Molecular BioSystems最新文献

Voltage-dependent anion channels (VDACs) are a family of three mitochondrial porins and the most abundant integral membrane proteins of the mitochondrial outer membrane (MOM). VDACs are known to be involved in metabolite/ion transport across the MOM and in many cellular processes ranging from mitochondria-mediated apoptosis to the control of energy metabolism, by interacting with cytosolic, mitochondrial and cytoskeletal proteins and other membrane channels. Despite redundancy and compensatory mechanisms among VDAC isoforms, they display not only different channel properties and protein expression levels, but also distinct protein partners. Here, we review the known protein interactions for each VDAC isoform in order to shed light on their peculiar roles in physiological and pathological conditions. As proteins associated with the MOM, VDAC opening/closure as a metabolic checkpoint is regulated by protein–protein interactions, and is of pharmacological interest in pathological conditions such as cancer. The interactions involving VDAC1 have been characterized more in depth than those involving VDAC2 and VDAC3. Nevertheless, the so far explored VDAC–protein interactions for each isoform show that VDAC1 is mainly involved in the maintenance of cellular homeostasis and in pro-apoptotic processes, whereas VDAC2 displays an anti-apoptotic role. Despite there being limited information on VDAC3, this isoform could contribute to mitochondrial protein quality control and act as a marker of oxidative status. In pathological conditions, namely neurodegenerative and cardiovascular diseases, both VDAC1 and VDAC2 establish abnormal interactions aimed to counteract the mitochondrial dysfunction which contributes to end-organ damage.

In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by complex networks of chemical reactions involving genes, proteins, and enzymes. Accurate computational models are indispensable means for understanding the mechanisms behind the evolution of a complex system, not always explored with wet lab experiments. To serve their purpose, computational models, however, should be able to describe and simulate the complexity of a biological system in many of its aspects. Moreover, it should be implemented by efficient algorithms requiring the shortest possible execution time, to avoid enlarging excessively the time elapsing between data analysis and any subsequent experiment. Besides the features of their topological structure, the complexity of biological networks also refers to their dynamics, that is often non-linear and stiff. The stiffness is due to the presence of molecular species whose abundance fluctuates by many orders of magnitude. A fully stochastic simulation of a stiff system is computationally time-expensive. On the other hand, continuous models are less costly, but they fail to capture the stochastic behaviour of small populations of molecular species. We introduce a new efficient hybrid stochastic–deterministic computational model and the software tool MoBioS (MOlecular Biology Simulator) implementing it. The mathematical model of MoBioS uses continuous differential equations to describe the deterministic reactions and a Gillespie-like algorithm to describe the stochastic ones. Unlike the majority of current hybrid methods, the MoBioS algorithm divides the reactions' set into fast reactions, moderate reactions, and slow reactions and implements a hysteresis switching between the stochastic model and the deterministic model. Fast reactions are approximated as continuous-deterministic processes and modelled by deterministic rate equations. Moderate reactions are those whose reaction waiting time is greater than the fast reaction waiting time but smaller than the slow reaction waiting time. A moderate reaction is approximated as a stochastic (deterministic) process if it was classified as a stochastic (deterministic) process at the time at which it crosses the threshold of low (high) waiting time. A Gillespie First Reaction Method is implemented to select and execute the slow reactions. The performances of MoBios were tested on a typical example of hybrid dynamics: that is the DNA transcription regulation. The simulated dynamic profile of the reagents’ abundance and the estimate of the error introduced by the fully deterministic approach were used to evaluate the consistency of the computational model and that of the software tool.

Long non-coding RNAs (lncRNAs), non-protein-coding RNAs that are more than 200 nucleotides in length, have been demonstrated to play a vital role in the pathophysiology of human diseases, particularly in tumorigenesis and progression of cancers. Dysregulation of lncRNAs, which serve as either oncogenes or tumor suppressor genes, is involved in diverse cellular processes, such as proliferation, dedifferentiation, migration, invasion and anti-apoptosis. Promoter of CDKN1A antisense DNA damage-activated RNA (PANDAR), which was recently found to manifest aberrant expression in various malignancies including non-small cell lung cancer, hepatocellular carcinoma, colorectal cancer and gastric cancer, is a novel cancer-related lncRNA. Deregulation of PANDAR contributes to tumorigenesis and progression of cancers, suggesting that PANDAR is likely to represent a viable biomarker and therapeutic target for human cancers. In this review, we summarize current evidence regarding the biological functions and mechanisms of PANDAR during tumor development.

Henipaviruses are severe human pathogens within the Paramyxoviridae family. Beyond the P protein, the Henipavirus P gene also encodes the V protein which shares with P its N-terminal, intrinsically disordered region (PNT) and possesses a unique C-terminal domain predicted to be folded and to bind zinc (ZnFD). Henipavirus V proteins antagonize IFN signaling through PNT-mediated binding to STAT1, and several paramyxoviral V proteins promote STAT1 degradation through binding to DDB1. Structural and molecular information on Henipavirus V proteins is lacking, and their ability to interact with DDB1 has not been documented yet. We cloned the V genes from Nipah and Hendra viruses and purified the V proteins from E. coli and DDB1 from insect cells. Using analytical size-exclusion chromatography, CD and SAXS we characterized the V proteins and their domains. Using pull-down and MST we assessed their binding abilities towards DDB1. We show that PNT remains disordered also in the context of the V protein, while the ZnFD adopts a predominant β conformation. We also show that the V proteins interact with DDB1 predominantly via their ZnFD. This is the first experimental characterization of the Henipavirus V proteins and the first experimental evidence of their interaction with DDB1. The DDB1–ZnFD interaction constitutes a promising target for antiviral strategies. These studies provide a conceptual asset to design new antiviral strategies expected to reduce or abrogate the ability of these viruses to escape the innate immune response. They also contribute to illuminating the conformational behaviour of proteins encompassing large intrinsically disordered domains.

Altered metabolism is often identified as a cause or an effect of physiology and pathogenesis. But it is difficult to predict the metabolic flux distributions of multicellular organisms due to the lack of an explicit metabolic objective function. Here we present a computational method which can successfully describe the differences in metabolism between two different conditions on a large scale. By integrating gene expression data with an existing comprehensive reconstruction of the global human metabolic network, we qualitatively predicted significantly differential fluxes without prior knowledge or the rate of metabolite uptake and secretion. Therefore, this method can be applied for both microorganisms and multicellular organisms. Different from traditional enrichment analysis methods and constraint-based models, we consider conditions and interactions within the metabolic network simultaneously. To apply the proposed method, we predicted altered fluxes for E. coli strains and clear cell renal cell carcinoma, while the E. coli strains are growing aerobically in a chemostat with different dilution rates and clear cell renal cell carcinoma is compared with normal kidney cells. Then we map the significantly differential reactions to metabolic subsystems defined in the original metabolic network for ccRCC to observe the altered metabolism. In contrast with existing studies, our results show a high accuracy of the E. coli experiment and a more reasonable prediction of the ccRCC experiment. The method presented here provides a computational approach for the genome-wide study of altered metabolism under pairs of conditions for both microorganisms and multicellular organisms.

Mass spectrometry (MS) has become an increasingly important technique to analyze proteins. In popular bottom-up MS-based proteomics, reduction and alkylation are routine steps to facilitate peptide identification. However, incomplete reactions and side reactions may occur, which compromise the experimental results. In this work, we systematically evaluated the reduction step with commonly used reagents, i.e., dithiothreitol, 2-mercaptoethanol, tris(2-carboxyethyl)phosphine, or tris(3-hydroxypropyl)phosphine, and alkylation with iodoacetamide, acrylamide, N-ethylmaleimide, or 4-vinylpyridine. By using digested peptides from a yeast whole-cell lysate, the number of proteins and peptides identified were very similar using four different reducing reagents. The results from four alkylating reagents, however, were dramatically different with iodoacetamide giving the highest number of peptides with alkylated cysteine and the lowest number of peptides with incomplete cysteine alkylation and side reactions. Alkylation conditions with iodoacetamide were further optimized. To identify more peptides with cysteine, thiopropyl-sepharose 6B resins were used to enrich them, and the optimal conditions were employed for the reduction and alkylation. The enrichment resulted in over three times more cysteine-containing peptides than without enrichment. Systematic evaluation of the reduction and alkylation with different reagents can aid in a better design of bottom-up proteomic experiments.

Particular lipid profiles have been found in two different protozoa of the Leishmania genus. Leishmania infantum, a visceral leishmaniasis causative agent and Leishmania amazonensis, a cutaneous leishmaniasis, reveal distinctive lipid contents of phosphatidylethanolamine and phosphatidylserine plasmalogens, sphingolipids, phosphatidylinositols, phosphatidylcholine, and phosphatidylethanolamine, which have been shown to be related to species, life-cycle of the parasite, and macrophage infection. L. infantum displayed a higher content of phosphatidylethanolamine plasmalogens than L. amazonensis, which may help to differentiate their unique clinical manifestations. Phosphatidylserines plasmalogens are also found to be an important lipid class for the intracellular form of the parasite. Our findings also reveal lipid classes that may be involved in visceralization pathways and parasite differentiation.

Solvent properties of water in aqueous solutions of polyethylene glycols of various molecular weights, L-proline, betaine, and a series of chlorides of varied concentrations are assayed using three solvatochromic dyes. The properties include solvent dipolarity/polarizability, hydrogen bond donor acidity, and hydrogen bond acceptor basicity. These properties are also evaluated in mixtures of two polymers, polymer and osmolyte, and two osmolytes. It is shown that linear combinations of solvent dipolarity/polarizability and hydrogen bond donor acidity assayed in individual solutions of crowders strongly correlate with the effects of the crowders on the stability of various proteins and nucleic acids reported in the literature. The solvent properties of water in aqueous mixtures of two macromolecular crowders, two osmolytes, or mixtures of an osmolyte and a macromolecular crowder vary differently for various solvent properties. The overall effects of the two components in the mixture on a given solvent property of water may be additive, reduced or enhanced depending on the particular composition of the mixture. It is hypothesized that changes in the solvent properties of water are related to changes in the water hydrogen-bonding structuring. It is suggested that the observed crowder-induced changes in the solvent properties of water should be taken into account in theoretical considerations of crowding effects in biological systems.

Chronic human immunodeficiency virus (HIV) infection, separately and in combination with highly active antiretroviral therapy (HAART) is closely associated with oxidative stress (OS). Most studies demonstrating redox imbalances in HIV-infected individuals have done so using conventional biochemical methodologies. The limited simultaneous detection of multiple OS markers within one sample is a major drawback of these methodologies and can be addressed through the use of metabonomics. HIV-metabonomic studies utilizing biofluids from HAART cohorts as the investigative source, are on the increase. Data from many of these studies identified metabolic markers indicative of HIV-induced OS, usually as an outcome of an untargeted metabonomics study. Untargeted studies cast a wide net for any and all detectable metabolites in complex mixtures. Given the prevalence of OS during HIV infection and antiviral treatment, it is perhaps not surprising that indicators of this malady would become evident during metabolite identification. At times, targeted studies for specific (non-OS) metabolites would also yield OS markers as an outcome. This review examines the findings of these studies by first providing the necessary background information on OS and the main ways in which free radicals/reactive oxygen species (ROS) produced during OS, cause biomolecular damage. This is followed by information on the biomarkers which come about as a result of free radical damage and the techniques used for assaying these stress indicators. The established links between elevated ROS and lowered antioxidants during HIV infection and the subsequent use of HAART is then presented followed by a review of the OS markers detected in HIV metabonomic studies to date. We identify gaps in HIV/HAART-associated OS research and finally suggest how these research gaps can be addressed through metabonomic analysis, specifically targeting the multiple markers of HIV-induced OS.

Extracellular microRNAs (miRNAs) carried by exosomes can play a key role in cell-to-cell communication. Deregulation of miRNA expression and exosome secretion have been related to pathological conditions such as cancer. While it is known that circulating miRNAs can alter gene expression in recipient cells, it remains unclear how significant the dynamical impact of these extracellular miRNAs is. To shed light on this issue, we propose a model for the spatio-temporal evolution of the protein expression in a cell tissue altered by abnormal miRNA expression in a donor cell. This results in a nonhomogeneous cellular response in the tissue, which we quantify by studying the range of action of the donor cell on the surrounding cells. Key model parameters that control the range of action are identified. Based on a model for a heterogeneous cell population, we show that the dynamics of gene expression in the tissue is robust to random changes of the parameter values. Furthermore, we study the propagation of gene expression oscillations in a tissue induced by extracellular miRNAs. In the donor cell, the miRNA inhibits its own transcription which can give rise to local oscillations in gene expression. The resulting oscillations of the concentration of extracellular miRNA induce oscillations of the protein concentration in recipient cells. We analyse the nonmonotonic spatial evolution of the oscillation amplitude of the protein concentration in the tissue which may have implications for the propagation of oscillations in biological rhythms such as the circadian clock.