Figure 1.
Digital phantom generation procedure.
Citation: Robert Cote, Laura Lynn Eggink, J. Kenneth Hoober. CLEC receptors, endocytosis and calcium signaling[J]. AIMS Allergy and Immunology, 2017, 1(4): 207-231. doi: 10.3934/Allergy.2017.4.207
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The effect of radiation on a living organism is assumed to be proportional to the energy imparted and absorbed in the unit mass of the exposed bodies. Geometrical modeling of the human body plays an important role in the calculation of the energy deposited in the tissues and organs of the body [1]. In medical physics terminology, these geometrical models are called phantoms and are classified in three categories of stylized, voxelized and hybrid models [2].
Stylized phantoms, sometimes called mathematical phantoms, are the first generation of the anthropomorphic phantoms that are used for dose calculations in the human body [3]. In stylized phantoms, 3D shapes of different sizes/materials are defined in the computer memory to represent different organs of the body [4]. Stylized phantoms can only be used to characterize the gross shape of the human anatomy.
In the 1980s, voxel-based phantoms were introduced to represent the detailed geometry of the human body. These phantoms are normally generated from the computed tomography scans or magnetic resonance images [5]. The difficulty in the generation of digital phantoms involve the segmentation of tissues and assignment of the material composition to the voxels of phantoms. In recent years, many digital phantoms have been introduced to provide much better anatomical realism compared to stylized phantoms. Depending on the resolution of the tomographic image, it is sometimes difficult or even impossible to segment small or tortuous tissues in the generation of digital phantoms [6]. These phantoms have limited flexibility to change the shape/posture of the organs.
To overcome the limitations of digital phantoms, researchers have focused on combining the realism of voxel phantoms with the flexibility of mathematical phantoms to generate hybrid phantoms [7]. Non-uniform rational B-spline or polygon-mesh surfaces provide great flexibility to define the organs and the organ motion in hybrid phantoms [8]. Despite all of the theoretical advantages, it is not yet possible to integrate a hybrid phantom into the geometry used in a Monte Carlo (MC) simulation. In practice, a hybrid phantom generator produces one or more digital phantoms at the desired spatial/time resolution that can be integrated into the geometry of simulations. The conversion procedure always has some degree of digitization error that becomes prominent in the voxels at the boundary of moving tissues. Such voxels may become a mixture of bordering tissues representing uncharacteristic properties/compositions. The main advantage of hybrid phantoms is that they provide the opportunity to consider organ motion in imaging studies [9].
In practice, digital phantoms are the most widely used geometrical model for medical dosimetry. However, they are mainly used to represent large-scale geometries (e.g., body, torso, head) at the pixel size of millimeters, which is not suitable for dosimetry at a microscopic level [10]. Due to the limitations of current imaging systems, the production of high-resolution digital phantoms is not possible, although radiation interacts with atoms and molecules and radiation damage starts at cellular levels. Moreover, many radionuclides emit very short-range particles (such as alpha and Auger electrons), delivering very high doses in a small spot. Therefore, nuclear medicine therapy with short-range particles requires microscale dosimetry. Dosimetry in nuclear medicine is based on the Medical Internal Radiation Dosimetry method, which is a semi-analytical method based on slowing-down approximation, to calculate the fraction of energy released from the source component that is absorbed in the target component [11].
One of the critical microstructures of tissues in the human body are the nephrons inside the kidneys. Kidneys act as the main excretory pathway for the radiopharmaceuticals administrated to the body, and are therefore a critical organ in radionuclide therapy [12]–[14]. Nephrons are functional subunits of kidneys, and each are individually responsible for excess substances from the blood. A healthy adult has 0.8 to 1.5 million nephrons in each kidney with slightly different sizes and postures. Nephrons are classified as cortical or juxtamedullary nephrons. Cortical nephrons have small, short loops of Henle and are largely located at the outer renal medulla. They make up around 85% of the nephron population in a human kidney and perform the excretory functions over radiopharmaceuticals. Juxtamedullary nephrons are much smaller in population, and their main function is controlling the concentration of urea.
A nephron is itself composed of thousands of cells in a complex arrangement in the shape of a narrow, long, curved tube. Nephrons perform different actions (i.e., excretion, reabsorption) on different types of radiopharmaceuticals within their substructures (i.e., glomeruli, tubules). Dose distribution along a nephron varies with the type of radiopharmaceutical administered. The probability of radiation nephropathy is consequently dependent on the distribution of dose along the nephron or, more correctly, the dose distribution within the nephron cells. Deterring the dose distribution in nephrons requires a high-resolution phantom because, in macroscale dosimetry, only the average dose to the nephron population can be estimated. Experiential studies have shown signs of radiation toxicity when the average dose to the mouse kidney is well below the threshold level [15],[16]. These observations also evoke the need for investigation of the dose distribution at the microscopic level.
A digital phantom representing a typical nephron can be helpful to estimate the dose distribution between the cells of nephrons and investigate the subunit that receives the highest dose and is therefore the most likely to be damaged by a given radiopharmaceutical.
In this paper, an anatomical model of a kidney based on a 3D high-resolution digital phantom is introduced. The constructed phantom represents a typical cortical nephron with a detailed anatomy. In order to validate the phantom, MC simulations are performed using a similar method to Hobbs et al. [17], who used a stylized phantom of a nephron for the microdosimetry of alpha-emitting radionuclides; our results are compared to the results published by them. S-values for the electron source are also calculated, and the differences between the two phantoms are discussed.
Voxelized phantoms can represent the complex geometry at the desired size and resolution. The procedure for generating a phantom is represented in Figure 1. In this study, a 3D Wavefront OBJ format containing the polygonal mesh structure of the nephron surface was created. Using commercial software, different components of the nephron (i.e., the glomeruli, proximal and distal tubules, and loops of Henle) were separated. Binvox freeware was used to convert each component into the 3D voxelized format. Binvox is a command-line program that rasterizes a 3D model into a binary 3D voxel grid. The generated phantoms were processed independently to segment each compartment into the urinary duct and epithelial cells. In the case of the glomeruli, they were segmented into the Bowman's capsule, Bowman's space and glomerulus. The outer shell in each subunit is the cell region, and the remaining voxels represent the pathway of urine through which the source is distributed. Different tissue indexes have been devoted to different segments of each compartment as identification indexes. The resultant files were finally converted into the interfile format. Interfile is a file format developed for nuclear medicine image data. An interfile data set consists of two files, i.e., a binary file containing image data and an ASCII format containing information about the dimensions, identification and processing history. The interfile format can be recognized by the GATE toolkit. To adjust the size of each compartment to suit the morphological data, the voxel sizes were set individually in each header file.
Figure 2 illustrates the morphological analysis of the generated nephron phantom. The voxelized phantom and its compartments are shown in this Figure. Each subunit of a nephron has an endothelial cell layer and a lumen. The matrix sizes of the glomeruli, tubules and loop of Henle were set to be 160×300×80, 357×257×286 and 357×57×459, respectively. The corresponding voxel sizes of the glomeruli, tubules and loop of Henle were set to be 1.5×1.5×1.5, 1×1×1 and 0.85×0.85×0.85 µm3, respectively. The arrangement of the voxels representing glomerular endothelial cells is also shown in Figure 3. All three components of the nephron were imported into the gate geometry simultaneously. The placement was correctly adjusted to make the three components represent a single nephron. All of the geometrical structures were made up of water because more than 70% of the mammalian body is made up of water (unit density).
The problem with particle tracking inside of the voxelized phantom is changing the material properties of voxels and the need to renew cross sections at the boundaries between voxels with different materials. Gates have three extra navigating algorithms to save time and memory when tracking particles inside voxelized phantoms. The native regular matrix algorithm was selected since all of the voxel material was set to be water and the voxel boundary problem does not exist.
A specific index number was allocated to each compartment of the nephron. The material composition and activity distribution was independently set for the voxels with the same index number. The index number was also used for dose calculation and output data extraction.
The volumes of the phantom compartments were calculated according to the number of voxels encountered in each part and the voxel size. This ensured that, not only would the shape of the nephron be realistic, but the volumes would be equivalent. For the current work, the phantom was filled with water in consideration of the fact that biological material consists of about 70% water. This is still a simple approximation, and it would be interesting to produce the current work while taking into consideration the concentration of the various elements found inside a nephron; however, this requires further information about the chemical composition of nephrons.
All simulations in this study were performed using GATE version 7.2. GATE is a general-purpose MC code that simulates the transport of particles over a wide range of energies. It is becoming increasingly popular in the field of medical physics and a good method to investigate cellular-level dosimetry. Although numerical approaches have many advantages, MC simulations can track particles with more precision than numerical approaches [18]–[20]. GATE has three different physics lists for the simulation: Livermore, Penelope and Geant4-DNA physics [21]. In the case of the first two physics lists, a condensed history algorithm is used to track charged particles. In the case of the Geant4-DNA physics list, a track structure algorithm is used to track charged particles. Geant4-DNA was used to score the deposited energy of the electrons in each voxel of the phantom. In this physics list, electrons are tracked down to 11 eV while considering all the necessary interactions by using ENDL97 cross-section tables. Geant4-DNA was developed to provide predictions of the biological effects at the cellular level [22]. Although the track structure is the most advanced algorithm for electron tracking for microscale geometries, its application is limited to a water medium, and its computational time is considerably long. In GATE terminology, the required medium for Geant4-DNA physics is G4_WATER.
To evaluate the digital phantom using the data published by Hobbs et al., similar simulations were performed using alpha-emitting radionuclides. However, alpha emitters are rarely used in radionuclide therapy, and the most widely used radionuclides are beta emitters. All particles were assumed to be independently uniformly distributed inside the glomeruli and proximal tubules of the digital phantom. The number of historical records in the simulations was set to be 106 for alpha particles to keep the uncertainty in dose calculation under 5%. In the case of a stylized phantom GATE provides the uncertainty of dose per compartment, but, for a digital phantom, some additional calculation is required. GATE provides the uncertainty of dose per pixel. The uncertainty of the dose in the nephron compartment was calculated by using the root mean squares method. The uncertainty of voxels was squared and summed up. The result was divided by the number of pixels in the compartment and the square root was accepted as the uncertainty of dose in the compartment.
The first stage in this study involved making a comparison with the results derived by using the voxelized and stylized phantom used by Hobbs et al [14]. In their study, the energy deposited by the alpha particles emitting to the glomeruli and the proximal tubules of the phantom were calculated. The same energy values as those reported by Hobbs et al [14] were used for the alpha particles. Simulations were performed for each radionuclide independently. The dose delivered to each compartment was finally summed up after normalization of the yields and branching ratios.
The simulations were conducted by using a high-power computer with a Linux Ubuntu 16.04 operating system. The simulations were performed in parallel by using 60 CPUs simultaneously. The duration of simulation was between 30 hours and 60 days. No variance reduction technique was used in the simulations.
The dose derived in the simulations were scaled based on their history number and corresponding radiation yield and used to calculate the S-values for the single-unit nephron geometrical model (u S-values), as explained in the Hobbs et al. study [23],[24].
For further investigation of the digital phantom, the electron source (energy range: 1, 10, 20, 30, ...70 keV) was independently distributed uniformly inside the glomeruli and the tubules of the digital phantom. The history number in each simulation varied from 106 to 109 to keep the uncertainty under 5%. The DNA physics library was used to score the deposited energy of the electrons in each voxel of the phantom. Simulations were performed to calculate the electron specific absorbed fraction (SAF) for the glomeruli and the tubules of the digital phantom.
The absorbed energy in each compartment was calculated by averaging the voxel values in the entire region of the components. The absorbed energy in the voxels of the target region was divided by the total energy emitted from the source region. The result was divided by the total mass of the voxels in the target region to calculate SAF values.
Similar simulations were performed using the stylized phantom, and the corresponding SAF values were calculated.
Table 1 presents the S-values for the human nephron model generated in this study, as well as the Hobbs et al. S-values given for their single-unit model. The relative differences (RDs) between the results of the models were calculated as follows:
where SD and SH represent the S-values calculated using our digital phantom and the Hobbs et al. model, respectively.
| Radionuclide | Simulation using digital phantom (Gy/Bq·s) | Hobbs et al. results (Gy/Bq·s) | RD |
| Ac-225 | |||
| glc←glc | 5.90E-05 | 5.70E-05 | 0.0351 |
| glc←prt | 5.99E-06 | 5.39E-06 | 0.1113 |
| prtc←glc | 2.80E-04 | 3.02E-04 | −0.0722 |
| prtc←prtc | 4.94E-04 | 4.54E-04 | 0.0881 |
| prtc←prtl | 4.89E-05 | 4.49E-05 | 0.0891 |
| prtl←prtc | 4.92E-05 | 4.52E-05 | 0.0885 |
| Bi-213 | |||
| glc←glc | 7.58E-05 | 7.28E-05 | 0.041602 |
| glc←prt | 1.59E-05 | 1.36E-05 | 0.168108 |
| prtc←glc | 3.62E-05 | 4.22E-05 | −0.14215 |
| prtc←prtc | 6.81E-05 | 6.47E-05 | 0.052553 |
| prtc←prtl | 7.35E-05 | 6.45E-05 | 0.139032 |
| prtl←prtc | 7.56E-05 | 6.46E-05 | 0.170664 |
| Fr-221 | |||
| glc←glc | 1.31E-04 | 1.26E-04 | 0.039112 |
| glc←prt | 1.67E-05 | 1.57E-05 | 0.0656 |
| prtc←glc | 6.10E-05 | 6.91E-05 | −0.11781 |
| prtc←glc | 6.10E-05 | 6.91E-05 | −0.11781 |
| prtc←prtc | 1.09E-04 | 1.04E-04 | 0.045949 |
| prtc←prtl | 1.19E-04 | 1.03E-04 | 0.159007 |
| prtl←prtc | 1.12E-04 | 1.04E-04 | 0.076645 |
| At-217 | |||
| glc←glc | 6.68E-05 | 6.37E-05 | 0.0487 |
| glc←prtc | 9.04E-06 | 8.34E-06 | 0.0839 |
| prtc←glc | 3.14E-05 | 3.44E-05 | −0.0872 |
| prtc←prtc | 5.93E-05 | 5.27E-05 | 0.1247 |
| prtc←prtl | 5.83E-05 | 5.23E-05 | 0.1147 |
| prtl←prtc | 5.75E-05 | 5.25E-05 | 0.0952 |
| Po-213 | |||
| glc←glc | 6.52E-05 | 6.27E-05 | 0.039112 |
| glc←prt | 7.85E-06 | 7.33E-06 | 0.07116 |
| prtc←glc | 2.99E-05 | 3.40E-05 | −0.12178 |
| prtc←prtc | 5.38E-05 | 5.15E-05 | 0.043949 |
| prtc←prtl | 5.72E-05 | 5.11E-05 | 0.119007 |
| prtl←prtc | 5.58E-05 | 5.12E-05 | 0.090645 |
Abbreviations: glc: glomerular cells; prtc: proximal tubule cells; prtl: proximal tubule lumen
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The RDs were up to 12.46% for self-absorption, but a little higher (17%) for cross-absorption. Self-absorption may be defined as the absorption of radiation when the source and target are the same. The self-absorbed dose that commonly provides the largest fractional contribution to the total absorbed dose in a target region refers to the case when the source and target regions are identical. The cross-absorbed dose refers to the case when the source and target regions are different. Pearson's correlation analysis confirmed a good linear correlation (R2 = 0.99) for the self-absorption and cross-absorption between two data series (R2 = 0.96). Figure 3(a) illustrates the linear curve that has been fitted to the scatter plot of the S-values. A high linear correlation (R2 = 0.991) can be seen between the two series of data. The slope of the fitted linear curve was + 0.95 higher than unity. Figure 3(b) includes the scatter plot of the RD between two series of data. This Figure demonstrates the Bland-Altman plot to reveal the RD versus reference values (Hobbs et al. S-values). The average value of the RDs, i.e., the bias, demonstrates a systematic difference in the MC simulation. As the plot shows, 95% of the data points were inside the limits of agreement (average of RD ± 1.96 × standard deviation of RD) and the statistical difference is acceptable (≈ 5%). There was a high bias (5.27%) between the two sets of data.
The variations of the self-absorption and cross-absorption SAF values according to electron energy are presented in Figure 4. As the graph shows, for the glomeruli as the source, the calculated SAF values derived using the stylized and digital phantoms were in good agreement. The maximum difference of the self-absorption in the glomeruli was -4.78% for the 1-keV electrons. Similar curves for the glomeruli as the source and the proximal tubules as the target (cross-absorption) are also shown in Figure 4. The RDs between the results obtained by using the digital and stylized phantoms for electrons were up to 9.54%. Although a greater difference was observed in the case of cross-absorption as compared to self-absorption, the trends of variation were similar for the two phantoms. The RD for the electrons in the range of 1 to 10 keV was a little higher than that for other energies for both the self-absorption and cross-absorption.
Figures 5 illustrates the Bland-Altman plots for the self-absorption and cross-absorption SAF values for the electron source within the energy range of 1 keV to 70 keV. The graph shows the RD between the results derived using the digital and stylized phantoms as a function of the energy of the electrons. The plots illustrate the agreement between two data series. The bias between the two data series (the average of RD%) was -0.65%, showing an almost perfect agreement. The plot reveals that the SAF values derived using the digital phantom were 1% smaller than the corresponding values derived using the stylized phantom. The limit of agreement (average ±1.98 times the standard deviation) is normally used for evaluation of the statistical variation between the two data series. As can be seen, 95% of the data points were inside the limits of agreement. For electrons with energy < 30 keV, the RDs were high (between 4% to 10%).
An obstacle in MC simulations at the microscopic level is the lack of high-resolution phantoms. The phantoms that are currently in use are stylized phantoms that simplify the geometries. This model only includes the glomeruli and proximal tubules in simple shapes. This is an important problem because a small variation in target and source volumes may lead to a considerable difference in the results of microscale simulations [20],[23]. Moreover, it is not possible to make a stylized phantom at the desired resolution. Therefore, the phantom must be close to the anatomy of the object to be represented. In the present study, a simple method was developed to generate digital phantoms from tessellated 3D graphical images. The method is expected to be useful for investigating the absorbed dose in organs on the microscale. A digital phantom representing the detailed anatomical properties of cortical nephrons in human kidneys was modeled in this study.
One of the main advantages of using the digital phantom is the possibility to investigate the dose distribution in voxels that can be approximate values for the dose distribution among cells. It is not necessary to have voxels at the size of cells in the tissue. It is always possible to consider a combination of voxels to represent the average size of the cells. This can be different for different locations of phantoms. The dose distribution in the cells is an important index to predict the response of tissues to radiation.
Another advantage of using the digital phantom is the possibility to consider the non-uniform distribution of radiopharmaceuticals in source components. The nephron digital phantom can also take into account the possible differentiation of uptake between superficial and juxtamedullary nephrons [24].
At present, there is no experimental data available for the validation of the results derived using the digital phantom of a nephron. However, the consistency with the results derived using the stylized phantom may be regarded as a type of validation. The maximum difference was up to 20%, which is not acceptable when the uncertainty of simulation is around 5% (95% confidence interval).
The S-values calculated using the digital phantom were all smaller than the values reported in the study by Hobbs et al. This can be due to the different energy spectra used in the two studies. In the present study, alpha energy spectra were derived using the MIRD radionuclide data and decay scheme [25]. Hobbs et al. derived the decay schemes from another data center [26],[27]. The difference can also be due to small differences in the shapes of the nephron components [27]. The volumes of nephron components are equal, but it is not possible to equalize the shapes simultaneously. A considerable part of these differences is due to the simplification of nephron anatomy. Therefore, it is obvious that the deviation from the real anatomy of a nephron leads to considerable differences in the S-values and dose calculations.
To some extent, the difference between the results of two phantoms could just be due to statistical variation (uncertainty) in the MC simulation. When the energy of particles is low, most of the energy of particles is absorbed in the source components, and only a small fraction of particle energy is released in the target components. From the statistical point of view, smaller values indicate higher statistical uncertainty. However, this does not explain the smaller S-values derived by using the digital phantom compared to the stylized phantom.
Despite the advantage of digital phantoms, tracking particles in discrete environments is much slower than in continuous mediums. The main reason is the need for calculation of the material properties each time a particle enters a voxel. This becomes a significant problem when the number of voxels is large and the voxel size is small. This may be the reason why the digital phantoms are not so popular in microdosimetry. However, high-power computers can overcome this difficulty.
The digital phantom developed in this study permits the calculation of the dose in fine spatial resolution. As the results showed, the average dose to cells is considerably different from the maximum and minimum doses to the cells. Depending on the threshold dose for cell damage, the relative number of cells that receive a dose higher than the threshold can be an index for the protection of normal cells or damage of malignant cells.
A digital phantom of a nephron was modeled in this study. This phantom provides the possibility to measure dose at the cellular level, and it can be helpful in the estimation of the kidney response to radionuclide therapy. The phantom represents the real anatomy of a nephron in detail, and it can be used to assess the effect of inhomogeneous activity distribution in the absorbed dose of a nephron. So, a generated phantom can be useful for obtaining accurate absorbed dose estimations for different radiation protection situations, as well as for the more precise evaluation of biological effects. This study showed that, depending on the radionuclide spectra, the dose to the nephron may be overestimated or underestimated, and that this is something expected in general. It would be useful to quantify this difference in more detail. This may be regarded as a sample for the development of a more accurate high-resolution digital phantom. The phantom is freely available from the corresponding author of this paper.
| [1] |
Das S, Dawson NL, Orengo RA (2015) Diversity in protein domain superfamilies. Curr Opin Genet Dev 35: 40–49. doi: 10.1016/j.gde.2015.09.005
|
| [2] |
Giuroiu I, Weber J (2017) Novel checkpoints and cosignaling molecules in cancer immunotherapy. Cancer J 23: 23–31. doi: 10.1097/PPO.0000000000000241
|
| [3] |
Wei SC, Levine JH, Cogdill AP, et al. (2017) Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170: 1120–1133. doi: 10.1016/j.cell.2017.07.024
|
| [4] | Murmann AE, McMahon KM, Haluck-Kangas A, et al. (2017) Induction of DISE in ovarian cancer cells in vivo. Oncotarget 8: 84643–84658. |
| [5] |
Buchbinder E, Desai A (2016) CTLA-4 and PD-1 pathways: similarities, differences, and implications of their inhibition. Am J Clin Oncol 39: 98–106. doi: 10.1097/COC.0000000000000239
|
| [6] | Rotte A, Jin JY, Lemaire V (2017) Mechanistic overview of immune checkpoints to support the rational design of their combinations in cancer immunotherapy. Ann Oncol. |
| [7] |
Zelensky AN, Gready JE (2005) The C-type lectin-like domain superfamily. FEBS J 272: 6179–6217. doi: 10.1111/j.1742-4658.2005.05031.x
|
| [8] |
Geijtenbeek TB, Gringhuis SI (2009) Signaling through C-type lectin receptors: shaping immune responses. Nat Rev Immunol 9: 465–479. doi: 10.1038/nri2569
|
| [9] |
García-Vallejo JJ, Van KY (2009) Endogenous ligands for C-type lectin receptors: the true regulators of immune homeostasis. Immunol Rev 230: 22–37. doi: 10.1111/j.1600-065X.2009.00786.x
|
| [10] |
Van KY, Ilarregui JM, van Vliet SJ (2015) Novel insights into the immunomodulatory role of the dendritic cell and macrophage-expressed C-type lectin MGL. Immunobiology 220: 185–192. doi: 10.1016/j.imbio.2014.10.002
|
| [11] |
Sancho D, Reis SC (2013) Sensing of cell death by myeloid C-type lectin receptors. Curr Opin Immunol 25: 46–52. doi: 10.1016/j.coi.2012.12.007
|
| [12] |
Chang SY, Kweon MN (2010) Langerin-expressing dendritic cells in gut-associated lymphoid tissues. Immunol Rev 234: 233–246. doi: 10.1111/j.0105-2896.2009.00878.x
|
| [13] |
Ingeborg SO, Unger WWJ, Yvette VK (2011) C-type lectin receptors for tumor eradication: future directions. Cancers 3: 3169–3188. doi: 10.3390/cancers3033169
|
| [14] |
Zhang F, Ren S, Zuo Y (2014) DC-SIGN, DC-SIGNR and LSECtin: C-type lectins for infection. Int Rev Immunol 33: 54–66. doi: 10.3109/08830185.2013.834897
|
| [15] | Yan H, Kamiya T, Suabjakyong P, et al. (2015) Targeting C-type lectin receptors for cancer immunity. Front Immunol 6: 408–416. |
| [16] |
Dambuza IM, Brown GD (2015) C-type lectins in immunity: recent developments. Curr Opin Immunol 32: 21–27. doi: 10.1016/j.coi.2014.12.002
|
| [17] | Ding D, Yao Y, Zhang S, et al. (2017) C-type lectins facilitate tumor metastasis. Oncol Lett 13: 13–21. |
| [18] |
Andersen CBF, Moestrup SK (2014) How calcium makes endocytic receptors attractive. Trends Biochem Sci 39: 82–90. doi: 10.1016/j.tibs.2013.12.003
|
| [19] | Eggensperger A, Tampé R (2015) The transporter associated with antigen processing: a key player in adaptive immunity. Biol Chem 396: 1059–1072. |
| [20] |
Rodriguez A, Regnault A, Kleijmeer M, et al. (1999) Selective transport of internalized antigens to the cytosol for MHC class I presentation in dendritic cells. Nat Cell Biol 1: 362–368. doi: 10.1038/14058
|
| [21] |
Solheim JC (1999) Class I MHC molecules: assembly and antigen presentation. Immunol Rev 172: 11–19. doi: 10.1111/j.1600-065X.1999.tb01352.x
|
| [22] |
Gil-Torregrosa BC, Lennon-Duménil AM, Kessler B, et al. (2004) Control of cross-presentation during dendritic cell maturation. Eur J Immunol 34: 398–407. doi: 10.1002/eji.200324508
|
| [23] |
Heath WR, Carbone FR (2001) Cross-presentation in viral immunity and self-tolerance. Nat Rev Immunol 1: 126–135. doi: 10.1038/35100512
|
| [24] |
Joffre OP, Segura E, Savina A, et al. (2012) Cross-presentation by dendritic cells. Nat Rev Immunol 12: 557–569. doi: 10.1038/nri3254
|
| [25] | McDonnell AM, Robinson BWS, Currie AJ (2010) Tumor antigen cross-presentation and the dendritic cell: where it all begins? Clin Dev Immunol 2010: 539519–539527. |
| [26] | Bousso P, Robey E (2003) Dynamics of CD8+ T cell priming by dendritic cells in intact lymph nodes. Nat Immunol 4: 579–585. |
| [27] |
Randolph GJ, Jakubzick C, Qu C (2008) Antigen presentation by monocytes and monocyte-derived cells. Curr Opin Immunol 20: 52–60. doi: 10.1016/j.coi.2007.10.010
|
| [28] |
Leiri?o P, Fresno CD, Ardavín C (2012) Monocytes as effector cells: activated Ly-6C(high) mouse monocytes migrate to the lymph nodes through the lymph and cross-present antigens to CD8+ T cells. Eur J Immunol 42: 2042–2051. doi: 10.1002/eji.201142166
|
| [29] |
Raghavan M, Wijeyesakere SJ, Peters LR, et al. (2013) Calreticulin in the immune system: ins and outs. Trends Immunol 34: 13–21. doi: 10.1016/j.it.2012.08.002
|
| [30] |
Lv D, Shen Y, Peng Y, et al. (2015) Neuronal MHC class I expression is regulated by activity driven calcium signaling. PLoS One 10: e0135223–e0135238. doi: 10.1371/journal.pone.0135223
|
| [31] | Skov S (1999) Intracellular signal transduction mediated by ligation of MHC class I molecules. Tissue Antigens 51: 215–223. |
| [32] |
Blum JS, Wearsch PA, Cresswell P (2013) Pathways of antigen processing. Annu Rev Immunol 31: 443–473. doi: 10.1146/annurev-immunol-032712-095910
|
| [33] |
Roche PA, Furuta K (2015) The ins and outs of MHC class II-mediated antigen processing and presentation. Nat Rev Immunol 15: 203–216. doi: 10.1038/nri3818
|
| [34] |
Mueller SN (2017) Spreading the load: antigen transfer between migratory and lymph node-resident dendritic cells promotes T-cell priming. Eur J Immunol 47: 1798–1801. doi: 10.1002/eji.201747248
|
| [35] |
Parton RG, Joggerst B, Simons K (1994) Regulated internalization of caveolae. J Cell Biol 127: 1199–1215. doi: 10.1083/jcb.127.5.1199
|
| [36] |
Levine TP, Chain BM (1992) Endocytosis by antigen presenting cells: dendritic cells are as endocytically active as other antigen presenting cells. Proc Natl Acad Sci USA 89: 8342–8346. doi: 10.1073/pnas.89.17.8342
|
| [37] |
Hohn C, Lee SR, Pinchuk LM, et al. (2009) Zebrafish kidney phagocytes utilize macropinocytosis and Ca+-dependent endocytic mechanisms. PLoS One 4: e4314–e4323. doi: 10.1371/journal.pone.0004314
|
| [38] |
Calmette J, Bertrand M, Vétillard M, et al. (2016) Glucocorticoid-induced leucine zipper protein controls macropinocytosis in dendritic cells. J Immunol 197: 4247–4256. doi: 10.4049/jimmunol.1600561
|
| [39] |
Ayroldi E, Riccardi C (2009) Glucocorticoid-induced leucine zipper (GILZ): a new important mediator of glucocorticoid action. FASEB J 23: 3649–3658. doi: 10.1096/fj.09-134684
|
| [40] | Ronchetti S, Migliorati G, Riccardi C (2015) GILZ as a mediator of the anti-inflammatory effects of glucocorticoids. Front Endocrinol 6: 170–175. |
| [41] |
Canton J, Schlam D, Breuer C, et al. (2016) Calcium-sensing receptors signal constitutive macropinocytosis and facilitate the uptake of NOD2 ligands in macrophages. Nat Commun 7: 11284–11295. doi: 10.1038/ncomms11284
|
| [42] | Redka DS, Gütschow M, Grinstein S, et al. (2017) Differential ability of pro-inflammatory and anti-inflammatory macrophages to perform macropinocytosis. Mol Biol Cell pii: mbc.E17-06-0419. |
| [43] | Carafoli E, Krebs J (2016) Why calcium? How calcium became the best communicator. J Biol Chem 291: 20849–20857. |
| [44] |
Caroppo R, Gerbino A, Fistetto G, et al. (2004) Extracellular calcium acts as a "third messenger" to regulate enzyme and alkaline secretion. J Cell Biol 166: 111–119. doi: 10.1083/jcb.200310145
|
| [45] | Drickamer K (1988) Two distinct classes of carbohydrate-recognition domains in animal lectins. J Biol Chem 263: 9557–9560. |
| [46] |
Drickamer K (1996) Ca(2+)-dependent sugar recognition by animal lectins. Biochem Soc T 24: 146–150. doi: 10.1042/bst0240146
|
| [47] |
Weis WI, Taylor ME, Drickamer K (1998) The C-type lectin superfamily in the immune system. Immunol Rev 163: 19–34. doi: 10.1111/j.1600-065X.1998.tb01185.x
|
| [48] |
Drickamer K, Taylor ME (2015) Recent insights into structures and functions of C-type lectins in the immune system. Curr Opin Struc Biol 34: 26–34. doi: 10.1016/j.sbi.2015.06.003
|
| [49] |
van Vliet SJ, Saeland E, Van KY (2008) Sweet preferences of MGL: carbohydrate specificity and function. Trends Immunol 29: 83–90. doi: 10.1016/j.it.2007.10.010
|
| [50] |
Van DD, Stolk DA, Van RVD, et al. (2017) Targeting C-type lectin receptors: a high-carbohydrate diet for dendritic cells to improve cancer vaccines. J Leukocyte Biol 102: 1017–1034. doi: 10.1189/jlb.5MR0217-059RR
|
| [51] |
Napoletano C, Zizzari IG, Rughetti A, et al. (2012) Targeting of macrophage galactose-type C-type lectin (MGL) induces DC signaling and activation. Eur J Immunol 42: 936–945. doi: 10.1002/eji.201142086
|
| [52] |
Engering A, Geijtenbeck TBH, van Vliet SJ, et al. (2002) The dendritic cell-specific adhesion receptor DC-SIGN internalizes antigen for presentation to T cells. J Immunol 168: 2118–2126. doi: 10.4049/jimmunol.168.5.2118
|
| [53] | Database of human proteins containing CTLDs. Available from: http://www.imperial.ac.uk/research/animallectins/ctld/mammals/humandata%20updated.html. |
| [54] | Cummings RD, McEver RP (2017) Chapter 34: C-Type lectins, In: Varki A, Cummings RD, Esko JD, et al., editors. Essentials of Glycobiology 3rd Ed. Cold Spring Harbor Laboratory Press, 2015–2017. |
| [55] |
Sancho D, Reis SC (2012) Signaling by myeloid C-type lectin receptors in immunity and homeostasis. Annu Rev Immunol 30: 491–529. doi: 10.1146/annurev-immunol-031210-101352
|
| [56] |
Billadeau DD, Leibson PJ (2002) ITAMs versus ITIMs: striking a balance during cell regulation. J Clin Invest 109: 161–168. doi: 10.1172/JCI0214843
|
| [57] |
Ivashkiv LB (2009) Cross-regulation of signaling by ITAM-associated receptors. Nat Immunol 10: 340–347. doi: 10.1038/ni.1706
|
| [58] |
Bezbradica JS, Rosenstein RK, DeMarco RA, et al. (2014) A role for the ITAM signaling module in specifying cytokine-receptor functions. Nat Immunol 15: 333–342. doi: 10.1038/ni.2845
|
| [59] |
Pollitt AY, Poulter NS, Gitz E, et al. (2014) Syk and Src family kinases regulate C-type lectin receptor 2 (CLEC-2)-mediated clustering of podoplanin and platelet adhesion to lymphatic endothelial cells. J Biol Chem 289: 35695–35710. doi: 10.1074/jbc.M114.584284
|
| [60] |
Unkeless JC, Jin J (1997) Inhibitory receptors, ITIM sequences and phosphatases. Curr Opin Immunol 9: 338–343. doi: 10.1016/S0952-7915(97)80079-9
|
| [61] |
van Vliet SJ, Aarnoudse CA, Vc BDB, et al. (2007) MGL-mediated internalization and antigen presentation by dendritic cells: a role for tyrosine-5. Eur J Immunol 37: 2075–2081. doi: 10.1002/eji.200636838
|
| [62] | Harris RL, Cw VDB, Bowen DJ (2012) ASGR1 and ASGR2, the genes that encode the asialoglycoprotein receptor (Ashwell Receptor), are expressed in peripheral blood monocytes and show inter-individual differences in transcript profile. Mol Biol Int 2012: 283974–283983. |
| [63] |
East L, Isacke CM (2002) The mannose receptor family. BBA-Gen Subjects 1572: 364–386. doi: 10.1016/S0304-4165(02)00319-7
|
| [64] | Uniport. Available from: http://www.uniprot.org/uniprot/P22897. |
| [65] |
Lo YL, Liou GG, Lyu JH, et al. (2016) Dengue virus infection is through a cooperative interaction between a mannose receptor and CLEC5A on macrophage as a multivalent hetero-complex. PLoS One 11: e0166474–e0166486. doi: 10.1371/journal.pone.0166474
|
| [66] | R?dgaard-Hansen S, Rafique A, Christensen PA, et al. (2014) A soluble form of the macrophage-related mannose receptor (MR/CD206) is present in human serum and elevated in critical illness. Clin Chem Lab Med 52: 453–461. |
| [67] |
Feinberg H, Park-Snyder S, Kolatkar AR, et al. (2000) Structure of a C-type carbohydrate recognition domain from the macrophage mannose receptor. J Biol Chem 275: 21539–21548. doi: 10.1074/jbc.M002366200
|
| [68] |
Ng KKS, Park-Snyder S, Weis WI (1998) Ca2+-dependent structural changes in C-type mannose-binding proteins. Biochemistry 37: 17965–17976. doi: 10.1021/bi981972a
|
| [69] | Iobst ST, Wormald MR, Weis WI, et al. (1994) Binding of sugar ligands to Ca(2+)-dependent animal lectins. I. Analysis of mannose binding by site-directed mutagenesis and NMR. J Biol Chem 269: 15505–15511. |
| [70] |
Weis WI, Drickamer K, Hendrickson WA (1992) Structure of a C-type mannose-binding protein complexed with an oligosaccharide. Nature 360: 127–134. doi: 10.1038/360127a0
|
| [71] |
Drickamer K (1992) Engineering galactose-binding activity into a C-type mannose-binding protein. Nature 360: 183–186. doi: 10.1038/360183a0
|
| [72] |
Apostolopoulos V, Pietersz GA, Loveland, BE, et al. (1995) Oxidative/reductive conjugation of mannan to antigen selects for T1 or T2 immune responses. Proc Natl Acad Sci USA 92: 10128–10132. doi: 10.1073/pnas.92.22.10128
|
| [73] |
Apostolopoulos V, Pietersz GA, Gordon S, et al. (2000) Aldehyde-mannan antigen complexes target the MHC class I antigen-presentation pathway. Eur J Immunol 30: 1714–1723. doi: 10.1002/1521-4141(200006)30:6<1714::AID-IMMU1714>3.0.CO;2-C
|
| [74] |
Apostolopoulos V, Pietersz GA, Tsibanis A, et al. (2014) Dendritic cell immunotherapy: clinical outcomes. Clin Transl Immunol 3: e21–e24. doi: 10.1038/cti.2014.14
|
| [75] |
Steinman RM, Turley S, Mellman I, et al. (2000) The induction of tolerance by dendritic cells that have captured apoptotic cells. J Exp Med 191: 411–416. doi: 10.1084/jem.191.3.411
|
| [76] |
Steinman RM, Hawiger D, Liu K, et al. (2003) Dendritic cell function in vivo during the steady state: a role in peripheral tolerance. Ann Ny Acad Sci 987: 15–25. doi: 10.1111/j.1749-6632.2003.tb06029.x
|
| [77] |
Redmond WL, Sherman LA (2005) Peripheral tolerance of CD8 T lymphocytes. Immunity 22: 275–284. doi: 10.1016/j.immuni.2005.01.010
|
| [78] |
Chieppa M, Bianchi G, Doni A, et al. (2003) Cross-linking of the mannose receptor on monocyte-derived dendritic cells activates an anti-inflammatory immunosuppressive program. J Immunol 171: 4552–4560. doi: 10.4049/jimmunol.171.9.4552
|
| [79] | Allavena P, Chieppa M, Blanchi G, et al. (2010) Engagement of the mannose receptor by tumoral mucins activates an immune suppressive phenotype in human tumor-associated macrophages. Clin Dev Immunol 2010: 547179–547188. |
| [80] |
Sharpe AH (2009) Mechanisms of costimulation. Immunol Rev 229: 5–11. doi: 10.1111/j.1600-065X.2009.00784.x
|
| [81] |
Chen L, Flies DB (2013) Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat Rev Immunol 13: 227–242. doi: 10.1038/nri3405
|
| [82] |
Anderson PJ, Kokame K, Sadler JE (2006) Zinc and calcium ions cooperatively modulate ADAMTS13 activity. J Biol Chem 281: 850–857. doi: 10.1074/jbc.M504540200
|
| [83] | Sorvillo N, Pos W, Lm VDB, et al. (2017) The macrophage mannose receptor promotes uptake of ADAMTS13 by dendritic cells. Blood 119: 3828–3835. |
| [84] |
Mahnke K, Guo M, Lee S, et al. (2000) The dendritic cell receptor for endocytosis, DEC-205, can recycle and enhance antigen presentation via major histocompatibility complex class II-positive lysosomal compartments. J Cell Biol 151: 673–683. doi: 10.1083/jcb.151.3.673
|
| [85] |
Platt CD, Ma JK, Chalouni C, et al. (2010) Mature dendritic cells use endocytic receptors to capture and present antigens. Proc Nat Acad Sci USA 107: 4287–4292. doi: 10.1073/pnas.0910609107
|
| [86] |
Tel J, Benitez-Ribas D, Hoosemans S, et al. (2011) DEC-205 mediates antigen uptake and presentation by both resting and activated human plasmacytoid dendritic cells. Eur J Immunol 41: 1014–1023. doi: 10.1002/eji.201040790
|
| [87] |
Hawiger D, Inaba K, Dorsett Y, et al. (2001) Dendritic cells induce peripheral T cell unresponsiveness under steady state conditions in vivo. J Exp Med 194: 769–779. doi: 10.1084/jem.194.6.769
|
| [88] |
Ma DY, Clark EA (2009) The role of CD40 and CD154/CD40L in dendritic cells. Semin Immunol 21: 265–272. doi: 10.1016/j.smim.2009.05.010
|
| [89] |
Lee GH, Askari A, Malietzis G, et al. (2014) The role of CD40 expression in dendritic cell in cancer biology: a systematic review. Curr Cancer Drug Tar 14: 610–620. doi: 10.2174/1568009614666140828103253
|
| [90] |
Sartorius R, D'Apice L, Trovato M, et al. (2015) Antigen delivery by filamentous bacteriophage fd displaying an anti-DEC-205 single-chain variable fragment confers adjuvanticity by triggering a TLR9-mediated immune response. Embo Mol Med 7: 973–988. doi: 10.15252/emmm.201404525
|
| [91] |
Melander MC, Jürgensen HJ, Madsen DH, et al. (2015) The collagen receptor uPARAP/Endo180 in tissue degradation and cancer (Review). Int J Oncol 47: 1177–1188. doi: 10.3892/ijo.2015.3120
|
| [92] |
Sturge J (2016) Endo180 at the cutting edge of bone cancer treatment and beyond. J Pathol 238: 485–488. doi: 10.1002/path.4673
|
| [93] |
Yuan C, Jürgensen HJ, Engelholm LH, et al. (2016) Crystal structures of the ligand-binding region of uPARAP: effect of calcium ion binding. Biochem J 473: 2359–2368. doi: 10.1042/BCJ20160276
|
| [94] |
East L, Rushton S, Taylor ME, et al. (2002) Characterization of sugar binding by the mannose receptor family member, Endo180. J Biol Chem 277: 50469–50475. doi: 10.1074/jbc.M208985200
|
| [95] |
Augert A, Payré C, de Launoit Y, et al. (2009) The M-type receptor PLA2R regulates senescence through the p53 pathway. Embo Rep 10: 271–277. doi: 10.1038/embor.2008.255
|
| [96] |
Jr BLH, Bonegio RG, Lambeau G, et al. (2009) M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. New Engl J Med 361: 11–21. doi: 10.1056/NEJMoa0810457
|
| [97] |
Takahashi S, Watanabe K, Watanabe Y, et al. (2015) C-type lectin-like domain and fibronectin-like type II domain of phospholipase A2 receptor 1 modulate binding and migratory responses to collagen. Febs Lett 589: 829–835. doi: 10.1016/j.febslet.2015.02.016
|
| [98] |
Nolin JD, Ogden HL, Lai Y, et al. (2016) Identification of epithelial phospholipase A2 receptor 1 as a potential target in asthma. Am J Resp Cell Mol 55: 825–836. doi: 10.1165/rcmb.2015-0150OC
|
| [99] |
Fresquet M, Jowitt TA, McKenzie EA, et al. (2017) PLA2R binds to the annexin A2-S100A10 complex in human podocytes. Sci Rep 7: 6876–6886. doi: 10.1038/s41598-017-07028-8
|
| [100] |
Santamaria-Kisiel L, Rintala-Dempsey A, Shaw GS (2006) Calcium-dependent and -independent interactions of the S100 protein family. Biochem J 396: 201–214. doi: 10.1042/BJ20060195
|
| [101] |
Goder V, Spiess M (2001) Topogenesis of membrane proteins: determinants and dynamics. Febs Lett 504: 87–93. doi: 10.1016/S0014-5793(01)02712-0
|
| [102] |
Zimmerman R, Eyrisch S, Ahmad M, et al. (2011) Protein translocation across the ER membrane. BBA-Biomembranes 1808: 912–924. doi: 10.1016/j.bbamem.2010.06.015
|
| [103] |
Feinberg H, Mitchell DA, Drickamer K, et al. (2001) Structural basis for selective recognition of oligosaccharides by DC-SIGN and DC-SIGNR. Science 294: 2163–2166. doi: 10.1126/science.1066371
|
| [104] | Mitchell DA, Fadden AJ, Drickamer K (2001) A novel mechanism of carbohydrate recognition by the C-type lectins DC-SIGN and DC-SIGNR. Subunit organization and binding to multivalent ligands. J Biol Chem 276: 28939–28945. |
| [105] |
Guo Y, Feinberg H, Conroy E, et al. (2004) Structural basis for distinct ligand-binding and targeting properties of the receptors DC-SIGN and DC-SIGNR. Nat Struct Mol Biol 11: 591–598. doi: 10.1038/nsmb784
|
| [106] |
Caparrós E, Munoz P, Sierra-Filardi E, et al. (2006) DC-SIGN ligation on dendritic cells results in ERK and PI3K activation and modulates cytokine production. Blood 107: 3950–3958. doi: 10.1182/blood-2005-03-1252
|
| [107] |
Iyori M, Ohtani M, Hasebe A, et al. (2008) A role of the Ca2+ binding site of DC-SIGN in the phagocytosis of E. coli. Biochem Bioph Res Co 377: 367–372. doi: 10.1016/j.bbrc.2008.09.142
|
| [108] |
Dos Santos á, Hadjivasiliou A, Ossa F, et al. (2017) Oligomerization domains in the glycan-binding receptors DC-SIGN and DC-SIGNR: sequence variation and stability differences. Protein Sci 26: 306–316. doi: 10.1002/pro.3083
|
| [109] |
Dodagatta-Marri E, Mitchell DA, Pandit H, et al. (2017) Protein-protein interaction between surfactant protein D and DC-SIGN via C-type lectin domain can suppress HIV-1 transfer. Front Immunol 8: 834–845. doi: 10.3389/fimmu.2017.00834
|
| [110] |
Chao PZ, Hsieh MS, Cheng CW, et al. (2015) Dendritic cells respond to nasopharygeal carcinoma cells through annexin A2-recognizing DC-SIGN. Oncotarget 6: 159–170. doi: 10.18632/oncotarget.2700
|
| [111] |
Chia J, Goh G, Bard F (2016) Short O-GalNAc glycans: regulation and role in tumor development and clinical perspectives. BBA-Gen Subjects 1860: 1623–1639. doi: 10.1016/j.bbagen.2016.03.008
|
| [112] |
Zheng J, Xiao H, Wu R (2017) Specific identification of glycoproteins bearing the Tn antigen in human cells. Angew Chem 129: 7213–7217. doi: 10.1002/ange.201702191
|
| [113] |
Feinberg H, Torgersen D, Drickamer K, et al. (2000) Mechanism of pH-dependent N-acetylgalactosamine binding by a functional mimic of the hepatocyte asialoglycoprotein receptor. J Biol Chem 275: 35176–35184. doi: 10.1074/jbc.M005557200
|
| [114] | Morell AG, Gregoriadis G, Scheinberg IH, et al. (1971) The role of sialic acid in determining the survival of glycoproteins in the circulation. J Biol Chem 246: 1461–1467. |
| [115] |
Grewal PK (2010) The Ashwell-Morell receptor. Method Enzymol 479: 223–241. doi: 10.1016/S0076-6879(10)79013-3
|
| [116] |
Weigel PH, Yik JHN (2002) Glycans as endocytosis signals: the cases of the asialoprotein and hyaluronan/chrondroitin sulfate receptors. BBA-Gen Subjects 1572: 341–363. doi: 10.1016/S0304-4165(02)00318-5
|
| [117] | Dixon LJ, Barnes M, Tang H, et al. (2013) Kupffer Cells in the Liver. Compr Physiol 3: 785–797. |
| [118] |
Tsuiji M, Fujimori M, Ohashi Y, et al. (2002) Molecular cloning and characterization of a novel mouse macrophage C-type lectin, mMGL2, which has a distinct carbohydrate specificity from mMGL1. J Biol Chem 277: 28892–28901. doi: 10.1074/jbc.M203774200
|
| [119] |
Kolatkar AR, Weis WI (1996) Structural basis of galactose recognition by C-type animal lectins. J Biol Chem 271: 6679–6685. doi: 10.1074/jbc.271.12.6679
|
| [120] |
Meier M, Bider MD, Malashkevich VN, et al. (2000) Crystal structure of the carbohydrate recognition domain of the H1 subunit of the asialoglycoprotein receptor. J Mol Biol 300: 857–865. doi: 10.1006/jmbi.2000.3853
|
| [121] |
Higashi N, Fujioka K, Denda-Nagai K, et al. (2002) The macrophage C-type lectin specific for galactose/N-acetylgalactosamine is an endocytic receptor expressed on monocyte-derived immature dendritic cells. J Biol Chem 277: 20686–20693. doi: 10.1074/jbc.M202104200
|
| [122] |
Lundberg K, Rydnert F, Broos S, et al. (2016) C-type lectin receptor expression on human basophils and effects of allergen-specific immunotherapy. Scand J Immunol 84: 150–157. doi: 10.1111/sji.12457
|
| [123] |
Vukman KV, Ravidà A, Aldridge AM, et al. (2013) Mannose receptor and macrophage galactose-type lectin are involved in Bordetella pertussis mast cell interaction. J Leukocyte Biol 94: 439–448. doi: 10.1189/jlb.0313130
|
| [124] |
Savola P, Kelkka T, Rajala HL, et al. (2017) Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis. Nat Commun 8: 15869–15882. doi: 10.1038/ncomms15869
|
| [125] | Gaur P, Myles A, Misra R, et al. (2016) Intermediate monocytes are increased in enthesitis-related arthritis, a category of juvenile idiopathic arthritis. Clin Exp Immunol 187: 234–241. |
| [126] |
Vlismas A, Bletsa R, Mavrogianni D, et al. (2016) Microarray analyses reveal marked differences in growth factor and receptor expression between 8-cell human embryos and pluripotent stem cells. Stem Cells Dev 25: 160–177. doi: 10.1089/scd.2015.0284
|
| [127] |
Klimmeck D, Hanssong J, Raffel S, et al. (2012) Proteomic cornerstones of hematopoietic stem cell differentiation: distinct signatures of multipotent progenitors and myeloid committed cells. Molec Cell Proteomics 11: 286–302. doi: 10.1074/mcp.M111.016790
|
| [128] |
Winkler C, Witte L, Moraw N, et al. (2014) Impact of endobronchial allergen provocation on macrophage phenotype in asthmatics. BMC Immunol 15: 12–22. doi: 10.1186/1471-2172-15-12
|
| [129] |
Mathews JA, Kasahara DI, Ribeiro L, et al. (2015) γδ T cells are required for M2 macrophage polarization and resolution of ozone-induced pulmonary inflammation in mice. PLoS One 10: e0131236–e0131251. doi: 10.1371/journal.pone.0131236
|
| [130] |
Shin H, Kumamoto Y, Gopinath S, et al. (2016) CD301b+ dendritic cells stimulate tissue-resident memory CD8+ T cells to protect against genital HSV-2. Nat Commun 7: 13346–13355. doi: 10.1038/ncomms13346
|
| [131] |
Linehan JL, Dileepan T, Kashem SW, et al. (2015) Generation of Th17 cells in response to intranasal infection requires TGF-β1 from dendritic cells and IL-6 from CD301b+ dendritic cells. Proc Natl Acad Sci USA 112: 12782–12787. 132. Wong KL, Tai JJ, Wong WC, et al. (2011) Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets. Blood 118: e16–e31. doi: 10.1182/blood-2010-12-326355
|
| [132] |
133. Wong KL, Yeap WH, Tai JJY, et al. (2012) The three human monocyte subsets: implications for health and disease. Immunol Res 53: 41–57. doi: 10.1007/s12026-012-8297-3
|
| [133] |
134. Michlmayr D, Andrade P, Gonzalez K, et al. (2017) CD14+CD16+ monocytes are the main target of Zika virus infection in peripheral blood mononuclear cells in a paediatric study in Nicaragua. Nat Microbiol 2: 1462–1470. doi: 10.1038/s41564-017-0035-0
|
| [134] |
135. Knudsen NH, Lee CH (2016) Identity crisis: CD301b(+) mononuclear phagocytes blur the M1-M2 macrophage line. Immunity 45: 461–463. doi: 10.1016/j.immuni.2016.09.004
|
| [135] |
136. Zhang W, Xu W, Xiong S (2011) Macrophage differentiation and polarization via phosphatidylinositol 3-kinase/Akt-ERK signaling pathway conferred by serum amyloid P component. J Immunol 187: 1764–1777. doi: 10.4049/jimmunol.1002315
|
| [136] |
137. Trowbridge IS, Thomas M (1994) CD45: an emerging role as a protein tyrosine phosphatase required for lymphocyte activation and development. Annu Rev Immunol 12: 85–116. doi: 10.1146/annurev.iy.12.040194.000505
|
| [137] | 138. van Vliet SJ, Gringhuis SI, Geijtenbeek TBH, et al. (2006) Regulation of effector T cells by antigen-presenting cells via interaction with the C-type lectin MGL with CD45. Nat Immunol 11: 1200–1208. |
| [138] |
139. Nam HJ, Poy F, Saito H, et al. (2005) Structural basis for the function and regulation of the receptor protein tyrosine phosphatase CD45. J Exp Med 201: 441–452. doi: 10.1084/jem.20041890
|
| [139] |
140. Xu Z, Weiss A (2002) Negative regulation of CD45 by differential homodimerization of the alternatively spliced isoforms. Nat Immunol 3: 764–771. doi: 10.1038/ni822
|
| [140] |
141. Kumar V, Cheng P, Condamine T, et al. (2016) CD45 phosphatase inhibits STAT3 transcription factor activity in myeloid cells and promotes tumor-associated macrophage differentiation. Immunity 44: 303–315. doi: 10.1016/j.immuni.2016.01.014
|
| [141] |
142. van Vliet SJ, van Liempt E, Geijtenbeek TB, et al. (2006) Differential regulation of C-type lectin expression on tolerogenic dendritic cell subsets. Immunobiology 211: 577–585. doi: 10.1016/j.imbio.2006.05.022
|
| [142] |
143. Marcelo F, Garcia-Martin F, Matsushita T, et al. (2014) Delineating binding modes of Gal/GalNAc and structural elements of the molecular recognition of tumor-associated mucin glycopeptides by the human macrophage galactose-type lectin. Chem Eur J 20: 16147–16155. doi: 10.1002/chem.201404566
|
| [143] |
144. Tanaka J, Gleinich AS, Zhang Q, et al. (2017) Specific and differential binding of N-acetylgalactosamine glycopolymers to the human macrophage galactose lectin and asialoglycoprotein receptor. Biomacromolecules 18: 1624–1633. doi: 10.1021/acs.biomac.7b00228
|
| [144] |
145. Khorev O, Stokmaier D, Schwardt O, et al. (2008) Trivalent, Gal/GaNAc-containing ligands designed for the asialoglycoprotein receptor. Bioorg Med Chem 16: 5216–5231. doi: 10.1016/j.bmc.2008.03.017
|
| [145] |
146. Nair JK, Willoughby JLS, Chan A, et al. (2014) Multivalent N-acetylgalactosamine-conjugated siRNA localizes in hepatocytes and elicits robust RNAi-mediated gene silencing. J Am Chem Soc 136: 16958–16961. doi: 10.1021/ja505986a
|
| [146] | 147. Lo-Man R, Bay S, Vichier-Guerre S, et al. (1999) A fully synthetic immunogen carrying a carcinoma-associated carbohydrate for active specific immunotherapy. Cancer Res 59: 1520–1524. |
| [147] |
148. Lo-Man R, Vichier-Guerre S, Bay S, et al. (2001) Anti-tumor immunity provided by a synthetic multiple antigenic glycopeptide displaying a tri-Tn glycotope. J Immunol 166: 2849–2854. doi: 10.4049/jimmunol.166.4.2849
|
| [148] |
149. Morgan AJ, Platt FM, Lloyd-Evans E, et al. (2011) Molecular mechanisms of endolysosomal Ca2+ signaling in health and disease. Biochem J 439: 349–374. doi: 10.1042/BJ20110949
|
| [149] |
150. Wragg S, Drickamer K (1999) Identification of amino acid residues that determine pH dependence of ligand binding to the asialoglyprotein receptor during endocytosis. J Biol Chem 274: 35400–35406. doi: 10.1074/jbc.274.50.35400
|
| [150] |
151. Onizuka T, Shimizu H, Moriwaki Y, et al. (2012) NMR study of ligand release from asialoglycoprotein receptor under solution conditions in early endosomes. Febs J 279: 2645–2656. doi: 10.1111/j.1742-4658.2012.08643.x
|
| [151] |
152. Gerasimenko JV, Tepikin AV, Petersen OH, et al. (1998) Calcium uptake via endocytosis with rapid release from acidifying endosomes. Curr Biol 8: 1335–1338. doi: 10.1016/S0960-9822(07)00565-9
|
| [152] |
153. Plattner H, Verkhratsky A (2016) Inseparable tandem: evolution chooses ATP and Ca2+ to control life, death and cellular signaling. Philos T R Soc B 371: 20150419–20150433. doi: 10.1098/rstb.2015.0419
|
| [153] |
154. Napoletano C, Rughetti A, Tarp MPA, et al. (2007) Tumor-associated Tn-MUC1 glycoform is internalized through the macrophage galactose-type C-type lectin and delivered to the HLA class I and II compartments in dendritic cells. Cancer Res 67: 8358–8367. doi: 10.1158/0008-5472.CAN-07-1035
|
| [154] |
155. Hiltbold EM, Vlad AM, Ciborowski P, et al. (2000) The mechanism of unresponsiveness to circulating tumor antigen MUC1 is a block in intracellular sorting and processing by dendritic cells. J Immunol 165: 3730–3741. doi: 10.4049/jimmunol.165.7.3730
|
| [155] |
156. Hanisch FG, Schwientek T, Von BergweltBaildon MS, (2003) O-Linked glycans control glycoprotein processing by antigen-presenting cells: a biochemical approach to the molecular aspects of MUC1 processing by dendritic cells. Eur J Immunol 33: 3242–3254. doi: 10.1002/eji.200324189
|
| [156] |
157. Freire T, Zhang X, Dériaud E, et al. (2010) Glycosidic Tn-based vaccines targeting dermal dendritic cells favor germinal center B-cell development and potent antibody response in the absence of adjuvant. Blood 116: 3526–3536. doi: 10.1182/blood-2010-04-279133
|
| [157] |
158. Freire T, Lo-Man R, Bay S, et al. (2011) Tn glycosylation of the MUC6 protein modulates its immunogenicity and promotes the induction of the Th17-biased T cell responses. J Biol Chem 286: 7797–7811. doi: 10.1074/jbc.M110.209742
|
| [158] |
159. Li D, Romain G, Flamar AL, et al. (2012) Targeting self- and foreign antigens to dendritic cells via DC-ASGPR generate IL-10-producing suppressive CD4+ T cells. J Exp Med 209: 109–121. doi: 10.1084/jem.20110399
|
| [159] |
160. Valladeau J, Duvert-Frances V, Pin JJ, et al. (2001) Immature human dendritic cells express asialoglycoprotein receptor isoforms for efficient receptor-mediated endocytosis. J Immunol 167: 5767–5774. doi: 10.4049/jimmunol.167.10.5767
|
| [160] | 161. Garg S, Oran A, Wajchman J, et al. (2003) Genetic tagging shows increased frequency and longevity of antigen-presenting, skin-derived dendritic cells in vivo. Nat Immunol 4: 907–912. |
| [161] | 162. Tomura M, Hata A, Matsuoka S, et al. (2014) Tracking and quantification of dendritic cell migration and antigen trafficking between the skin and lymph nodes. Sci Rep 4: 6030–6040. |
| [162] |
163. Kitano M, Yamazaki C, Takumi A, et al. (2016) Imaging of the cross-presenting dendritic cell subsets in the skin-draining lymph node. Proc Natl Acad Sci USA 113: 1044–1049. doi: 10.1073/pnas.1513607113
|
| [163] |
164. Wan YY, Flavell RA (2009) How diverse-CD4 effector T cells and their functions. J Mol Cell Biol 1: 20–36. doi: 10.1093/jmcb/mjp001
|
| [164] |
165. Lanzavecchia A (1985) Antigen-specific interaction between T and B cells. Nature 314: 537–539. doi: 10.1038/314537a0
|
| [165] |
166. Shumilina E, Huber SM, Lang F (2011) Ca2+ signaling in the regulation of dendritic cell functions. Am J Physiol Cell Ph 300: C1205–C1214. doi: 10.1152/ajpcell.00039.2011
|
| [166] |
167. Cowen DS, Lazarus HM, Shurin SB, et al. (1989) Extracellular adenosine triphosphate activates calcium mobilization in human phagocytic leukocytes and neutrophil/monocyte progenitor cells. J Clin Invest 83: 1651–. doi: 10.1172/JCI114064
|
| [167] | 168. Bretou M, Sáez PJ, Sanséau D, et al. (2017) Lysosome signaling controls the migration of dendritic cells. Sci Immunol 2: In press. |
| [168] |
169. Vukcevic M, Zorzato F, Spagnoli G, et al. (2010) Frequent calcium oscillations lead to NFAT activation in human immature dendritic cells. J Biol Chem 285: 16003–16011. doi: 10.1074/jbc.M109.066704
|
| [169] |
170. Jégouzo SAF, Quintero-Martinez, Ouyang X, et al. (2013) Organization of the extracellular portion of the macrophage galactose receptor: a trimeric cluster of simple binding sites for N-acetylgalactosamine. Glycobiology 23: 853–864. doi: 10.1093/glycob/cwt022
|
| [170] | 171. Humeau J, Bravo-San PJ, Vitale I, et al. (2017) Calcium signaling and cell cycle: progression or death. Cell Calcium 17: In press. |
| [171] |
172. Nicotera P, Orrenius S (1998) The role of calcium in apoptosis. Cell Calcium 23: 173–180. doi: 10.1016/S0143-4160(98)90116-6
|
| [172] | 173. Schwarz EC, Qu B, Hoth M (013) Calcium, cancer and killing: the role of calcium in killing cancer cells by cytotoxic T lymphocytes and natural killing cells. BBA-Mol Cell Res 1833: 1603–1611. |
| [173] |
174. Cui C, Merritt R, Fu L, et al. (2017) Targeting calcium signaling in cancer therapy. Acta Pharm Sinica B 7: 3–17. doi: 10.1016/j.apsb.2016.11.001
|
| [174] |
175. Halling DB, Liebeskind BJ, Hall AW, et al. (2016) Conserved properties of individual Ca2+-binding sites in calmodulin. Proc Nat Acad Sci USA 113: E1216–E1225. doi: 10.1073/pnas.1600385113
|
| [175] |
176. Agrawal RS, Connolly SF, Herrmann TL, et al. (2007) MHC class II levels and intracellular localization in human dendritic cells are regulated by calmodulin kinase II. J Leukocyte Biol 82: 686–699. doi: 10.1189/jlb.0107045
|
| [176] |
177. Connolly SF, Kusner DJ (2007) The regulation of dendritic cell function by calcium-signaling and its inhibition by microbial pathogens. Immunol Res 39: 115–127. doi: 10.1007/s12026-007-0076-1
|
| [177] |
178. Shi Y (2009) Serine/threonine phosphatases: mechanism through structure. Cell 139: 468–484. doi: 10.1016/j.cell.2009.10.006
|
| [178] | 179. Song MS, Salmena L, Pandolfi PP (2012) The functions and regulation of the PTEN tumor suppressor. Nat Rev Mol Cell Bio 13: 283–296. |
| [179] |
180. Cho US, Xu W (2007) Crystal structure of a protein phosphatase 2A heterotrimeric holoenzyme. Nature 445: 53–57. doi: 10.1038/nature05351
|
| [180] |
181. Feske S (2007) Calcium signaling in lymphocyte activation and disease. Nat Rev Immunol 7: 690–702. doi: 10.1038/nri2152
|
| [181] |
182. Müller MR, Rao A (2010) NFAT, immunity and cancer: a transcription factor comes of age. Nat Rev Immunol 10: 645–656. doi: 10.1038/nri2818
|
| [182] |
183. Nakanishi A, Hatano N, Fujiwara Y, et al. (2017) AMP-activated protein-kinase-mediated feedback phosphorylation controls the Ca2+/calmodulin dependence of Ca2+/CaM-dependent protein kinase kinase β. J Biol Chem 292: 19804–19813. doi: 10.1074/jbc.M117.805085
|
| [183] |
184. Gaertner TR, Kolodziej SJ, Wang D, et al. (2004) Comparative analysis of the three-dimensional structures and enzymatic properties of α, β, γ, and δ isoforms of Ca2+-calmodulin-dependent protein kinase II. J Biol Chem 279: 12484–12494. doi: 10.1074/jbc.M313597200
|
| [184] |
185. Wiede F, Dudakov JA, Lu KH, et al. (2017) PTPN2 regulates T cell lineage commitment and αβ versus γδ specification. J Exp Med 214: 2733–2758. doi: 10.1084/jem.20161903
|
| [185] |
186. Wang X, Marks CR, Perfitt TL, et al. (2017) A novel mechanism for Ca2+/calmodulin-dependent protein kinase II targeting to L-type Ca2+ channels that initiates long-range signaling to the nucleus. J Biol Chem 292: 17324–17336. doi: 10.1074/jbc.M117.788331
|
| [186] |
187. Lin MY, Zal T, Ch'En IL, et al. (2005) A pivotal role for the multifunctional calcium/calmodulin-dependent protein kinase II in T cells: from activation to unresponsiveness. J Immunol 174: 5583–5592. doi: 10.4049/jimmunol.174.9.5583
|
| [187] | 188. Ratner AJ, Bryan R, Weber A, et al. (2017) Cystic fibrosis pathogens activate Ca2+-dependent mitogen-activated protein kinase signaling pathways in airway epithelial cells. J Biol Chem 276: 19267–19275. |
| [188] | 189. Bononi A, Agnoletto C, De ME, et al. (2011) Protein kinases and phosphatases in the control of cell fate. Enz Res 2011: 329098–329113. |
| [189] |
190. Suzuki K, Hata S, Kawabata Y, et al. (2004) Structure, activation, and biology of calpain. Diabetes 53: S12–S18. doi: 10.2337/diabetes.53.2007.S12
|
| [190] | 191. Frangioni JV, Oda A, Smith M, et al. (1993) Calpain-catalyzed cleavage and subcellular relocation of protein phosphotyrosine phosphatase 1B (PTP-1B) in human platelets. EMBO J 12: 4843–4856. |
| [191] | 192. Baba Y, Kurosaki T (2016) Role of calcium signaling in B cell activation and biology. Curr Top Microbiol 393: 143–147. |
| [192] |
193. Vaeth M, Zee I, Concepcion AR, et al. (2015) Ca2+ signaling but not store-operated Ca2+ entry is required for the function of macrophages and dendritic cells. J Immunol 195: 1202–1217. doi: 10.4049/jimmunol.1403013
|
| [193] |
194. Ledderose C, Bao Y, Lidicky M, et al. (2014) Mitochondria are gate-keepers of T cell function by producing the ATP that drives purinergic signaling. J Biol Chem 289: 25936–25945. doi: 10.1074/jbc.M114.575308
|
| [194] | 195. Zizzari IG, Napoletano C, Battisti F, et al. (2015) MGL receptor and immunity: when the ligand can make the difference. J Immunol Res 2015: 450695–450702. |
| [195] |
196. van Vliet SJ, Bay S, Vuist IM, et al. (2013) MGL signaling augments TLR2-mediated responses for enhanced IL-10 and TNF-α secretion. J Leukocyte Biol 94: 315–323. doi: 10.1189/jlb.1012520
|
| [196] |
197. Nunes P, Demaurex N (2010) The role of calcium signaling in phagocytosis. J Leukocyte Biol 88: 57–68. doi: 10.1189/jlb.0110028
|
| [197] |
198. Lm VDB, Gringhuis SI, Geijtenbeek TB (2012) An evolutionary perspective on C-type lectins in infection and immunity. Ann Ny Acad Sci 1253: 149–158. doi: 10.1111/j.1749-6632.2011.06392.x
|
| [198] | 199. Kushchayev SV, Sankar T, Eggink LL, et al. (2012) Monocyte galactose/N-acetylgalactosamine-specific C-type lectin receptor stimulant immunotherapy of an experimental glioma. Part 1: stimulatory effects on blood monocytes and monocyte-derived cells of the brain. Cancer Manag Res 4: 309–323. |
| [199] | 200. Kushchayev SV, Sankar T, Eggink LL, et al. (2012) Monocyte galactose/N-acetylgalactosamine-specific C-type lectin receptor stimulant immunotherapy of an experimental glioma. Part II: combination with external radiation improves survival. Cancer Manag Res 4: 325–334. |
| [200] | 201. Roby KF, Eggink LL, Hoober JK (2017) An innovative immunotherapeutic strategy for ovarian cancer: glycomimetic peptides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017, Washington DC. Available from: http://cancerres.aacrjournals.org/content/77/13_Supplement/170. |
Figures(3)
Robert Cote, Laura Lynn Eggink, J. Kenneth Hoober. CLEC receptors, endocytosis and calcium signaling[J]. AIMS Allergy and Immunology, 2017, 1(4): 207-231. doi: 10.3934/Allergy.2017.4.207
| Radionuclide | Simulation using digital phantom (Gy/Bq·s) | Hobbs et al. results (Gy/Bq·s) | RD |
| Ac-225 | |||
| glc←glc | 5.90E-05 | 5.70E-05 | 0.0351 |
| glc←prt | 5.99E-06 | 5.39E-06 | 0.1113 |
| prtc←glc | 2.80E-04 | 3.02E-04 | −0.0722 |
| prtc←prtc | 4.94E-04 | 4.54E-04 | 0.0881 |
| prtc←prtl | 4.89E-05 | 4.49E-05 | 0.0891 |
| prtl←prtc | 4.92E-05 | 4.52E-05 | 0.0885 |
| Bi-213 | |||
| glc←glc | 7.58E-05 | 7.28E-05 | 0.041602 |
| glc←prt | 1.59E-05 | 1.36E-05 | 0.168108 |
| prtc←glc | 3.62E-05 | 4.22E-05 | −0.14215 |
| prtc←prtc | 6.81E-05 | 6.47E-05 | 0.052553 |
| prtc←prtl | 7.35E-05 | 6.45E-05 | 0.139032 |
| prtl←prtc | 7.56E-05 | 6.46E-05 | 0.170664 |
| Fr-221 | |||
| glc←glc | 1.31E-04 | 1.26E-04 | 0.039112 |
| glc←prt | 1.67E-05 | 1.57E-05 | 0.0656 |
| prtc←glc | 6.10E-05 | 6.91E-05 | −0.11781 |
| prtc←glc | 6.10E-05 | 6.91E-05 | −0.11781 |
| prtc←prtc | 1.09E-04 | 1.04E-04 | 0.045949 |
| prtc←prtl | 1.19E-04 | 1.03E-04 | 0.159007 |
| prtl←prtc | 1.12E-04 | 1.04E-04 | 0.076645 |
| At-217 | |||
| glc←glc | 6.68E-05 | 6.37E-05 | 0.0487 |
| glc←prtc | 9.04E-06 | 8.34E-06 | 0.0839 |
| prtc←glc | 3.14E-05 | 3.44E-05 | −0.0872 |
| prtc←prtc | 5.93E-05 | 5.27E-05 | 0.1247 |
| prtc←prtl | 5.83E-05 | 5.23E-05 | 0.1147 |
| prtl←prtc | 5.75E-05 | 5.25E-05 | 0.0952 |
| Po-213 | |||
| glc←glc | 6.52E-05 | 6.27E-05 | 0.039112 |
| glc←prt | 7.85E-06 | 7.33E-06 | 0.07116 |
| prtc←glc | 2.99E-05 | 3.40E-05 | −0.12178 |
| prtc←prtc | 5.38E-05 | 5.15E-05 | 0.043949 |
| prtc←prtl | 5.72E-05 | 5.11E-05 | 0.119007 |
| prtl←prtc | 5.58E-05 | 5.12E-05 | 0.090645 |
Abbreviations: glc: glomerular cells; prtc: proximal tubule cells; prtl: proximal tubule lumen
DownLoad:
CSV
| Radionuclide | Simulation using digital phantom (Gy/Bq·s) | Hobbs et al. results (Gy/Bq·s) | RD |
| Ac-225 | |||
| glc←glc | 5.90E-05 | 5.70E-05 | 0.0351 |
| glc←prt | 5.99E-06 | 5.39E-06 | 0.1113 |
| prtc←glc | 2.80E-04 | 3.02E-04 | −0.0722 |
| prtc←prtc | 4.94E-04 | 4.54E-04 | 0.0881 |
| prtc←prtl | 4.89E-05 | 4.49E-05 | 0.0891 |
| prtl←prtc | 4.92E-05 | 4.52E-05 | 0.0885 |
| Bi-213 | |||
| glc←glc | 7.58E-05 | 7.28E-05 | 0.041602 |
| glc←prt | 1.59E-05 | 1.36E-05 | 0.168108 |
| prtc←glc | 3.62E-05 | 4.22E-05 | −0.14215 |
| prtc←prtc | 6.81E-05 | 6.47E-05 | 0.052553 |
| prtc←prtl | 7.35E-05 | 6.45E-05 | 0.139032 |
| prtl←prtc | 7.56E-05 | 6.46E-05 | 0.170664 |
| Fr-221 | |||
| glc←glc | 1.31E-04 | 1.26E-04 | 0.039112 |
| glc←prt | 1.67E-05 | 1.57E-05 | 0.0656 |
| prtc←glc | 6.10E-05 | 6.91E-05 | −0.11781 |
| prtc←glc | 6.10E-05 | 6.91E-05 | −0.11781 |
| prtc←prtc | 1.09E-04 | 1.04E-04 | 0.045949 |
| prtc←prtl | 1.19E-04 | 1.03E-04 | 0.159007 |
| prtl←prtc | 1.12E-04 | 1.04E-04 | 0.076645 |
| At-217 | |||
| glc←glc | 6.68E-05 | 6.37E-05 | 0.0487 |
| glc←prtc | 9.04E-06 | 8.34E-06 | 0.0839 |
| prtc←glc | 3.14E-05 | 3.44E-05 | −0.0872 |
| prtc←prtc | 5.93E-05 | 5.27E-05 | 0.1247 |
| prtc←prtl | 5.83E-05 | 5.23E-05 | 0.1147 |
| prtl←prtc | 5.75E-05 | 5.25E-05 | 0.0952 |
| Po-213 | |||
| glc←glc | 6.52E-05 | 6.27E-05 | 0.039112 |
| glc←prt | 7.85E-06 | 7.33E-06 | 0.07116 |
| prtc←glc | 2.99E-05 | 3.40E-05 | −0.12178 |
| prtc←prtc | 5.38E-05 | 5.15E-05 | 0.043949 |
| prtc←prtl | 5.72E-05 | 5.11E-05 | 0.119007 |
| prtl←prtc | 5.58E-05 | 5.12E-05 | 0.090645 |
