Variation for mean size of native and heated casein micelles and for heat-induced change in size.
\r\n\t
",isbn:"978-1-83969-545-2",printIsbn:"978-1-83969-544-5",pdfIsbn:"978-1-83969-546-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"c77f99db5569e8d0325b856cb7d75b17",bookSignature:"Prof. Maged Marghany",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10854.jpg",keywords:"Optical, Radar, Algorithm, Programming, Big Data, Deep Learning, Image Processing, Time Series Data Analysis, Large Scale Methods, Signal Processing, Computer Vision, Remote Sensing",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 18th 2021",dateEndSecondStepPublish:"March 18th 2021",dateEndThirdStepPublish:"May 17th 2021",dateEndFourthStepPublish:"August 5th 2021",dateEndFifthStepPublish:"October 4th 2021",remainingDaysToSecondStep:"14 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:'Prof. Marghany was recently ranked among the top two percent scientists in a global list compiled by the prestigious Stanford University. A pioneering scientist in microwave remote sensing invented a new theory Quantum Nonlinear Ocean Dynamics " Quantized Marghany\'s Front".',coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"96666",title:"Prof.",name:"Maged",middleName:null,surname:"Marghany",slug:"maged-marghany",fullName:"Maged Marghany",profilePictureURL:"https://mts.intechopen.com/storage/users/96666/images/system/96666.png",biography:"Prof.Dr. Maged Marghany, recently, ranked among the top two percent scientists in a global list compiled by the prestigious Stanford University. Prof.Dr. Maged Marghany is currently a Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala Darussalam, Banda Aceh, Indonesia. He is the author of 5 titles including Advanced Remote Sensing Technology for Tsunami Modelling and Forecasting which is published by Routledge Taylor and Francis Group, CRC and Synthetic Aperture Radar Imaging Mechanism for Oil Spills, which is published by Elsevier, His research specializes in microwave remote sensing and remote sensing for mineralogy detection and mapping. Previously, he worked as a Deputy Director in Research and Development at the Institute of Geospatial Science and Technology and the Department of Remote Sensing, both at Universiti Teknologi Malaysia. Maged has earned many degrees including a post-doctoral in radar remote sensing from the International Institute for Aerospace Survey and Earth Sciences, a Ph.D. in environmental remote sensing from the Universiti Putra Malaysia, a Master of Science in physical oceanography from the University Pertanian Malaysia, general and special diploma of Education and a Bachelor of Science in physical oceanography from the University of Alexandria in Egypt. Maged has published well over 250 papers in international conferences and journals and is active in International Geoinformatics, and the International Society for Photogrammetry and Remote Sensing (ISPRS).",institutionString:"Syiah Kuala University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"9",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Syiah Kuala University",institutionURL:null,country:{name:"Indonesia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"347259",firstName:"Karmen",lastName:"Daleta",middleName:null,title:"Dr.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"karmen@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"5104",title:"Environmental 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Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50314",title:"Measurement of Casein Micelle Size in Raw Dairy Cattle Milk by Dynamic Light Scattering",doi:"10.5772/62779",slug:"measurement-of-casein-micelle-size-in-raw-dairy-cattle-milk-by-dynamic-light-scattering",body:'\nIt is well known that the casein fraction of bovine milk exists as polydisperse, large, roughly spherical colloidal particles, 50–600 nm in diameter (average ~150 nm), called “casein micelles” [1]. The size, form, and structure of the casein micelle are of great importance for the milk industry especially for cheese making, yellow cheese, etc. [2]. Casein micelle contains an average of 3.4 g H2O per gram dry matter, which consists approximately of 93% protein and about 7% of inorganic component (Ca2+), formed phosphoprotein complexes. The casein micelle in milk consists of four caseins: αs1- (CSN1S1), αs2- (CSNS2), β- (CSN2), and k-casein (CSN3). The latter is important for the stability and properties of the casein micelle. Although k-casein is in a relatively low amount of the casein system (12–15% of whole casein), it is soluble in the presence of Ca2+, whereas the remaining 85% of casein are precipitated by Ca2+. CSN3 stabilizes up to 10 times its weight of the Ca2+-sensitive caseins via the formation of micelles [3] (Figure 1).
\nElectron micrograph of an individual casein micell [4].
One of the most important issues concerning the casein micelle is how the casein particles (micelles) in milk are stabilized and destabilized by the action of various agents and conditions. Thus, various hypotheses of casein micelle structure are proposed.
\nThe first model of casein micelle structure was proposed by Waugh and Nobel in 1965 [5]. This model is based on the casein solubility in Ca2+ solutions. The model describes the formation of low-weight-ratio complexes of αs1- and k-caseins in the absence of calcium. The monomers of αs1- or β-caseins interact with a charged phosphate loop (Figure 2), then begin to aggregate to a limiting size (the caseinate core) while the calcium ions are added. Precipitation of the caseinate is stopped after a monolayer of the low-weight αs1-k-casein complexes is formed. This coat complex has the k-casein monomers spread out completely on the surface [8].
\nStructure of casein micelle proposed by Waugh [6]. (A) Monomer model for αs1- or β-caseins with charged loop. (B) A tetramer of αs1-casein monomers. (C) Planar model for a core polymer of αs1- and β-caseins [7].
The first sub-micelle model was proposed by Morr in 1967 [9]. Morr considered that αs1-, β-, and k-casein monomers form small uniform submicelles. The submicelles, estimated by sedimentation velocity studies, are stabilized by hydrophobic bonding and calcium caseinate bridges, and the submicelles are aggregated and held together by colloidal calcium phosphate linkages with a micelle structure covered by αs1- and k-casein [7, 10].
\nThe model, described by Slattery and Evard [11] and Slattery [12], falls in the last category. In this model, the caseins first aggregate via hydrophobic interaction into subunits of 15–20 molecules each. The pattern of interaction is such that it brings about a variation in the k-casein content of these submicelles. The k-casein congregate on the micelle surface, those submicelles poor (like αs and β-casein) or totally deficient in k-casein are located in the interior of the micelle (Figure 2). This model does not explain what provokes the segregation of the k-casein or why k-casein molecules, having preferred to associate with their own kind to form aggregate patches in the k-rich submicelles, should then associate with the other caseins to complete the building of the submicelle. Another criticism of this model is the late entry of calcium phosphate into the assembly process by Slattery and Evard [11]. Separating the caseins from calcium and phosphate until this point in the assembly process is not really possible, since both calcium and phosphate are involved in the phosphorylation of the protein chain which occurs post-translation [13] immediately and presumably before the association of the chains into submicelles.
\nRepresentation of the casein micelle dual binding model proposed by Horne [16].
The last category of models is based on the properties of the isolated casein constituents, causing or directing the formation of the internal structure of the casein micelle [7].
\nThe first internal structure model was suggested by Rose in 1969 [14]. He assumed that β-casein monomers begin to self-associate into chain-like polymers. Subsequently, αs1-caseins molecules are attached to the β-casein polymers, while k-caseins interact with αs1-caseins, forming aggregates of limited size. Upon forming the micelle structure, colloidal calcium phosphate acts as a stabilizing agent and cross-links the network. In addition, these micelle networks are oriented in such a way that β-casein is directed internal, while the k-casein is directed external [8].
\nAn alternative internal structural model is proposed by Holt [15] with the help of transmission electron micrographs. This model emphasized the role of hydrophobic interactions in giving rise to submicelles, the Holt model relies solely on the interactions between the caseins and calcium phosphate to hold the micelle together. In this model, the calcium phosphate is in the form of nanoclusters and the interaction sites on the caseins are the phosphoseryl clusters of the calcium-sensitive caseins (Figure 3). Because aS1- and aS2-caseins have more than two such clusters, arguably in the case of aS1-casein, they are able to cross-link the nanoclusters into extended 3-dimensional network structures. The dual-binding in Holt models is in the size of the nanocluster and in the number of phosphate clusters (or casein molecules) that the surface of the nanocluster can accommodate (Figure 4).
\nSchematic representation of Holt’s model. The calcium phosphate formed nanocluster as interact with phosphoseryl residue of caseins [16].
Dynamic light scattering (DLS), sometimes referred to as quasi-elastic light scattering (QELS) or as photon correlation spectroscopy (PCS), is a non-invasive, well-established technique for measuring the size and size distribution of molecules and particles typically in the submicron region, and with the latest technology lower than 1 nm.
\nA laser or any other monochromaric light source is shot through a polarizer and into a sample. The scattered light is then sent through a second polarizer where it is collected by a photomultiplier. The resulting image is then projected onto a screen and this phenomenon is known as speckle pattern (Figure 5) [17].
\nPrinciple of dynamic light scattering. www.brookhaveninstruments.com.
All of the molecules in the solution are being hit with the light and all of the molecules diffract the light in all directions [18]. The diffracted light from all of the molecules can either interfere constructively (shown as light regions) or destructively (shown as dark regions). The process is repeated at short time intervals. The resulting set of speckle patterns is then analyzed by an autocorrelator in order to compare the intensity of light at each spot over time. The polarizers that are being used can be set up in two geometrical configurations. One of the polarizers is set in a vertical/vertical (VV) geometry, while the second polarizer allows light through which is in the same direction as the primary polarizer. On the contrary, in vertical/horizontal (VH) geometry, the second polarizer allows light which is not in same direction as the incident light.
\nDLS is used for characterization of the various particle sizes (PSs), including proteins, carbohydrates, micelles, polymers, and nanoparticles. If the system is not dispersing in size, the average effective diameter of the particles can be determined [19]. This measurement depends on many factors as size of the particle core, size of surface structures, particle concentration, and the type of ions present in the medium. The diffusion coefficient of the particles can be determined since DLS typically measures fluctuations in scattered light intensity due to diffusing particles. DLS software of commercial instruments displays the distribution of particle population at different diameters [20]. In a monodisperse system, there should be only one population, while a polydisperse system would exhibit multiple particle populations. If a sample contains more than one size population, then either the CONTIN analysis should be applied for PCS instruments or the power spectrum method should be applied for Doppler shift instruments [21]. DLS can be used for convenient stability studies. Conducting periodical DLS measurements of a sample can demonstrate whether the particles aggregate over time. This can be achieved by checking for an increase in the particle hydrodynamic radius. If particles actually aggregate, a larger population of particles with a larger radius should be observed. In addition, some DLS machines can analyze stability depending on temperature by controlling the temperature in situ.
\nThe average casein micelle size varies widely between milk samples of individual cows. Casein micelle size is also variable and can range between 154 and 230 nm in milks from individual cows [22]. Moreover, micelles as small as 125 nm and as large as 487 nm have been reported in fractionated bulk milk [23]. Casein micelle size may be influenced by cow genetics, e.g., casein protein variants [24–26], protein post-translational modification involving phosphorylation and glycosylation of casein molecules, and the levels of mineral compounds, such as calcium, in the milk [27]. Micelle size may also be influenced by farming and environmental factors such as feed [28] and season [29]. On the other hand, casein micelle size is of an important significance concerning influence the renneting behavior and the texture of manufactured dairy products [30]. The main used techniques for the analysis of PS and particle size distribution (PSD) are DLS, nanoparticle tracking analysis (NTA), scanning electron microscopy (SEM), size exclusion chromatography (SEC), cell electrophoresis, analytical ultracentrifugation (AUC), etc. Various analytical techniques may give different results [31–34]. DLS is the most user-friendly and it gives relatively accurate and consistent results of protein samples which can be obtained in a short period of time [35]. The size of casein micelle is estimated in raw milk samples, pasteurized milk, bulk milk, milk powder, etc. Here we will highlight determining of casein micelle size in raw milk by the DLS method.
\nResearch of Bijl et al. [25] have revealed the casein micelle size in raw milk in 50 Holstein-Friesian cows and 54 Montbéliarde cows. Initially, all animals were genotyped for the k-casein gene. On the basis of average casein micelle size, the authors defined two types of micelles: small (170.6 ± 9.1 nm) and large (206.7 ± 5.0 nm). The results showed no significant differences between the two types of micelles and caseins concentration in milk. On the other hand, there was a positive influence between k-casein and casein micelle size. The mean casein micelle size in CSN3 AA cows was 203.5 ± 14.6 nm), while in cow with AB genotype, it was 173.1 ± 5.4 nm. This study was the first to report a correlation between casein micelle size and glycosylation of k-CN. The authors concluded that changes in the structure of k-casein clusters caused by glycosylation can influence micellar stabilization during or after casein micelle formation in the mammary gland, and thereby influence casein micelle size.
\nAnother study by de Kruif and Huppertz [22] has revealed the casein micelle size in individual milk samples in 18 Holstein-Friesian cows by DLS measurement. Data from Figure 4 showed clear differences between hydrodynamic radius (Rh) between cows but do not vary as a function of stage of lactation. To study the effect of different lactations on casein micelle size, the authors have collected the individual milk samples from 68 cows.
\nThe results revealed that Rh did not change during milking, lactation, or even over a period of 3 years. This suggests that casein micelle size is strongly genetically determined and is extremely constant in the milks of individual cows. In this study, casein composition of each individual milk samples was determined by RP-HPLC. The results showed that first there were no significant differences between casein composition, as a function of stage of lactation, and second, casein composition did not influence on the casein micelle size (Figure 6).
\nHydrodynamic radius at a scattering angle of 93° in the milk of 15 individual cows taken at 4 different time points during a single lactation [22].
The study of Hristov et al. [26] aimed to compare the size of casein micelle in individual milk samples in dependence of kappa casein (CSN3) genetic polymorphism in 16 cows of Bulgarian Rhodopean cattle breed. The three defined kappa casein genotypes AA, AB, and BB were used for determining the casein micelle size by DLS. On the other hand, individual milk samples of each cow were assessed for protein and fat content. The results showed differences in the size and polydispersity of the casein micelles in milk from cows with different genotypes. The hydrodynamic radii of micelles at a scattering angle of 90°C varied from 80 to 120 nm and polydispersity varied from 0.15 to 0.37. The authors concluded that the casein micelle size of CSN3 AA cows (~120 nm) exceeds in about 60% of cows with AB (~80 nm) and BB genotype (~70 nm). In contrast, protein and fat content in milk cannot be correlated to casein micelle size. The obtained results could be useful for improving technological properties of milk and the yield of milk products (Figure 7).
\nRepresentative DLS distributions in raw milk sample from heterozygous AB (CSN3) cow: a) correlation function, b) intensity particle size distribution, and c) multimodal number particle size distribution [26].
Beliciu and Moraru [36] have analyzed the effect of the solvent on the accuracy of casein micelle PS determination by DLS at different temperatures and how to establish a clear protocol for these measurements. DLS analyses were performed at 6, 20, and 50°C in raw and pasteurized skimmed milk as sources of casein micelles. The pH, ionic concentration, refractive index, and viscosity of all solvents were determined. The solvents were evaluated by DLS to ensure that they did not have a significant influence on the results of the PS measurements. The authors concluded that when an ultrafiltered permeate was used as a solvent, the PS and polydispersity of casein micelles decreased as temperature increased. The effective diameter of casein micelles from raw skimmed milk diluted with ultrafiltered permeate was 176.4 ± 5.3 nm at 6°C, 177.4 ± 1.9 nm at 20°C, and 137.3 ± 2.7 nm at 50°C. Overall, the results of this study suggest that the most suitable solvent for the DLS analyses of casein micelles was casein-depleted ultrafiltered permeate. Dilution with water led to micelle dissociation, which significantly affected the DLS measurements, especially at 6 and 20°C.
\nA similar investigation was carried out by Mootse et al. [37]. The authors aimed to study casein micelle size in individual Estonian Holstein dairy cows during 1-year period by DLS. The main results of this study can be summarized as follows: 1) The average mean intensity (mode) of casein micelle particle size distribution (CM PSD) in raw milk of Estonian Holstein dairy cows was 171.13 nm and its variation (range 135–210 nm) resembled statistically normal distribution. 2) There was a weak correlation between average mode and its variation in milk samples of individual cows which may refer to the possible influence of cows\' physiological status, disease incidences, and stages of lactation, etc., which will be studied in a further research.
\nDevold et al. [28] have screened the influence of genetic milk protein variants on mean size of native and heated casein micelles in 58 cows of Norwegian red cattle breed. The results showed that the mean size of native and heated casein micelles was significantly influenced by the following parameters: group of cows (different feeding regime), genotype of αS1-casein (native mean size only) and k-casein, pH and the content of casein, whey protein, and casein number (Table 1).
\nVariable | \nMean size of casein micelles | \n||
---|---|---|---|
Native | \nHeated | \nChange | \n|
αs1-CN | \n(BB > BC)a | \nc | \nc | \n
β-CN | \nc | \nc | \nc | \n
α-CN | \n(AA, AE > AB)b | \n(AA, AE > AB)a | \nc | \n
β-lg | \nc | \nc | \nc | \n
Fed. reg. | \n(Gr.3E > Gr.1)a | \n(Gr.3E > Gr.1)a | \nc | \n
W.O.Lact. | \nc | \nc | \nc | \n
Casein (%) | \nb | \nb | \nc | \n
WP (%) | \nb | \nb | \nc | \n
Casein no. | \nb | \na | \nc | \n
pH | \na | \na | \nc | \n
Ca2+ | \nc | \nc | \na | \n
Ca | \nc | \nc | \nc | \n
Mg | \nc | \nc | \nc | \n
Citrate | \nc | \nc | \nc | \n
Variation for mean size of native and heated casein micelles and for heat-induced change in size.
Scanning electron microscopy (SEM) imaging of the casein gel network produced from left, small casein micelle, and right, b large casein micelle milks. Images are at 10,000× magnification, scale bar = 1 μm [30].
Logan et al. [30] investigated the combined effects of milk fat globule size (MFG) and casein micelle size in bulk milk on the onset of gelation and the maximum rate of gelation. The results showed that casein micelle size is the major determinant of rennet gel strength. Milk fat globules could conceivably enhance the rheological properties of the rennet gel depending on their size in relation to the pore size of the casein micelle network. When the gels were formed with milks of large casein micelle, the size of MFG did not affect the gel properties, probably because both large and small MFG were smaller than the pore size of the large casein micelle network. On the other hand, when the gels were formed with milks of small casein, the large MFG enhanced the gel firmness more than the small MFG. It is possible that when the size of large MFG fits well with the pore size of the casein micelle network, a synergistic effect on rennet elastic gel network is provided (Figure 8).
\n\nFinally, the effect of gross composition, protein composition, total and ionic calcium content, phosphorous content, and casein micelle size on chymosin-induced gelation was determined in milk from 98 Swedish Red cows by Gustavsson et al. [36]. The obtained results showed that protein content, ionic calcium concentration, total calcium content, and casein micelle size were the most important factors explaining the variation of gelation properties in this sample set of Swedish red cows. Furthermore, it was shown that composite effect of beta and kappa casein genetic variants have an effect on casein micelle size and it was suggested that this could be the reason for previously published differences in gelation properties between the composite genotypes in the present data set. Non-coagulating milk is a problem in Swedish red cows and the present study showed that non-coagulating milk is more common in cows in their first parity than in cows in their second parity.
\nMany products in the food industry, including milk, ice cream, mayonnaise, jam, etc., are colloids or have been produced via colloids. Dispersed particles determine, through their concentration, distribution, size, and structure, the physicochemical and organoleptic quality properties of a product. Additionally, the complex interactions between colloidal particles and with other ingredients will contribute to the rheology, texture, stability, appearance, and many other food characteristics. Techniques to measure the particle properties of the colloid systems allow for better understanding of these complex relations and further improvement food processing techniques and recipes. Furthermore, the implementation of such sensing-technologies in a food plant could enable online monitoring of the colloids or the derived products during the production process and promote early detection of an altering product quality.
\nNowadays, when society is leading an increasingly sedentary lifestyle with constant access to food without the need for effort, we observe the raising occurrence of diseases with metabolic dysregulation. This financial and social burden has caused the great need for understanding mechanistic details of metabolic response pathways, causes of their impairment, and following consequences. Carbohydrate metabolism is mainly related to glucose. Its level should remain in a narrow range (4–7 mM) by balancing glucose release into the circulation, its absorption from the intestine, the breakdown of stored glycogen in liver, and the uptake of blood glucose by peripheral tissues. These processes are regulated by a few metabolic hormones with insulin being the most important one.
\nInsulin is an anabolic peptide hormone secreted by pancreatic β cells, whose mature form arises in two stages [1]. First, preproinsulin is processed via cutting of the signal fragment and forming proinsulin [2]. This is followed by the excision of the middle fragment (C chain—35 aa), which gives dipeptide made up of two chains (A—21 aa, B—30 aa) connected by two disulfide bonds [3]. Insulin is a multitask protein involved, among others, in the regulation of carbohydrate and lipid metabolism (Table 1). The most important stimulus for insulin production is a postprandial increase of blood glucose level. By increasing insulin production and its impact on effector cells (myocytes, adipocytes, and hepatocytes), glucose transport to the inside of the cells gets increased while reducing blood glucose level. This is achieved by an increased translocation of the insulin-dependent glucose carriers (GLUT), with GLUT-4 being found in skeletal muscle, hepatocytes, and adipocytes [4].
\n\n | Upregulation | \nDownregulation | \n
---|---|---|
Carbohydrate metabolism | \nGlucose uptake via GLUT4 Glycogen synthesis Glycolysis Conversion of pyruvate to acetyl CoA | \nGlycogenolysis Gluconeogenesis | \n
Lipid metabolism | \nFatty acids synthesis Triglycerides synthesis Cholesterol synthesis | \nLipids oxidation Triglycerides breakdown | \n
Protein metabolism | \nTranscription of proteins involved in energy stores generation | \nTranscription of proteins involved in energy stores release | \n
Metabolic functions of insulin.
When glucose concentration exceeds 30 mM in the small intestine, glucose transport to the inside of the pancreatic β cells is initiated in an insulin-independent way via GLUT2 (Figure 1). GLUT2 facilitates transport with a concentration gradient. Inside the cell, glucose is converted into glucose-6-phosphate, which prevents the equalization of glucose levels and sustained transport to the cell. Glucose-6-phosphate enters the glycolysis, which results in the production of ATP molecules. As a result of a continuous glucose supply, the level of ATP is constantly increasing. This causes an inhibition of the potassium channel with the outflow of K+ ions from the cell being blocked. K+ ions concentration increases inside the cell, which becomes electropositive until the charges on the membrane are aligned and membrane becomes depolarized. Depolarization activates the voltage-dependent calcium channel, promoting the influx of Ca2+ ions to the cell. Ca2+ ions activate the ryanodine channel located in the membrane of insulin-accumulating vesicles, inducing their migration into the cell membrane and releasing their content [5].
\nInsulin release. Glucose is transported into β-cells via GLUT2 in an insulin-independent way with concentration gradient. Then, glucose is phosphorylated by glucokinase to glucose-6-phosphate, which allows for its inclusion to metabolic processes and ATP production. Raised ATP level triggers accumulation of K+ ions along with membrane depolarization. The latter activates Ca2+ channels, leading to increased concentration of Ca2+ ions inside the cell and consequent release of insulin from vesicles. For details see text.
Released insulin participates in many metabolic actions, such as glycogen deposition in liver and skeletal muscles, a stimulation of lipogenesis and inhibition of lipolysis, and repression of gluconeogenesis in liver, but mainly in increased glucose uptake through insulin receptor signaling pathway [6]. Signal transmission from the blood to the inside of the cell is a complicated and strongly integrated process. It begins with binding of the hormone to the insulin receptor (IR), eliciting the large protein signal complex formation just below the surface of the cell membrane around IR’s cytoplasmic domains (Figure 2) [7]. IRs are heterotetrameric glycoproteins containing two extracellular (α) and two intracellular (β) subunits. They occur mainly on the cell surface of metabolically active tissues like muscles, liver, and fat. The binding of insulin by extracellular subunits leads to IR dimerization, which allows ATP binding to β-subunits [8]. This causes the activation of the catalytic domains of tyrosine kinases in the cytoplasm [9]. In the first stage, there is an autophosphorylation of the receptor followed by phosphorylation of several substrate proteins, where IRS (insulin receptor substrate) proteins seem to be most significant ones. The phosphorylation occurs on tyrosine residues, and then, phosphorylated IRS proteins can trigger two major signaling pathways. First pathway leads from Ras to mitogen-activated kinases (MAPK), being involved in the expression regulation of genes playing a role in cell growth and differentiation. The second one, phosphatidylinositol 3 kinase (PI3K) pathway, elicits AKT/PKB kinase phosphorylation, and it is responsible for the metabolic action of insulin.
\nInsulin signaling pathway. Insulin attaches to insulin receptors triggering its dimerization and intracellular autophosphorylation of their tyrosine residues, which constitute an attachment for IRS proteins. These molecules also undergo phosphorylation and form a complex with PI3K utilizing SH2 domains. PI3K phosphorylates PIP2, which results in PIP3 formation and activation of PDK1/2. AKT gets phosphorylated and activated by PDK1/2, subsequently eliciting phosphorylation of AS160. The latter is responsible for GLUT4 translocation to cellular membrane and glucose inflow.
As shown in Figure 2, activation of PI3K/AKT pathway starts with binding of IRS proteins via SH2 domains to PI3 kinase regulatory subunits. This results in the activation of PI3K that phosphorylates phosphatidylinositol 4,5-biphosphate (PIP2) to phosphatidylinositol(3,4,5)-triphosphate (PIP3). This, in turn, leads to the activation of PIP3-dependent kinases: PDK-1 and PDK-2 and eventually to the activation of AKT/PKB kinase and atypical PKCs [10]. Subsequently, AKT catalyzes the phosphorylation of AS160 substrate protein that stimulates the translocation of GLUT glucose transporters from the cytoplasmic vesicles onto the cell membrane surface and thereby increases the insulin-dependent transport of glucose into the cell. GLUT4 occurs mainly in the interior of the nonstimulated cell, due to the proper proportion of two actions: slow exocytosis and rapid endocytosis. AS160 increases GLUT4 exocytosis and inhibition of its endocytosis via its downstream target, Rab10, in adipocytes. This results in GLUT4 accumulation in the plasma membrane [11]. Besides the activation of insulin-dependent glucose uptake via GLUT4, AKT has many intracellular targets and mediates numerous metabolic effects. For instance, AKT triggers phosphorylation of glycogen synthase kinase 3 (GSK3), which leads to stimulation of glycogen synthesis in liver and skeletal muscle [12].
\nThe PI3K/AKT pathway is under strict control, and its disturbances are the cause of many diseases, including primarily insulin resistance. Further knowledge of the mechanisms regulating this signaling is one of the most important challenges of modern science. Currently, three specific signaling nodes have been distinguished: (a) IRS proteins, (b) regulatory-PI3K kinase subunits, and (c) kinase isoform Akt/PKB [13]. Disturbances of any of these nodes are mainly responsible for the reduction of the signal transmission efficiency and related diseases.
\nIRS family consists of six proteins (IRS1–6), where two representatives, IRS1 and IRS2, are crucial in insulin signaling transduction. IRS proteins show tissue-specific expression and functionality [14]. They have three characteristic domains: (a) pleckstrin homology domain at N-terminus, (b) a phosphotyrosine-binding domain enabling binding to IR in the center, and (c) several sites of phosphorylation on tyrosine and serine residues at C-terminus. After tyrosine residues become phosphorylated, IRS binds by C-terminus domain to molecules containing an Src-homology-2 domain (SH2) [15]. IRS-1 and IRS-2 are widely expressed in all tissues, playing major roles in the maintenance of energy balance: muscle, liver, fat, and pancreatic islets. However, it seems that IRS1 plays the main role in myocytes and adipose tissue, while IRS2 is a key player in hepatocytes and islet cells [16, 17].
\nGenerally, there are three ways allowing the regulation of IRS (Figure 3). Crucial control occurs mainly by multiple serine and threonine residues, which may be phosphorylated by different kinases. The phosphorylation of serine residues may inhibit insulin signaling by blocking tyrosine phosphorylation, which is necessary for signal transduction. However, the details of this inhibitory mechanism are still not well understood. Indeed, there is a strong correlation among serine phosphorylation, decreased tyrosine phosphorylation, and insulin resistance, which is closely related to abnormalities within PI3K pathway. Most critical enzymes being able to phosphorylate IRS in serine residue are stress-induced kinases like ERK, JNK, and AMPK along with inflammatory kinase IKK and other downstream kinases, such as AKT, atypical PKC isoforms, mTOR, or S6K [18, 19]. Blockage of IRS causes the reduced cell response for stimulation with insulin and formation of insulin resistance, the first step toward diabetes. This inhibitory phosphorylation mostly occurs because of low-grade inflammation state, which is caused by lipid accumulation [20]. Studies on palmitate showed that it significantly decreased the insulin-stimulated Ser phosphorylation of Akt and Tyr phosphorylation of IRS-1 [21]. Some drugs exert similar effect. The prominent example is simvastatin, which is commonly used in the prevention and treatment of cardiovascular diseases. Simvastatin reduces the phosphorylation of insulin-induced IR at Tyr, IRS-1 at Tyr, and AKT at Thr [22, 23]. Therefore, therapy with simvastatin or other statins might be a risk factor for the development of insulin resistance or diabetes. This effect can be decreased by many natural substances like silibinin (principal flavonoid contained in silymarin, a mixture of flavonolignans extracted from Silybum marianum seeds). Silibinin prevents PI3K/AKT pathway inhibition by decreasing IRS1 phosphorylation on Tyr [24]. Similar mechanism is typical of PTP1B (protein-tyrosine phosphatase 1B), whose overexpression can inactivate the whole PI3K pathway [25]. Since this protein was found to be overexpressed in insulin-sensitive peripheral tissues (fat, muscle) and in hepatic cells during insulin-resistant state, searching for PTP1B inhibitors has become an important area of research in the treatment of impairment of insulin transmission pathway. FYGL (Fudan-Yueyang G. lucidum extract) appears to be a promising substance showing PTP1B inhibitory activity with weak cell permeability and bioavailability [26, 27].
\nOverview of three major mechanisms affecting IRS-dependent signal transduction. Signaling via IR may be modulated simply by the decreased rate of IRS gene transcription. Second, proteins with PTB domains may compete with IRS for binding to phosphotyrosines of IR. Finally, IRS phosphorylation of serine residue is known to suppress phosphorylation of its tyrosine, which is indispensable for signal transduction.
IRS function can be also regulated by competitively inhibiting the binding of IR to IRS, primarily by proteins containing phosphotyrosine-binding (PTB) domain. One of them, NYGGF4, is highly expressed in obese individuals. Studies on skeletal myotubes showed the reduced insulin-induced phosphorylation of IRS1 at Tyr and Akt phosphorylation at Ser residue without changes in the insulin-stimulated tyrosine phosphorylation of IR [28, 29].
\nAmong other IRS modulatory mechanisms, it is worth mentioning about expression regulation of IRS mediated by hyperinsulinemia and other hormones [30]. Anjali et al. showed that FSH (follicle stimulating hormone) induces expression of IRS2 in granulosa cells [31]. Also, some natural medicines like Tangzhiqing formula, a mix of five herbs, modulate IRS expression level in HEPG2 cells (IR1 and IRS2) and L-6 myotubes (IRS1) [32].
\nPI3 kinases constitute protein family, which exhibits activity of phosphorylation of lipids and proteins. They are divided into three groups according to their structural features and substrate preferences (Table 2). Members of I class are the most crucial in insulin signaling pathway. PI3K-1 are heterodimers made up of regulatory and catalytic subunits. The regulatory subunit is generally referred to as p85. They all have a similar domain structure: SH3 domain, breakpoint cluster region homology (BH), and two SH2 domains with iSH2 (interSH2) domain in between [33]. Signaling is initiated by p85 interacting through the SH2 domain with IRS phosphotyrosine motif. Subsequently, p85 is joined through its iSH2 domain to the adapter binding domain (ABD) of catalytic subunit called p110. Besides ABD, p110 also contains Ras-binding domain (RBD), which is involved in interaction with Ras protein superfamily, C2 and the helical scaffolding domains, along with kinase domain participating in PIP3 formation [34].
\nClass | \nMembers | \nCatalytic subunit | \nRegulatory subunit | \nMain reaction | \nReference | \n
---|---|---|---|---|---|
Ia | \nPI3Kα PI3Kβ PI3Kδ | \np110 (α/β/δ) | \np85α, p55α, p50α, p85β, p55γ | \nPtdIns(4,5)P2 → PtdIns (3,4,5)P3 | \n[33] | \n
Ib | \nPI3Kγ | \np110γ | \np101 p84/87 | \nPtdIns(4,5)P2 → PtdIns (3,4,5)P3 | \n[33] | \n
II | \nPI3K-C2α PI3K-C2β PI3K-C2γ | \nMonomeric | \nPtdIns(4)P → PtdIns (3,4)P2 | \n[76] | \n|
III | \nPI3K-C3 | \nVps34 | \nVps15 | \nPtdIns → PtdIns (3)P3 | \n[77] | \n
Classification of PI3K family members.
p85 protects p110 from degradation by forming a heterodimer. Furthermore, this binding allows p110 translocation to the cell membrane, where catalytic subunit is able to send a signal via phosphorylation of PIP2 to PIP3, a lipid second messenger. Interestingly, p110α is the most prominent one from all PI3K catalytic subunits in insulin-dependent pathway [35]. Cells with its deletion exhibit hyperglycemia and glucose intolerance [36]. While p110β seems to play a secondary role, its presence is necessary for p110α activity and thus maintenance of basal threshold of PIP3 [37, 38]. PIP3 is bound by proteins with PH domain such as AKT and PDK1. This critical event allows further signal transduction to downstream proteins.
\nIn this control node, a few aspects are taken into account. Firstly, signaling via PI3K is critically dependent upon PI3K regulatory subunit with p85 mediating either its restriction or promotion. In cells deprived of upstream stimuli, p85 reduces p110 activity. It is executed through C2 and helical scaffolding domains, which form inhibitory contacts with p85. Furthermore, monomeric p85 binds to phosphorylated sites of IRS, thus blocking p85-p110 heterodimer attached to IRS [39]. p110, another essential regulatory molecule, undergoes spatial regulation in some types of human cancer. Studies on HepG2 cells demonstrated that PAQR3 (progestin and adipoQ receptor family member 3) associates with p110α by attracting it to Golgi apparatus, a place of PAQR3 exclusive localization. This event inhibits the interaction between p85a and p110α [40, 41].
\nThere are two other possible PI3K activation pathways, both being dependent on ligand-membrane receptor binding. The first mechanism is based on binding the adaptor protein GRB2 to RTK (receptor tyrosine kinase). When GRB2 is already attached to GAB protein, it is allowed to bind p85. By contrast, the second way of PI3K activation is not dependent on p85 subunit. In this scenario, GRB2 binds to SOS, which activates RAS, leading to activation of p110α subunit. In addition, the p110β catalytic subunit may be stimulated in a similar, p85-independent way via G protein-coupled receptors [42].
\nAnother critical regulatory mechanism is associated with the control of PIP3 level. There are several well-known inhibitors which dephosphorylate PIP3 with phosphatase and tensin homolog (PTEN) being the most well-known one. Undoubtedly, PTEN is an intriguing protein for research in the context of diseases with PI3K signaling impairment. For instance, in adipose tissue, it can be blocked by H2S or its precursor,
Last but not least, PI3K dysregulation can be also underlain by gene mutations of p110α and p85 subunits or PI3K negative regulators. For instance, loss of function or deletion of PTEN is known to occur in numerous types of cancer. Therefore, enormous attempts are put into research focused on searching compounds targeting PI3K. The most common PI3K regulators are Wortmannin (steroid fungal metabolite) and LY294002 (morpholine-containing chemical compound) [51]. Moreover, there are multiple members of a new generation of more stable molecules such as SF-1126, CAL101, GSK615, XL147, and PF-4989216, which evoke the suppression of overactive PI3K signaling particularly in cancer [52].
\nAKT (also named PKB) occurs in mammals in three isoforms (AKT1, AKT2, and AKT3). Although they share a similar domain structure (N-terminal PH domain, a central kinase domain, and C-terminal domain), AKT isoforms exhibit target specificity and play divergent roles. AKT2 is the most essential in glucose uptake [53].
\nThe PH domain enables AKT to be attracted by PIP3 just as PDK1. After binding to PIP3, AKT undergoes conformational changes that allow revealing the phosphorylation site. While they are in nearby, PDK is able to phosphorylate AKT on Thr308. Nevertheless, for full activation of AKT (besides AKT3), second phosphorylation on Ser residue is necessary (AKT1-Ser473 and Ser-474 AKT2). Ser473 is modified by PDK-2/mTORC2 (mammalian target of rapamycin complex 2) [54]. AKT activation is terminated through the action of PP2 (protein phosphatase 2) and PHLPP (PH domain leucine rich repeat phosphatase), which perform dephosphorylation of Thr308 and Ser473, respectively [55].
\nWhile phosphorylation status of both of these sites is fundamental for AKT activity, there is plethora of other posttranslational modifications affecting its performance [56]. For instance, oxidation of Cys124 triggered by PDGF-induced (platelet-derived growth factor) ROS leads to the blockage of AKT2 activity [57]. Besides PI3K-dependent activation, AKT may be switched on by alternative modulators. Namely, two groups of uncommon AKT activators are distinguished: tyrosine kinases (e.g., ACK1, SRC, PTK6) and serine/threonine kinases (e.g., TBK1, IKBKE). ACK1, a non-receptor tyrosine kinase, is capable of regulating AKT recruitment to the plasma membrane due to AKT phosphorylation on Tyr176, making it preferentially binding to phosphatidic acid—a membrane phospholipid. This elicits AKT attachment to plasma membrane even in the presence of some specific PI3K inhibitors. The increase of AKT2 activity occurs in many cancers, which may be underlain by auto-activating mutations of ACK1. Another nonreceptor kinase involved in AKT regulation is Src. Its action takes place on Tyr315 and 326. By contrast, PTK6 responds to epidermal growth factor (EGF), whose overexpression is typical of many cancers, via phosphorylating Tyr215 and 326. Modifications triggered by Src and PTK6 are resistant to some popular PI3K inhibitors. The second group of AKT activators, Ser/Thr kinases, modifies Thr195, Ser378, and Ser473 (TBK1), as well as Ser137, Thr308, and Ser473 (IKBKE). These alternative activation modes may suggest that under some particular conditions, cells can turn on AKT signaling in quick response [58].
\nDue to the fact that AKT, just like PI3K, is one of the most commonly deregulated molecules in human cancers, AKT inhibitors development constitutes an important field of research. Currently tested molecules utilize two major mechanisms. First group acts as competitors for ATP-binding site of AKT (e.g., GSK690693, GDC-0068, GSK2110183, and GSK2141795). They share features of major pharmacophore with minor differences. The second group is composed of allosteric AKT inhibitors (e.g., 2,3-diphenylquinoxaline and analogs, alkylphospholipids). Many of these molecules are in clinical trial phase and have a potential in the treatment of AKT dysregulations [59].
\nThe relationship between environmental factors like diet, drugs, lifestyle in general, and PI3K pathway remains undeniable. Herein, we will discuss major agents responsible for PI3K modulation. In terms of mediated effect, they can be divided into two types: insulin sensitizing factors and insulin-resistance inducing factors. They do not usually affect a specific protein, but through their action, they dysregulate the entire pathway and the overall metabolism.
\nDue to the fact that insulin is one of the key regulators of metabolism, it is not surprising that the most important factor modulating its action is diet. Impairment of PI3K signaling is well known to be connected with obesity. Depending on the tissue, the mechanism of obesity-induced insulin resistance seems to differ, but it is in general connected with lipid overload. In liver and muscles, the most crucial is elevation of FFA level, which is characteristic for the obese. In consequence, toxic lipids, mainly ceramides and diacylglycerol (DAG), do accumulate. The increased amount of ceramides causes PP2A stimulation, which terminates insulin pathway via AKT dephosphorylation. On the other hand, DAG activates PKC isoforms (ε and θ) [60]. The latter ones are able to obstruct signaling either by IRS (muscles) or IR (liver). PKC isoforms activation leads to increased expression of NFκB (nuclear factor kappa-light-chain-enhancer of activated B cells), which takes part in inflammatory cell response. Subsequently, NFκB activates pro-inflammatory cytokines and stress-induced serine-threonine kinases like JNK, which are able to block insulin signaling pathway via improper IRS phosphorylation. Furthermore, the increasing concentration of lipids in the cells leads to the aggregation of toxic metabolites derived from the incomplete oxidation, and, as a result, the elevated synthesis of free radicals. This is also correlated with increased activation of stress-induced kinases. In overall, these events lead to PI3K pathway impairment and the emergence of insulin resistance [60, 61, 62].
\nThe mechanism of obesity-induced insulin resistance formation in adipose tissue is also related to lipid overload but has a different course. It is connected to the constant enlargement of adipocytes, which along with dysregulation of adipogenesis leads to the introduction of hypoxia. Reduced oxygen supply introduces cellular stress response, which includes activation of stress-induced kinases, pro-inflammatory cytokines, and tissue infiltration by pro-inflammatory macrophages. These events result in low-grade inflammation state characteristic of PI3K impairment. Adipose tissue is not only an energy reservoir but also an active endocrine organ, which produces hormones called adipocytokines. They are sensors of nutritional and metabolic homeostasis. Accumulation of visceral fat and inflammation development alters the secretary profile of adipocytokines. Adipocytes start to send pro-inflammatory signals like TNF-α and interleukin1 (IL1). Other typical insulin resistance-inducing cytokines are resistin and IL-6, which activate pro-inflammatory pathways of NFκB and JNK kinase, leading to defective response to insulin [63].
\nWhile prolonged high-calorie diet undeniably leads to insulin resistance, proper dietary style can be a sensitizing factor as well. There are many diet supplements improving insulin signaling. Herein, we will point out only a few members of this enormous group. For instance, glutamine (Gln) supplementation Gln increases the expression of key PI3K signaling molecules (PI3K, PDK1, and GLUT4) and promotes AKT phosphorylation, GLUT4 translocation, and glucose uptake in the presence of insulin during exposure to hyperglycemia [64]. An epidemic of obesity and numerous side effects of drugs that increase insulin sensitivity has caused the great interest among scientists to search for natural sensitizers. They include dieckol (an extract from a brown seaweed), which enhances translocation of GLUT4 in peripheral tissues [65]. Another seaweed improving glucose uptake is Gelidium amansii. It exhibits antihyperglycemic, antioxidant, and antiobesity effects potentially via PI3K/AKT/GLUT4 signaling [66]. Also, carnosol, a compound found in spices such as sage or rosemary, increases glucose uptake via GLUT4 [67]. Interestingly, it has been proven that 1,25-dihydroxyvitamin D3 (active form of vitamin D3), which is mainly provided with food, can improve glucose uptake and has a potential in acting as an anti-inflammatory factor [68]. It seems that an alternative for typical drugs like metformin or pioglitazone, which cause side effects, may be products containing natural substances like Jiangtang Xiaoke granule. The latter is composed of 10 herbs, and it can significantly increase the expression of vast PI3K proteins in mice even upon hyperglycemia [69].
\nComponents of the diet are not the only ones able to improve the signaling via discussed pathway. Studies on rat model demonstrated that long-term caloric restriction may enhance AKT2-dependent mechanism for improving insulin-stimulated glucose uptake. Moreover, a lot of research has been carried out to indicate that physical exertion has a positive effect on insulin [70, 71, 72, 73].
\nPI3K pathway impairment is related to many diseases, among which the most common and worth attention are insulin resistance and numerous types of cancers.
\nInsulin resistance may be defined as a subnormal glucose response to endogenous and/or exogenous insulin. Peripheral tissues are not able to respond to the hormone by increasing glucose uptake from the bloodstream. Initially, pancreatic β-cells are not harmed yet, and in response to high glucose level, they synthesize more and more insulin. However, if this state lasts for a long time, islet cells start to overgrow, and deterioration of their function and/or decline of β-cell mass do occur. As normalization of glucose level does not occur, cells are becoming more and more resistant to insulin simultaneously forming a vicious circle of insulin resistance. The most affected tissues are the most metabolically active ones like liver, muscles, and fat. Although the pathogenesis of insulin resistance is getting better understood, the exact mechanism is still not clear. The causes may be connected to abnormal insulin production, but in most cases, the changes in insulin receptors and their substrates along with defects in post-receptor signaling play the role.
\nPI3K pathway is one of the most frequently deregulated signaling pathways in human cancers. As it plays an essential role in many biological processes like cell survival, proliferation, migration and differentiation, its dysregulation may result in tumorigenesis. The most common changes are mutations (PIK3CA, AKT1, and PTEN), genes amplification (PIK3CA, AKT1, and AKT2), and loss of expression or deletion of the tumor suppressor PTEN [74]. The highest prevalence of mutations within PI3K pathway is typical of lung cancer, breast cancer, endometrial cancer, and head and neck cancer along with glioblastoma [75].
\nInsulin is the most crucial agent in glucose metabolism. It stimulates glucose uptake from the bloodstream to peripheral tissues. Furthermore, it is responsible for energy storage through accelerating glycogen synthesis and lipogenesis. In general, it promotes cellular events leading to energy storage and represses processes of energy release (Figure 4). Insulin action takes place mainly through PI3K pathway and results not only in metabolic effects but also in mitotic response. Insulin is also involved in phenomena connected with cell survival. Multitasking nature of this hormone causes that any abnormality in its signal transmission can result in serious consequences, such as diabetes and cancer. These two diseases are the scourge of the modern world. The steadily increasing percentage of people suffering from insulin resistance or full-blown diabetes and the high incidence of cancer have caused scientists to focus on seeking therapeutic goals that may contribute to the prevention or treatment of these disorders. In insulin-resistance, the main target constitutes the improvement of insulin sensitivity. Among common approaches, it is worth to highlight two of them: increasing fatty acids oxidation and elongation of IR activation state by blocking PTP1B activity. Promising therapeutic targets seem to be also pro-inflammatory cytokines and other proteins involved in inflammation response. On the other hand, cancer cells show mainly hyperactivity of PI3K pathway and the increased glucose uptake. Therefore, it seems that blockage of impaired signal transduction may contribute to suppression of the growth of the tumor. For this reason, intensive search for selective inhibitors or silencers of the insulin pathway are underway. Conducting further research may become the basis for the development of new methods of prevention and more effective treatment strategies for these diseases.
\nCritical actions and pathways controlled by insulin.
This paper was supported by a grant no. 503/2-159-01/503-21-002 from the Medical University of Lodz and by The Polish Society of Metabolic Disease.
\nThe authors declare that there is no conflict of interest regarding the publication of this paper.
ABD | adapter binding domain |
ACK-1 | activated CDC42 kinase 1 |
AKT (PKB) | protein kinase B |
AMPK | 5’AMP-activated protein kinase |
AS160 | Akt substrate of 160 kDa |
BH domain | breakpoint cluster region homology domain |
EGF | epidermal growth factor |
ERK | extracellular signal-regulated kinase |
FSH | follicle stimulating hormone |
FYGL | Fudan-Yueyang G. lucidum extract |
GAB | GRB2-associated binding protein |
GLUT1–4 | glucose transporter type 1–4 |
GSK3 | glycogen synthase kinase 3 |
GRB2 | growth factor receptor-bound protein 2 |
HEPG2 | human liver cancer cell line |
IKBKE | inhibitor of nuclear factor kappa-B kinase subunit epsilon |
IKK | IκB kinase |
IR | insulin receptor |
IRS | insulin receptor substrate |
JNK | c-Jun N-terminal kinase |
MAPK | mitogen-activated protein kinase |
mTOR | mammalian target of rapamycin kinase |
mTORC2 | mammalian target of rapamycin complex 2 |
NYGGF4 (PID1) | phosphotyrosine interaction domain-containing protein 1 |
PAQR3 | progestin and adipoQ receptor family member 3 |
PDGF | platelet-derived growth factor |
PDK1 | pyruvate dehydrogenase lipoamide kinase isozyme 1 |
PH domain | pleckstrin homology domain |
PHLPP | PH domain leucine rich repeat phosphatase |
PI3K | phosphatidylinositol-4,5-bisphosphate 3-kinase |
PIP2 | phosphatidylinositol 4,5-bisphosphate |
PIP3 | phosphatidylinositol (3,4,5)-trisphosphate |
PKC | protein kinase C |
PP2 | protein phosphatase 2 |
PTB domain | phosphotyrosine-binding domain |
PTB1 | polypyrimidine tract binding protein-1 |
PTEN | phosphatase and tensin homolog |
PTK6 | tyrosine-protein kinase 6 |
PTP1B | protein-tyrosine phosphatase 1B |
RBD | Ras-binding domain |
ROS | reactive oxygen species |
S6K | ribosomal S6 kinase |
SH2 domain | Src-homology-2 domain |
SHIP | SH2-containing inositol 5′-phosphatase |
SOS | son of sevenless, guanine nucleotide exchange factor |
SRC | proto-oncogene tyrosine-protein kinase Src |
TBK1 | TANK binding kinase 1 |
IntechOpen - where academia and industry create content with global impact
",metaTitle:"Team",metaDescription:"Advancing discovery in Open Access for the scientists by the scientist",metaKeywords:null,canonicalURL:"page/team",contentRaw:'[{"type":"htmlEditorComponent","content":"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\\n\\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
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\\n\\nSara Uhac, COO
\\n\\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\\n\\nAdrian Assad De Marco
\\n\\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\\n\\nDr Alex Lazinica
\\n\\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
\\n"}]'},components:[{type:"htmlEditorComponent",content:"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\n\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\n\nBut, one thing we have in common is -- we are all scientists at heart!
\n\nSara Uhac, COO
\n\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\n\nAdrian Assad De Marco
\n\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\n\nDr Alex Lazinica
\n\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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