, 2003). Concomitantly with the loss of mitochondrial membrane potential observed in our study, the mitochondrial permeability transition induction by ROS releases several factors relevant to apoptosis, such as cytochrome c, apoptosis inducing factor (AIF) and endonuclease G (EndoGIA) ( Cai and Jones, 1998 and van Gurp et al., 2003). In previous studies from our laboratory, it was

also demonstrated that G8 and G12 decrease the reduced/oxidized glutathione ratio ( Locatelli et al., 2008 and Locatelli et al., 2009). Changes in the GSH level and in the redox state of mitochondria are associated selleck inhibitor with oxidative stress induced by various oxidizing agents ( Brodie and Reed, 1992 and Mckernan et al., 1991). At the cellular level, the gallic acid esters, are hydrolyzed enzymatically by cytoplasmic esterases to gallic acid and alcohols (Kubo et al., 2002 and Nakagawa et al., 1995). Studies on the carcinogenicity of propyl gallate in human leukemia cell line suggest that its hydrolysis product, gallic acid, plays an important role in this effect since it is more easily oxidized than propyl gallate, resulting in redox activity enhancement, and consequently in increasing the reactive oxygen species production (Kobayashi et al., 2004). On the other hand, when rat hepatocytes were incubated with the

esterase inhibitor diazinon the cytotoxic effects of propyl gallate was enhanced suggesting Selleckchem Vorinostat that the hepatotoxicity induced by propyl gallate was not mediated by its metabolites (Nakagawa et al., 1995). In our study, gallic acid did not alter cell viability, mitochondrial potential nor cellular redox status in mouse melanoma B16F10 cells suggesting that unlike the experiment with propyl gallate mentioned above, these effects do not depend on gallic acid formation by esterases

Ureohydrolase hydrolysis of G8 and G12. In conclusion, to increase the reliability of our results, we used more than one assay to determine the effects of G8 and G12 on the viability of B16F10 cells. G8 and G12 induced lysosomal damage and a significant LDH release in lower concentrations than those necessary to obtain the same effect on mitochondria. The interaction of the compounds with the plasma membrane probably triggered the cascade of cell death. Additionally, it has been shown evidences that, at least in particular conditions, the release into the cytosol of lysosomal constituents may initiate the events related to apoptosis. The triggering of the apoptotic cascade may involve early release of lysosomal constituents leading to an increase in mitochondrial oxidant production, additional lysosomal rupture followed by mitochondrial cytochrome c release. The induced apoptotic cell death by G8 and G12 that was demonstrated by our previous studies was confirmed here by caspase-3 activation.

Though phase II enzymes catalyze the detoxification Selleckchem NVP-BKM120 of BPDE, some of the reactive electrophiles interact

covalently with DNA to form adducts that mark an early initiation event. Unrepaired/misrepaired adducts lead to mutation in genes involved in proliferation, growth, apoptosis and finally to a disease condition such as cancer [4]. Plant-derived natural compounds have been receiving increased attention as chemopreventives because of their low toxicity and high tolerability. The efficacy of polyphenols when administered before or after the carcinogen treatment has been established and shown to modulate carcinogen-induced incidence/multiplicity/latency period of tumor development [5]. Curcumin/turmeric has been shown to possess chemopreventive activity at both initiation and promotion stages of chemical-induced carcinogenesis ([6], [7], [8], [9] and [10]). Earlier studies have shown that dietary curcumin pre-treatment decreases the formation of B(a)P-derived DNA adducts in mouse tissues by inhibiting carcinogen-induced phase I enzymes and directly Dasatinib datasheet inducing

phase II enzymes [7]. Effects of turmeric/curcumin after exposure to carcinogens on the repair or disappearance of adducts, if any, are not known. Hence, in the present study, the post-treatment effect of curcumin on the disappearance of BPDE-DNA adducts in tissues of mice have been evaluated. Herein, we show that dietary curcumin treatment subsequent to B(a)P exposure enhances the disappearance of BPDE-DNA adducts. This could possibly be due to the curcumin-mediated enhancement of apoptosis of DNA adduct-containing PAK6 cells

and/or repair of DNA-adducts in mouse tissues. Benzo(a)pyrene [B(a)P] (purity ∼98%) and curcumin (purity ∼65-70%) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Antibodies for Bax, Bcl-2, cyclin D1, β-actin, anti-mouse horseradish peroxidase (HRP) conjugated secondary antibodies were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA) and caspase-3 from Abcam (Cambridge, MA, USA). Monoclonal antibody for BPDE-DNA adduct clone 5D11 was obtained from Hycult Biotechnology (Uden, Netherlands). The monoclonal antibody for proliferating cell nuclear antigen (PCNA) was procured from BD Pharmingen (San Diego, CA, USA). The anti-rabbit HRP conjugated secondary antibodies were obtained from Amersham Biosciences (Buckinghamshire, UK). All animal studies were conducted with approval from the Institutional Animal Ethics Committee endorsed by the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Government of India guidelines.

No new methodology has shown clear superiority over the others in terms of reliability and validity. All methodologies have

features that limit their application in specific situations [20]. For example, when selleck chemicals holistic methodologies are used for sensory characterization all samples should be simultaneously evaluated. This limits the number of samples that can be considered in a study and makes it difficult to compare results obtained in different sessions. One of the approaches that have been proposed to overcome this limitation is to consider reference samples as in Polarized Sensory Positioning (PSP) [30]. In PSP samples are used as poles and are included in all the evaluations, which enables comparing results obtained in different sessions or the evaluation of split sample sets. This approach can be combined with other methodologies for sensory characterization to stabilize sample configurations Rapamycin clinical trial obtained in different sessions, as it has been done with projective mapping and flash profile 31 and 32. Another relevant issue that deserves further exploration is the influence of training on results from new methodologies. Although, some studies have reported that including short tasks for familiarizing naïve assessors with the methodology or the sample set improve the quality of results

from new methodologies 33 and 34•, the influence of short training sessions on results from new methodologies has not been largely studied. This type of research can shed

light on the need to familiarize assessors with the methodologies or the sample set, particularly when dealing with complex products. The use of new methodologies for sensory characterization will likely continue its steady growth. Further research on the applicability, reliability, and reproducibility of new approaches for sensory characterization is still strongly needed, as well as recommendations on how to implement them, analyze data and interpret results. In this sense, understanding the cognitive processes involved in sample evaluation and analyzing large number of sensory characterization studies with different new methodologies on products with different complexity can provide valuable insights and largely contribute to the development of a rapidly evolving field. Papers of particular interest, published within the period of Mannose-binding protein-associated serine protease review, have been highlighted as: • of special interest Research conducted in the author’s laboratory was supported by Comisión Sectorial de Investigación Científica (Universidad de la República, Uruguay) and Agencia Nacional de Investigación e Innovación. Ana Giménez, Leticia Vidal, Lucía Antúnez and Luis de Saldamando are thanked for all their work and support. “
“Current Opinion in Food Science 2015, 3:xx–yy This review comes from a themed issue on Sensory science and consumer perception Edited by Paula A Varela-Tomasco http://dx.doi.org/10.1016/j.cofs.2014.08.

15 and 19 This fact could explain the positivity for the protein in the odontoblasts of ameloblastic fibro-odontoma. In the presented study, there

was no immunoreaction against podoplanin antibody in orthokeratinized odontogenic cysts (OOCs), except when the epithelium was associated with inflammatory infiltrate. This intriguing finding was also observed in radicular12 and dentigerous cysts,6 and 8 and in human inflamed gingiva20 previously. It suggests that podoplanin expression is required when morphologic changes such as regeneration, reparative or even neoplastic process occur. In addition, high podoplanin expression is found in myoepithelial cells of breast glands21 and salivary glands,21, 22 and 23 both cells with elevated demand for cytoskeletal activity. As discussed above, U0126 mw the expression

of podoplanin has not been restricted to neoplastic odontogenic tissues but to physiological and reactive processes either. In normal odontogenic tissues, positivity for the protein was found in areas of high demand for proliferative activity, i.e. dental lamina 15 and 19 and terminal Tofacitinib chemical structure portion of Hertwig sheath 15 and 19 of murine tooth and basal layer of radicular cyst. 12 Recently, Okamoto et al.8 investigated whether podoplanin expression could be a useful parameter for reclassification of the odontogenic keratocyst from cyst to tumour status. The authors compared qualitatively the podoplanin expression in 46 keratocystic odontogenic tumours (KCOTS) and 11 orthokeratinized odontogenic cysts. They concluded that the podoplanin was higher in KCOTS than in OOCs, probably because KCOTS has more of a neoplastic character, with progression and local invasiveness. In view of the above findings, we designed this study to verify quantitatively the possible association between podoplanin expression and proliferative activity of epithelial odontogenic cells in keratocystic odontogenic tumour and its indolent counterpart, orthokeratinized odontogenic cyst.

Interestingly, a strong correlation was found between podoplanin expression and proliferative index of odontogenic cells (Table 2). In Non-specific serine/threonine protein kinase other words, the mitotic rate of epithelial odontogenic cells in KCOTS was statistically significant higher than in OOCs, reinforcing the previous findings of Okamoto et al.8 Moreover, Tsuneki et al. showed that podoplanin-positive cells are located in the cell proliferation centre because PCNA (proliferating cell nuclear antigen)-positive are also distributed in the periphery/basal zone of KCOTS cell nests and other benign odontogenic tumours.13 Once the overexpression of podoplanin can promote the formation of elongated cell extensions and increase adhesion and migration3 its expression may be required in the mitotic process. However, our results should be analysed carefully.

If the

angle in a bin is φ  , then the value α=φ−φ¯/σφ is computed, where φ¯ is the mean angle and σφ its standard deviation in all the bins located at the same depth as the bin considered. Only those angles within two standard deviations around the mean (i.e. |α| < 2) have been taken into account in the analyses. These values were quantised to four values corresponding to the four intervals [− 2, − 1], [− 1, 0], [0, 1] and [1, 2]. The procedures for the echogram loading and the computation of the Haralick variables were implemented in the Octave language and are available on the website http://www.kartenn.es/downloads. Energy-based acoustic classification. Based on the volume backscatter of the sound wave, a OSI-744 nmr classification of the data could be tested using the roughness and hardness acoustic indexes. These indexes are computed from the first and second acoustic bounces respectively, and have been introduced as seabed features (Orłowski 1982). The first echo energy (E1) is computed as the time integral of the received backscattered energy corresponding to the diffuse surface reflection (i.e. without the leading Selleck ATR inhibitor increasing power signal). The second echo energy (E2)

is computed as the time integral of the entire second bounce signal. Both energies are normalised by depth applying the correction + 20 log(R), where R is the range. This approach using two variables was introduced for seabed classification by Burns et al. (1989) and is currently used by the commercial system RoxAnn (Sonavision Limited, Aberdeen, UK). Multivariate statistical analysis. The multivariate statistical method used was based on Legendre et al. (2002) and Morris & Ball (2006) and includes dimensional mafosfamide reduction, principal component analysis (PCA)

and clustering analysis of the reduced variables. The original variables included in the analysis were the energy variables (E1, E2) and the alongship and athwartship Haralick variables, corresponding to Type 1 and Type 2 textural features. The matrix of Haralick textural features was centred and normalised and the PCA was applied (using singular value decomposition whenever more variables than samples were available) to obtain new uncorrelated variables (independent components). Only those components having eigenvalues larger than 1 were kept for the subsequent hierarchical cluster analysis (known as Kaiser’s rule). This choice removes noise from the analysis retaining only variables having higher variance than the original (normalised) ones. The clustering analysis of these selected principal component variables was performed using an agglomerative nested hierarchical algorithm to generate dendrograms; complete linkage and Euclidean distances were used. Finally, a stability analysis, based on Jaccard’s similarity values (J-values) was used to test the significance of these clusters, i.e.

Yet this model notably fails to explain intestinal plasticity where the reverse applies, that is, the acquisition of stem

cell ‘equivalence’ from phenotypically diverse cells. Again, advances in our understanding of mammalian neurogenesis indicate the potential LGK-974 clinical trial for a more dynamic regulation of these types of specification events than originally proposed that may help explain intestinal plasticity. In the mammalian nervous system, expression of the proneural bHLH transcription factors Ngn2 and Ascl1 oscillates with a periodicity of 2–3 hours in neural stem/progenitor cells. Oscillations are controlled by a transcriptional double negative feedback loop; the proneural transcription factors control expression of Delta-like ligands, activating Notch signalling and consequently resulting in delayed anti-phased expression of short-lived repressors (the Hes proteins) [26 and 27••]. Such Notch/Delta-mediated interactions ERK inhibitor between adjacent cells result in reciprocal Delta, bHLH and Hes oscillations where neighbours are out of synchrony and progenitor maintenance prevails [27•• and 28]. Cessation of oscillations of both

proneural and Hes proteins coincides with fate choice decisions, and results in sustained high expression of proneural proteins to drive differentiation, with reciprocal sustained low expression of Hes inhibitors. Indeed, in the nervous system stable, as opposed to oscillatory, bHLH expression seems to be absolutely required for cells to exit the cell cycle and adopt a differentiated fate [27••, 28 and 29]. As the essential players in fate decisions in the crypt are highly analogous to those in the nervous system, it seems likely that such oscillatory

expression of Y-27632 mw proneural and Hes proteins also occurs in the intestine. For instance, Atoh1 upregulates Delta expression and is itself repressed by Notch and Hes activity [5 and 9], so is well-placed to be part of a similar double negative feedback loop driving oscillatory expression as is seen for Hes1, Ngn2 and Ascl1 (Figure 4) [29 and 30]. Active Notch is required for Ascl2 expression but may also have contradictory effects as Hes1 has been described as suppressing Ascl2′s expression in epidermal cells [31]. Ascl2 can also be directly activated by Wnt and has a crucial role in maintaining stemness [8, 10 and 31]. Speculatively, oscillatory expression of Ascl2 may be required for this function, as is the case for Ascl1 and neural stem cell maintenance.

Images were analyzed for count density [photostimulated luminescence (PSL) per unit area] with a computerized image analysis program (AIDA Image Analyzer version 4.22; Raytest Isotopenmessgeräte, Straubenhardt, selleckchem Germany). A square-sized region of interest was drawn over each chamber containing cultured cells, and the background was subtracted from the image data. Data were normalized to the total

amount of radioactivity used in each experiment. Cells were cultured in six-well plates (Nunc) in a hypoxic workstation (Invivo2; Ruskinn Technology Ltd) under 1% O2 at 37°C for 1, 3, 6, 12, and 24 hours. Control cells were cultured in 21% O2 at 37°C (normoxia, 0 hour). Cells were harvested by adding 200 μl of sodium dodecyl sulfate (SDS)–Triton lysis buffer [50 mM Tris (pH 7.5),

150 mM NaCl, 0.5% Triton X-100 (TX-100), 5% glycerol, 1% SDS, and a complete protease inhibitor tablet]. Total cellular protein concentrations were determined using the bicinchoninic selleck chemical acid assay method (BCA Protein Assay Kit; Pierce™, Thermo Scientific, Waltham, MA, USA) before addition of SDS buffer. Equal amounts of protein were separated on a 10% SDS–polyacrylamide gel electrophoresis and blotted onto a polyvinylidene fluoride (PVDF) membrane (Bio-Rad Laboratories, Hercules, CA, USA). Proteins were detected by Western blot analysis and enhanced chemiluminescence with Hif-1α antibody (610959; BD Transduction Laboratories, 1:3000). β-Actin was used as a loading control (Ac-74; Sigma-Aldrich, St. Louis, MO, USA 1:3000). Descriptive statistics for the data are presented as arithmetic means and range. Mean levels of [18F]EF5 and [18F]FDG (tumor uptake), membranous CA IX, membranous Glut-1, and nuclear Hif-1α scores were compared between groups (UT-SCC-8, UT-SCC-34, and UT-SCC-74A) with one-way analysis of variance (ANOVA). Likewise, mean levels of [18F]EF5 (in vitro uptake) were compared between UT-SCC-8, UT-SCC-25, UT-SCC-34, and UT-SCC-74A. In addition, pairwise comparison between

groups was performed with 3-mercaptopyruvate sulfurtransferase Tukey test. Pearson correlation coefficient was calculated between [18F]FDG uptake and Hif-1α expression for each group separately. All tests were performed as two sided with a 0.05 significance level. In addition, P values less than .10 were reported as a trend toward significance. The analyses were done with SAS System (version 9.3 for Windows) (SAS Institute, Cary, NC, USA). Xenografts derived from UT-SCC-25 cells did not grow in nude mice. The uptake of [18F]EF5 and [18F]FDG in individual xenografts induced from UT-SCC-8, UT-SCC-34, and UT-SCC-74A cell lines is shown in Figure 1A. The uptake of both tracers reached equilibrium in tumors after 20 minutes ( Figure 1B). Representative PET images of [18F]EF5 and [18F]FDG uptake are shown in Figure 1C. The uptake of [18F]EF5 in UT-SCC-8 tumors was 0.87 (0.68-0.99) %ID/g and showed a trend toward a significantly lower (P = .

The area under the ROC curve (AUC) is also a very common performance metric in medical decision-making [12], bioinformatics [13] and statistical learning [14]. An important and often neglected step is the panel’s performance comparison against that of single biomarkers. A fair evaluation would process the panel and single biomarkers with the same tools (sensitivity and specificity or AUC) on the same independent test set or with the same CV procedure [1]. Then performance could Selleckchem PTC124 be compared either with McNemar’s test (for sensitivity or specificity)

or using ROC curves. The methods we propose here, which use single biomarker thresholds as the base of their decisions, are part of the PanelomiX software. In threshold-based combinations, thresholds are often chosen in a univariate manner. For example, Ranson et al. [4] selected convenient prognostic sign cut-off values outside the range of the mean plus or minus one standard deviation; Morrow and Braunwald [15] chose the 99th percentile Trichostatin A concentration of the control distribution; Sabatine et al. [16] used the cut-offs described in the literature. In contrast, Reynolds et al. [17] adopted a multivariate approach and tested many thresholds by 10% increments. This approach takes into account the interaction that may arise when biomarkers are combined. PanelomiX

can combine biomarkers (molecule levels, clinical scores, etc.) in a multivariate manner. Therefore we developed an exhaustive search algorithm to select the optimal thresholds, and called it iterative combination of biomarkers and thresholds (ICBT). To minimize

execution times, we developed several approaches to reduce Cyclic nucleotide phosphodiesterase complexity and hence increase search speed. As it has been shown to be an efficient feature selection method [11], we used random forest [18] and [19] as a filtering method to reduce both the number of biomarkers and thresholds that account for the search space size. Random forest builds a large number of decision trees that are made slightly different by bootstrapping. In the end, the classification is the average prediction of all trees. PanelomiX has already been applied to predict the outcome of an aneurysmal subarachnoid haemorrhage (aSAH) [20] and to assess the progression of human African trypanosomiasis [21]. Below, we demonstrate the PanelomiX methodology and performance, using 8 parameters for the determination of outcome for patients with an aSAH. The approach adopted here is based on the ICBT method. A threshold is defined for each biomarker by an optimization procedure defined in the following sections. A patient’s score is the number of biomarkers exceeding their threshold values. We can write this as: equation(1) Sp=∑i=1nI(Xip≥Ti)where Sp is the score for patient p, n is the number of biomarkers, Xip is the concentration of the ith biomarker in patient p, Ti is the threshold for the ith biomarker, and I(x) is an indicator function which takes the value of 1 for x = true and 0 otherwise.

2D) which presented mainly eosinophils and neutrophils ( Fig. 2E and F). The infiltrated area was predominantly submeningeal and distributed along vessels that penetrate the spinal cord tissue. There was associated edema and vascular congestion of meninges in both WT and PAFR−/− mice. We also removed

brainstem tissue from the same animals to measure N-acetyl-β-d-glucosaminidase (NAG) activity, an index of macrophage sequestration. EAE-induced WT animals present increased NAG activity (OD = 3.27 ± 0.26) when compared to controls (2.58 ± 0.07; p < 0.05) and EAE-induced PAFR−/− animals (OD = 2.26 ± 0.13; p < 0.001) ( Fig. 3). There was no difference between EAE-induced PAFR−/− and control PAFR−/− mice. To investigate whether Y-27632 chemical structure PAFR−/− mice presented altered rolling and adhesion of leukocytes in CNS microvasculature, we performed intravital microscopy in the cerebral microvasculature on day 14 post immunization. EAE-induced WT mice presented elevated levels (p < 0.001) of rolling ( Fig. 4A; cells/min, mean ± SE; 22.42 ± 3.31) and adhering ( Fig. 4B;

cells/100μm; 7.33±0.83) cells when compared to control mice (rolling: 0.83 ± 0.29; adhering: LBH589 chemical structure 0.89 ± 0.32). PAFR−/− mice also presented high levels (p < 0.001) of rolling (15.54 ± 2.49) and adhering (7.44 ± 0.71) leukocytes, similar to their WT counterparts but higher than PAFR−/− controls (rolling: 0.67 ± 0.14; adhering: 0.73 ± 0.12) ( Fig. 4). We measured cytokines and chemokines known to be involved in EAE. Cytokine IL-17 (pg/100 mg of tissue; mean ± SE; 175.60 ± 12.64) and chemokines CCL2 (128.40 ± 7.11) and CCL5 (882.40 ± 39.61) were elevated in EAE-induced WT mice after 14 days of immunization when compared to controls (IL-17: 117.40 ± 9.50; CCL2: 43.45 ± 4.37; CCL5: 479.40 ± 36.02; p < 0.05) ( Fig. 5) and PAFR−/− mice after 14 days of EAE induction (IL-17: 146.50 ± 5.08; CCL2: 49.99 ± 1.65; CCL5: 590.70 ± 17.66; p < 0.05). Also, there was no difference between EAE-induced PAFR−/− mice and PAFR−/− controls (IL-17: 157.00 ± 16.40; CCL2: 54.85 ± 3.79; CCL5: 632.90 ± 46.72).

We performed leukocytes isolation and staining to define which cells were infiltrating the CNS (Fig. 6). EAE-induced WT mice presented elevated levels of CD4+ stained cells (percentage of CD4+staining; median < range>: 1.71 < 0.41–10.99>) when compared to PAFR−/− (0.20 < 0.12–0.28>) mice after 14 days of induction (p < 0.01). There was also 4-Aminobutyrate aminotransferase a higher staining of cells synthesizing IL-17 (3.94 < 2.74–12.33>) in WT mice when compared to PAFR−/− animals (2.75 < 2.21–3.29>; p < 0.05). In this work, we showed that the absence of PAF receptor attenuates EAE. This better clinical outcome was associated with lower levels of cytokines and reduced mononuclear cell infiltration in the CNS. Interestingly, there was a change in the profile of the inflammatory infiltrate composed mainly of neutrophils and eosinophils, while no alteration in pivotal steps (rolling and adhesion) of cell recruitment was noticed.

A reduction of the intensity of the HN resonances of protein B upon irradiation of protein A identifies the region of B in contact with A ( Fig. 2). In this experiment protein A is unlabelled, while protein B is 2H, 15N labelled, such that the saturation transfer is specific for the protein–protein interaction interface. Another version of this experiment can be designed that detects the methyl groups of protein B while saturating the aromatic or aliphatic resonances of protein A, or even detect the saturation selleck kinase inhibitor transfer to the RNA aromatic protons upon saturation of protein side-chain resonances.

Dependent on the scheme of saturation and detection, the experiment can be performed either in D2O or in a mixture D2O/H2O to reduce dilution of the signal due to H2O mediated spin diffusion. Ku-0059436 We have applied this methodology to the ternary hPrp31 (human Prp31)–15.5K–U4

5′-SL (stem–loop) spliceosomal complex, which, due to its large size and instability, is not suitable for a complete structure determination by NMR [29]. We designed an experimental protocol where the protein–protein interaction surface is defined for 15.5 K by cross-saturation NMR data, while the relative orientation of the U4 RNA and the hPrp31 protein are described by mutational and cross-linking data. The decrease of the intensity of the HN resonances of 2D, 15N-labelled 15.5 K upon saturation of the methyl resonances of hPrp31 in the hPrp31–15.5K–U4 5′-SL complex was quantified and translated into distances. Using these data in a restrained ensemble docking protocol, we obtained a model for the ternary complex; comparison of the docking model with the crystal structure of a truncated version of the complex reveals that the docking model is accurate and reproduces all the features of the complex three-dimensional architecture Chlormezanone ( Fig.

2). Furthermore, the atomic details of the protein–protein interaction surface, both in terms of electrostatics and van der Waals contacts, also show excellent agreement to the crystal structure, demonstrating that good accuracy can be obtained at an atomic level even when using sparse and highly ambiguous NMR restraints. Once the mutual interaction surfaces have been defined by chemical shift mapping and cross-saturation experiments, the single components need to be placed in the correct mutual orientation. To this end, one can use residual dipolar couplings (RDCs) [30] measured for each component of the complex under the same alignment conditions. RDCs report on the orientation of internuclear vectors with respect to the magnetic field; therefore, if the structure of the single components is known, the data can be used to orient the components with respect to each other. In high-molecular weight RNP complexes 15N–HN and 13C–1H RDCs of amide and methyl groups [31], respectively, are likely to be available for proteins, while for the nucleic acid components 15N–H and 13C–1H RDCs are available at most for the aromatic rings.