VDAC functions, amongst these, phosphorylation is quite noticeable. In this review, we have tried to give a glimpse of the recent advancements in the biochemical/interactional regulation of plant VDACs. We also cover a critical analysis on the importance of PTMs in the functional regulation of VDACs. Besides, the review also encompasses numerous studies which can identify VDACs as a connecting link between Ca2+ and reactive oxygen species signaling in special reference to the plant systems.

The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization-the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.

Chemotherapy is one of the most effective methods of treating tumors in clinical study currently, but drug side effects usually are unbearable to the patient, which also makes it difficult to continue chemotherapy. Enhanced drug efficacy and reduced drug side effects are the main strategies for tumor therapy. Herein, based on biochemical regulation, theanine liposomes were designed to adjuvant doxorubicin (DOX) therapy, which can reduce the adverse reactions and enhance the effect of DOX. Stigmasterol was applied instead of traditional cholesterol for reducing the risk of cardiovascular disease. The as-prepared theanine liposomes by two methods had optimal sizes (154.8 and 169.0 nm), which can effectively accumulate in tumor tissues. In vitro experiments demonstrated that the theanine liposomes had a good effect of sustaining drug release. Cell uptake indicated that the presence of theanine can effectively inhibit glutathione (GSH) levels in cells and increase the uptake of DOX. In tumor bearing mice experiments, the combination of the theanine liposomes and DOX showed a better tumor inhibitory effect with a smaller tumor volume (2.7 fold) compared with that of the free DOX group. Meanwhile, under the mediation of theanine, the amount of doxorubicin was greatly reduced to achieve the same therapeutic effect, and the side effects of the drug were largely inhibited. Therefore, theanine liposomes have great application potential in tumor chemotherapy.

The amino acids arginine and ornithine are the precursors of a wide range of nitrogenous compounds in all living organisms. The metabolic conversion of ornithine into arginine is catalyzed by the sequential activities of the enzymes ornithine transcarbamylase (OTC), argininosuccinate synthetase (ASSY) and argininosuccinate lyase (ASL). Because of their roles in the urea cycle, these enzymes have been purified and extensively studied in a variety of animal models. However, the available information about their molecular characteristics, kinetic and regulatory properties is relatively limited in plants. In conifers, arginine plays a crucial role as a main constituent of N-rich storage proteins in seeds and serves as the main source of nitrogen for the germinating embryo. In this work, recombinant PpOTC, PpASSY and PpASL enzymes from maritime pine (Pinus pinaster Ait.) were produced in Escherichia coli to enable study of their molecular and kinetics properties. The results reported here provide a molecular basis for the regulation of arginine and ornithine metabolism at the enzymatic level, suggesting that the reaction catalyzed by OTC is a regulatory target in the homeostasis of ornithine pools that can be either used for the biosynthesis of arginine in plastids or other nitrogenous compounds in the cytosol.

Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their effective graph and dynamics canalizing map, as well as other tools to uncover minimum sets of control variables.

Diacylglycerol acyltransferase 1 (DGAT1) catalyzes the final and committed step in the Kennedy pathway for triacylglycerol (TAG) biosynthesis and, as such, elucidating its mode of regulation is critical to understand the fundamental aspects of carbon metabolism in oleaginous crops. In this study, purified Brassica napus diacylglycerol acyltransferase 1 (BnaDGAT1) in n-dodecyl-β-d-maltopyranoside micelles was lipidated to form mixed micelles and subjected to detailed biochemical analysis. The degree of mixed micelle fluidity appeared to influence acyltransferase activity. BnaDGAT1 exhibited a sigmoidal response and eventual substrate inhibition with respect to increasing concentrations of oleoyl-CoA. Phosphatidate (PA) was identified as a feed-forward activator of BnaDGAT1, enabling the final enzyme in the Kennedy pathway to adjust to the incoming flow of carbon leading to TAG. In the presence of PA, the oleoyl-CoA saturation plot became more hyperbolic and desensitized to substrate inhibition indicating that PA facilitates the transition of the enzyme into the more active state. PA may also relieve possible autoinhibition of BnaDGAT1 brought about by the N-terminal regulatory domain, which was shown to interact with PA. Indeed, PA is a key effector modulating lipid homeostasis, in addition to its well recognized role in lipid signaling. BnaDGAT1 was also shown to be a substrate of the sucrose non-fermenting-1-related kinase 1 (SnRK1), which catalyzed phosphorylation of the enzyme and converted it to a less active form. Thus, this known regulator of carbon metabolism directly influences TAG biosynthesis.