Following excited-state chemical shifts in molecular ultrafast x-ray photoelectron spectroscopy

The conversion of photon energy into other energetic forms in molecules is accompanied by charge moving on ultrafast timescales. We directly observe the charge motion at a specific site in an electronically excited molecule using time-resolved x-ray photoelectron spectroscopy (TR-XPS). We extend the concept of static chemical shift from conventional XPS by the excited-state chemical shift (ESCS), which is connected to the charge in the framework of a potential model. This allows us to invert TR-XPS spectra to the dynamic charge at a specific atom. We demonstrate the power of TR-XPS by using sulphur 2p-core-electron-emission probing to study the UV-excited dynamics of 2-thiouracil. The method allows us to discover that a major part of the population relaxes to the molecular ground state within 220–250 fs. In addition, a 250-fs oscillation, visible in the kinetic energy of the TR-XPS, reveals a coherent exchange of population among electronic states.

Backbone chemical shift assignment and dynamics of the N-terminal domain of ClpB from Francisella tularensis type VI secretion system

The Hsp100 family member ClpB is a protein disaggregase which solubilizes and reactivates stress-induced protein aggregates in cooperation with the DnaK/Hsp70 chaperone system. In the pathogenic bacterium Francisella tularensis, ClpB is involved in type VI secretion system (T6SS) disassembly through depolymerization of the IglA-IglB sheath. This leads to recycling and reassembly of T6SS components and this process is essential for the virulence of the bacterium. Here we report the backbone chemical shift assignments and 15N relaxation-based backbone dynamics of the N-terminal substrate-binding domain of ClpB (1-156).

Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures

PurposeOsteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures.Materials and MethodsTwenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables.ResultsPatients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p<0.001). For the model with integral vBMD as the dependent variable, T2* combined with three PDFF-based texture features explained 40% of the variance (adjusted R2[Ra2] = 0.40; p<0.001). Furthermore, regarding the differentiation between patients with and without osteoporotic vertebral fractures, a model including texture features from CT and CSE-MRI data showed better performance than a model based on integral vBMD and PDFF only (Ra2 = 0.47 vs. Ra2 = 0.81; included texture features in the final model: integral vBMD, CT_Short-run_emphasis, CT_Varianceglobal, and PDFF_Variance).ConclusionUsing texture analysis for spine CT and CSE-MRI can facilitate the differentiation between patients with and without osteoporotic vertebral fractures, implicating that future fracture prediction in osteoporosis may be improved.

Structure determination of protein-peptide complexes from NMR chemical shift data using MELD.

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding upon binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine structures of peptide-protein complexes. Here, we demonstrate this approach for polypeptide com-plexes formed with the extraterminal (ET) domain of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can form different conformations (e.g., helices and hair-pins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding pos-es, using only backbone chemical shift data, in excellent agreement with experimentally-determined structures. The approach also predicts relative binding affinities of different peptides. This hybrid MELD-NMR approach provides a powerful new tool for structural analysis of protein-polypeptide complexes in the low NMR information content regime, which can be used successfully for flexible systems where one polypeptide binding partner folds upon complex formation.

Determination of chemical shifts in 6-condensed syringylic lignin model compounds

A small library of 6-substituted syringyl model compounds with aliphatic, carboxylic, phenylic, benzylic alcohols and brominated substituents were prepared. The influence of the substituents on the chemical shifts of the compounds was analyzed. All of model compounds showed a characteristic increase in the 13C NMR chemical shift of the methoxy group vicinal to the substitution. This 13C NMR peak and its corresponding correlation peak in HSQC could potentially be used to identify 6-condensation in syringylic lignin samples.

Could we assess the functional status, of hormone secreting, or non-secreting of the adrenal masses regarding their Magnetic Resonance Imaging (MRI) characteristics?

Background: Diagnostic imaging techniques including magnetic resonance imaging (MRI) should also perform on all patients with incidentalomas. However, there is a limited study whether the quantitative measurements (signal intensity index, adrenal to spleen ratio) in MRI could predict the functional status of adrenal adenomas. Material-Method: Between 2015-2020; 404 patients (265 females, 139 males) with adrenal mass who were referred to the university hospital for further investigation were included. After detailed diagnostic hormonal evaluation, all patients underwent MRI 1.5 T device (Signa, GE Medical Systems; Milwaukee, USA). The signal intensities of the adrenal lesions on T2W images were qualitatively evaluated and noted as homogenous or heterogeneous in comparison with the liver signal intensity (SI). A chemical-shift SI index and chemical shift adrenal-to-spleen SI ratio were also calculated. Results: While 331(81.9%) of the patients had nonfunctional adrenal mass, the rest of them (n=73, 18.1%) were patients with functional (autonomous cortisol secretion-ACS, cushing syndrome-CS, pheochromocytoma, primary hyperaldosteronism-PA) adrenal masses. In phase vs phase values of patients with NFAI, Pheo(n=17), ACS (n=30), CS (n=11), and PA (n=15) were 474.04±126.7 vs 226.6±132.4, 495.3±182.8 vs 282.17±189.1, 445.2±134.8 vs 203.3±76.2, 506.8±126.5 vs 212.2±73.6 and 496.2±147.5 vs 246.6±102.1, respectively. Mean signal intensity index (SII) and adrenal to spleen ratio (ASR) of all groups (NFAI, Pheo, ACS, CS, PA) were 52.0±24.8 and 0.51, 44.9±22.5 and 0.55, 49.5±24.5 and 0.53, 56.2±16.4 and 0.43, 47.6±25.1 and 0.54, respectively. Based the current accepted measurements in the case of ASR and SII, all lesions were similar and shown as fat rich adenomas (p*= 0.552, p** = 0.45). Conclusion: The quantitative assessment (SII, ASR) of intracellular lipids in an incidentally discovered adrenal tumour could only help distinguish adrenal masses in case of adenomas or non-adenomas As initial diagnostic evaluation, clinical and laboratory assessment ,to distinguish hormone secretion, should be taken in all patients with adrenal incidentalomas.

Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences

The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, n = 19), heterogeneous (Group 2, n = 23), and no (Group 3, n = 30) signal drop. Histopathology or follow-up data served as reference standard to classify AL. ROIs were drawn both on CS and DCE images to obtain adrenal CS signal intensity index (ASII), absolute (AWO), and relative washout (RWO) values. Quantitative parameters (QP) were compared with ANOVA analysis and post hoc Dunn’s test. The performance of QP to classify AL was assessed with receiver operating characteristic analysis. CS ASII values were significantly different among the three groups (p < 0.001) with median values of 71%, 53%, and 3%, respectively. AWO/RWO values were similar in Groups 1 (adenomas) and 2 (benign AL) but significantly (p < 0.001) lower in Group 3 (20 benign AL and 10 malignant AL). With cut-offs, respectively, of 60% (Group 1 vs. 2), 20% (Group 2 vs. 3), and 37% (Group 1 vs. 3), CS ASII showed areas under the curve of 0.85, 0.96, and 0.93 for the classification of AL, overall higher than AWO/RWO. In conclusion, AL with qualitative heterogeneous signal drop at CS represent benign AL with QP by DCE sequence similar to those of AL with homogeneous signal drop at CS, but different to those of AL with no signal drop at CS; ASII seems to be the only quantitative parameter able to differentiate AL among the three different groups.