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MOF-based Electrochemical Sensors for Protein Detection
Published in Ram K. Gupta, Tahir Rasheed, Tuan Anh Nguyen, Muhammad Bilal, Metal-Organic Frameworks-Based Hybrid Materials for Environmental Sensing and Monitoring, 2022
Yang Liu, Juanhua Zhou, Shiyu Zhang, Hongye Wang
Protein kinase is a kind of enzyme that catalyzes the process of protein phosphorylation. Its activity is closely related to various diseases. Therefore, the detection of protein kinase activity in clinical diagnosis can help diagnosis. Song et al. proposed a low fouling and highly sensitive electrochemical sensor for T4 polynucleotide kinase (PNK) detection based on zwitterionic peptide and self-sacrificial Fe-MOF [67]. The zwitterionic peptide could be assembled into antifouling layers and the Fe-MOF formed Prussian blue (PB) after reacting with K4Fe(CN)6. Thus, the biosensor prevented the adsorption of nonspecific proteins and had a high sensitivity. Another kinase, protein tyrosine kinase-7 (PTK7) could also be detected by an electrochemical biosensor based on Zn-MOF-on-Zr-MOF architecture [33]. In addition to electrochemical biosensors, photoelectrochemical biosensors attracted lots of interest as well. Our team developed a highly sensitive photoelectrochemical biosensor for the detection of protein kinase A (PKA) by Ru(bpy)32+ loaded UiO-66 (Ru(bpy)32+@UiO-66) [44]. UiO-66 not only increased the load of Ru(bpy)32+ but also provided a large number of phosphorylated kemptide binding sites. Therefore, the biosensor achieved high sensitivity and fast response.
Explainable Artificial Intelligence: Guardian for Cancer Care
Published in Mohamed Lahby, Utku Kose, Akash Kumar Bhoi, Explainable Artificial Intelligence for Smart Cities, 2021
On the genetic/molecular level, the progressive accumulation of mutations is the major source of cancer initiation. But still, all the mutations do not equally contribute to cancer, as only a few of them are rare and disease drivers. The human genome contains around 518 protein kinase genes that are collectively known as kinome. Multiple classifier systems were used to segregate the mutations in positive and negative classes. Positive and negative mutations were retrieved from the ‘Catalogue of Somatic Mutations in Cancer’ (COSMIC) database and SNP@Domain database. Also, protein characteristics concerning point mutations were determined from various sources, including KinBas, Uniprot, EMBOSS, etc. Eleven machine learning methods, including J48 (Tree), Random Forest, NB Tree, Functional Tree, Decision Table, DTNB, LWL (J48+KNN), Bayes Net, Naive Bayes, SVM, and Neural Network, were applied for the development of classifier system to identify EGFR mutations. For the first time, the connection between EGFR mutations T725M and L861R was established with cancers (U et al., 2014).
Biochemical pathways
Published in Christian Mazza, Michel Benaïm, Stochastic Dynamics for Systems Biology, 2016
Christian Mazza, Michel Benaïm
Cells receive information from their environment, and, in turn, respond in a way that is coded by their genes and epigenetic factors. Cells are able to respond to many chemical and physical agents which can induce transitory or permanent changes in cells. These signalling molecules bind to protein receptors, which interact with special proteins in the cytoplasmic or plasma membrane. The latter then transduce (or send) the signal to deeper levels within the cell. Protein kinases and protein phosphatases mediate a significant part of the signal transduction in eukaryotic cells. The human kinome contains more than 500 types of protein kinase (see, e.g., [118]) and approximatively 150 types of protein phosphatase. These pathways are designed to elicit a cellular response like the activation of transcription factors in response to external signals, and thus increase the expression of some target genes, producing in this way mRNA flux; see, e.g., [120] for a nice exposition of cellular processing, from the point of view of systems biology. The network structures associated with these protein or signalling networks are similar to the topologies observed in metabolic networks, and contain positive and negative feedback loops; see, e.g., [135]. Cellular processing units are designed and work like electronic circuits or neural networks; see, e.g., chapter 6 of [4], where such pathways are studied using tools from neural network theory. A basic difference is that the living processing units involved can move and diffuse inside the cell. It is difficult to give a general overview of the various existing topologies. The reader can consult, among others, [52], [191], [13], [177], [116], [53] and [151] to get ideas on known network structures.
Combined treatment with auranofin and trametinib induces synergistic apoptosis in breast cancer cells
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Min-Kyung Joo, Sangyun Shin, Dong-Jin Ye, Hong-Gyu An, Tae-Uk Kwon, Hyoung-Seok Baek, Yeo-Jung Kwon, Young-Jin Chun
Mitogen-activated protein kinases (MAPKs) are types of protein kinases involved in cell signal transduction (Choi and Sung 2019; Mirzoeva et al. 2009). MAPK signaling pathway is known to play an important role in tumor proliferation and metastasis by regulating the expression of various genes, especially in breast cancer (Ahmad et al. 2016; Mirzoeva et al. 2009). Several investigators reported that therapeutic strategies involving hormone blockade, including estrogen deprivation and tamoxifen, are known to induce drug resistance through activation of the MAPK-ERK pathway (Coutts and Murphy 1998; Shim et al. 2000; Thrane et al. 2013; Yue et al. 2002). Based upon these findings, MAPK-ERK signaling was considered a potential therapeutic target for hormone receptor-positive breast cancer. However, clinical studies investigating MEK inhibitors failed to demonstrate a marked anticancer effect (Adjei et al. 2008; Rinehart et al. 2004). To overcome these obstacles, combinational therapeutic strategies for breast cancer using selective MEK inhibitors such as trametinib were evaluated (Leung et al. 2014; Mirzoeva et al. 2009).
Antimicrobial and antileukemic effects: in vitro activity of Calyptranthes grandifolia aqueous leaf extract
Published in Journal of Toxicology and Environmental Health, Part A, 2020
Fernanda Majolo, Shanna Bitencourt, Bruna Wissmann Monteiro, Gabriela Viegas Haute, Celso Alves, Joana Silva, Susete Pinteus, Roberto Christ Vianna Santos, Heron Fernandes Vieira Torquato, Edgar Julian Paredes-Gamero, Jarbas Rodrigues Oliveira, Claucia Fernanda Volken De Souza, Rui Felipe Pinto Pedrosa, Stefan Laufer, Márcia Inês Goettert
Since modulation of the immune system has been an emerging concept in the control of tumor cell proliferation, targeting protein kinases may be a useful strategy to generate antitumor drugs (Kauffmann et al. 2016; Limberger et al. 2002). In order to investigate the specificity of C. grandifolia as a kinase inhibitor, the aqueous extract was tested for its ability to inhibit JAK3 and p38α in vitro. The inhibitory potency (IC50) of the extract was assessed by a direct ELISA assay. Figure 4 demonstrates C. grandifolia extract markedly inhibited JAK3 and p38α activity with an IC50 value in low concentration (JAK3 = 20.09 ng/ml; p38α = 5,9 µg/ml). CP-690550 (Tofacitinib), a commercial pan-JAK inhibitor, and SB203580, a commercial p38-inhibitor, were used as positive controls, presenting, respectively, the following values IC50 0.57 ng/ml and not detectable (0 µg/ml).
Molecular mechanisms underlying titanium dioxide nanoparticles (TiO2NP) induced autophagy in mesenchymal stem cells (MSC)
Published in Journal of Toxicology and Environmental Health, Part A, 2019
Shunbang Yu, Yongping Mu, Xudong Zhang, Jian Li, Charles Lee, He Wang
The family of mitogen-activated protein kinases (MAPKs) is composed of three major groups: the extracellular-regulated kinases (ERKs), the C-Jun N-terminal Kinases (JNKs) and the p38 MAPKs (Oh and Lim 2009; Raman, Chen, and Cobb 2007). It has been reported that activation of MAPK is involved in TiO2NP-triggered apoptosis (Dhupal et al. 2018). Kang et al. (2009) showed that TiO2NP initiated apoptosis in lymphocytes via activation of MAPK. Activation of MAPK pathway by ROS was also found to protect cells from death (Wang et al. 1998). Further, MAPK was suggested to play a regulatory role in autophagy (Chuang, Wang, and Yang 2000; CheCheng et al. 2008). In our experiment, MAPK pathway was activated as evidenced by TiO2NP-initiated elevation in protein expression levels of p38, JNK, and ERK. In contrast, TiO2NP in the presence inhibitors of p38, JNK, and ERK, the nanoparticle stimulated rise in protein expression in these pathways was blocked indicating that MAPK pathway played a role in the observed oxidant and autophagic responses in MSCs of TiO2NP.