Myc and cell cycle control Bretones, Gabriel; Delgado, M. Dolores; León, Javier
Biochimica et biophysica acta,
20/May , Letnik:
1849, Številka:
5
Journal Article
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Soon after the discovery of the Myc gene (c-Myc), it became clear that Myc expression levels tightly correlate to cell proliferation. The entry in cell cycle of quiescent cells upon Myc enforced ...expression has been described in many models. Also, the downregulation or inactivation of Myc results in the impairment of cell cycle progression. Given the frequent deregulation of Myc oncogene in human cancer it is important to dissect out the mechanisms underlying the role of Myc on cell cycle control. Several parallel mechanisms account for Myc-mediated stimulation of the cell cycle. First, most of the critical positive cell cycle regulators are encoded by genes induced by Myc. These Myc target genes include Cdks, cyclins and E2F transcription factors. Apart from its direct effects on the transcription, Myc is able to hyperactivate cyclin/Cdk complexes through the induction of Cdk activating kinase (CAK) and Cdc25 phosphatases. Moreover, Myc antagonizes the activity of cell cycle inhibitors as p21 and p27 through different mechanisms. Thus, Myc is able to block p21 transcription or to induce Skp2, a protein involved in p27 degradation. Finally, Myc induces DNA replication by binding to replication origins and by upregulating genes encoding proteins required for replication initiation. Myc also regulates genes involved in the mitotic control. A promising approach to treat tumors with deregulated Myc is the synthetic lethality based on the inhibition of Cdks. Thus, the knowledge of the Myc-dependent cell cycle regulatory mechanisms will help to discover new therapeutic approaches directed against malignancies with deregulated Myc. This article is part of a Special Issue entitled: Myc proteins in cell biology and pathology.
•Myc can stimulate cell cycle progression through several parallel mechanisms.•Many of the pivotal positive cell cycle regulators are encoded by Myc target genes.•Myc induces the expression of some cyclins, Cdks and proteins involved in replication.•Myc counteracts the activity of p21 and p27 cell cycle inhibitors.
The construction of a quantum computer remains a fundamental scientific and technological challenge because of the influence of unavoidable noise. Quantum states and operations can be protected from ...errors through the use of protocols for quantum computing with faulty components. We present a quantum error-correcting code in which one qubit is encoded in entangled states distributed over seven trapped-ion qubits. The code can detect one bit flip error, one phase flip error, or a combined error of both, regardless on which of the qubits they occur. We applied sequences of gate operations on the encoded qubit to explore its computational capabilities. This seven-qubit code represents a fully functional instance of a topologically encoded qubit, or color code, and opens a route toward fault-tolerant quantum computing.
Regression is a very relevant problem in machine learning, with many different available approaches. The current work presents a comparison of a large collection composed by 77 popular regression ...models which belong to 19 families: linear and generalized linear models, generalized additive models, least squares, projection methods, LASSO and ridge regression, Bayesian models, Gaussian processes, quantile regression, nearest neighbors, regression trees and rules, random forests, bagging and boosting, neural networks, deep learning and support vector regression. These methods are evaluated using all the regression datasets of the UCI machine learning repository (83 datasets), with some exceptions due to technical reasons. The experimental work identifies several outstanding regression models: the M5 rule-based model with corrections based on nearest neighbors (cubist), the gradient boosted machine (gbm), the boosting ensemble of regression trees (bstTree) and the M5 regression tree. Cubist achieves the best squared correlation ( R2) in 15.7% of datasets being very near to it, with difference below 0.2 for 89.1% of datasets, and the median of these differences over the dataset collection is very low (0.0192), compared e.g. to the classical linear regression (0.150). However, cubist is slow and fails in several large datasets, while other similar regression models as M5 never fail and its difference to the best R2 is below 0.2 for 92.8% of datasets. Other well-performing regression models are the committee of neural networks (avNNet), extremely randomized regression trees (extraTrees, which achieves the best R2 in 33.7% of datasets), random forest (rf) and ε-support vector regression (svr), but they are slower and fail in several datasets. The fastest regression model is least angle regression lars, which is 70 and 2,115 times faster than M5 and cubist, respectively. The model which requires least memory is non-negative least squares (nnls), about 2 GB, similarly to cubist, while M5 requires about 8 GB. For 97.6% of datasets there is a regression model among the 10 bests which is very near (difference below 0.1) to the best R2, which increases to 100% allowing differences of 0.2. Therefore, provided that our dataset and model collection are representative enough, the main conclusion of this study is that, for a new regression problem, some model in our top-10 should achieve R2 near to the best attainable for that problem.
We construct a topological invariant that classifies density matrices of symmetry-protected topological orders in two-dimensional fermionic systems. As it is constructed out of the previously ...introduced Uhlmann phase, we refer to it as the topological Uhlmann number n_{U}. With it, we study thermal topological phases in several two-dimensional models of topological insulators and superconductors, computing phase diagrams where the temperature T is on an equal footing with the coupling constants in the Hamiltonian. Moreover, we find novel thermal-topological transitions between two nontrivial phases in a model with high Chern numbers. At small temperatures we recover the standard topological phases as the Uhlmann number approaches to the Chern number.
Laccase-based biosensors for detection of phenolic compounds Rodríguez-Delgado, Melissa M.; Alemán-Nava, Gibrán S.; Rodríguez-Delgado, José Manuel ...
TrAC, Trends in analytical chemistry (Regular ed.),
December 2015, 2015-12-00, 2015-12, Letnik:
74
Journal Article
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•Transduction principles, laccase sources and immobilization method.•Laccase from Trametes, Aspergillus and Ganoderma genres employed in biosensing.•Amperometry is the transduction principle most ...used in laccase-biosensor design.•Lifetime and stability up to 10 months were achieved by electrochemical devices.•Optical transduction principle shows the best limit of detection at trace levels.
Monitoring of phenolic compounds in the food industry and for environmental and medical applications has become more relevant in recent years. Conventional methods for detection and quantification of these compounds, such as spectrophotometry and chromatography, are time consuming and expensive. However, laccase biosensors represent a fast method for on-line and in situ monitoring of these compounds. We discuss the main transduction principles. We divide the electrochemical principle into amperometric, voltammetric, potentiometric and conductometric sensors. We divide optical transducers into fluorescence and absorption. The amperometric transducer method is the most widely studied and used for laccase biosensors. Optical biosensors present higher sensitivity than the other biosensors. Laccase production is dominated by a few fungus genera: Trametes, Aspergillus, and Ganoderma. We present an overview of laccase biosensors used for the determination of phenolic compounds in industrial applications.
Ultralong organic phosphorescence holds great promise as an important approach for optical materials and devices. Most of phosphorescent organic molecules with long lifetimes are substituted with ...heavy atoms or carbonyl groups to enhance the intersystem crossing (ISC), which requires complicated design and synthesis. Here, we report a cyclization-promoted phosphorescence phenomenon by boosting ISC. N-butyl carbazole exhibits a phosphorescence lifetime (τp) of only 1.45 ms and a low phosphorescence efficiency in the solution state at 77 K due to the lack of efficient ISC. In order to promote its phosphorescence behavior, we explored the influence of conjugation. By linear conjugation of four carbazole units, possible ISC channels are increased so that a longer τp of 2.24 s is observed. Moreover, by cyclization, the energy gap between the singlet and triplet states is dramatically decreased to 0.04 eV for excellent ISC efficiency accompanied by increased rigidification to synergistically suppress the nonradiative decay, resulting in satisfactory phosphorescence efficiency and a prolonged τp to 3.41 s in the absence of any heavy atom or carbonyl group, which may act as a strategy to prepare ultralong phosphorescent organic materials by enhancing the ISC and rigidification.
We introduce the Uhlmann geometric phase as a tool to characterize symmetry-protected topological phases in one-dimensional fermion systems, such as topological insulators and superconductors. Since ...this phase is formulated for general mixed quantum states, it provides a way to extend topological properties to finite temperature situations. We illustrate these ideas with some paradigmatic models and find that there exists a critical temperature Tc at which the Uhlmann phase goes discontinuously and abruptly to zero. This stands as a borderline between two different topological phases as a function of the temperature. Furthermore, at small temperatures we recover the usual notion of topological phase in fermion systems.
Collagen is the oldest and most abundant extracellular matrix protein that has found many applications in food, cosmetic, pharmaceutical, and biomedical industries. First, an overview of the family ...of collagens and their respective structures, conformation, and biosynthesis is provided. The advances and shortfalls of various collagen preparations (e.g., mammalian/marine extracted collagen, cell‐produced collagens, recombinant collagens, and collagen‐like peptides) and crosslinking technologies (e.g., chemical, physical, and biological) are then critically discussed. Subsequently, an array of structural, thermal, mechanical, biochemical, and biological assays is examined, which are developed to analyze and characterize collagenous structures. Lastly, a comprehensive review is provided on how advances in engineering, chemistry, and biology have enabled the development of bioactive, 3D structures (e.g., tissue grafts, biomaterials, cell‐assembled tissue equivalents) that closely imitate native supramolecular assemblies and have the capacity to deliver in a localized and sustained manner viable cell populations and/or bioactive/therapeutic molecules. Clearly, collagens have a long history in both evolution and biotechnology and continue to offer both challenges and exciting opportunities in regenerative medicine as nature's biomaterial of choice.
Collagen is the most abundant extracellular matrix protein and is used extensively in food, cosmetic, pharmaceutical, and biomedical industries. The fundamentals of collagen biosynthesis, assembly, and native crosslinking are provided, along with methods to produce natural and synthetic collagens and to fabricate, stabilize, and characterize collagen‐based devices.
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