Structural and Folding Dynamic Properties of the T70N Variant of Human Lysozyme

Definition of the transition mechanism from the native globular protein into fibrillar polymer was greatly improved by the biochemical and biophysical studies carried out on the two amyloidogenic variants of human lysozyme, I56T and D67H. Here we report thermodynamic and kinetic data on folding as well as structural features of a naturally occurring variant of human lysozyme, T70N, which is present in the British population at an allele frequency of 5% and, according to clinical and histopathological data, is not amyloidogenic. This variant is less stable than the wild-type protein by 3.7 kcal/mol, but more stable than the pathological, amyloidogenic variants. Unfolding kinetics in guanidine are six times faster than in the wild-type, but three and twenty times slower than in the amyloidogenic variants. Enzyme catalytic parameters, such as maximal velocity and affinity, are reduced in comparison to the wild-type. The solution structure, determined by 1H NMR and modeling calculations, exhibits a more compact arrangement at the interface between the beta-sheet domain and the subsequent loop on one side and part of the alpha domain on the other side, compared with the wild-type protein. This is the opposite of the conformational variation shown by the amyloidogenic variant D67H, but it accounts for the reduced stability and catalytic performance of T70N

Gender-related stress factors and emotional perception in migraine: a structured online questionnaire in migraine patients and controls

Background: While migraine is markedly prevalent in women, gender-related phenotype differences were rarely assessed. For this reason, we investigated, through a multicenter observational cross-sectional study, based on an online questionnaire, gender-related differences in stress factors, emotions, and pain perception in migraine patients and controls and their impact on migraine severity. Methods: The study was designed as an online questionnaire. The link was emailed to healthy subjects (C) and migraine patients (MIG) (age 18-75, education ≥ 13 years) recruited during the first visit in 8 Italian Headache Centers adhering to Italian Society for Headache Study (SISC). The questionnaire included personal/social/work information, the Perceived Stress Scale, the Romance Quality Scale, the Emotion Regulation Questionnaire, the Beck Anxiety Inventory, the Body Perception Questionnaire, the pain perception, and a self-assessment of migraine severity in the last 3 months. Results: 202 MIG and 202 C completed the survey. Independently from gender, migraine was characterized by higher pain sensitivity and more severe partner relationships. The female gender, in MIG, exhibited higher anxiety scores, body awareness, and reduced emotional suppression. Body awareness and emotional suppression were discriminating factors between genders in control and migraine groups without relevant influence on disease features. Perceived perception of migraine severity was similar between genders. Conclusion: Gender-related emotional and stress factors did not contribute to delineate a distinct phenotype in migraine men and women. The possible impact of emotional and stress factors characterizing genders could be considered for a single case-tailored therapeutic approach

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Protein Aggregation

Protein aggregation occurs in vivo as a result of improper folding or misfolding. Diverse diseases arise from protein misfolding and are now grouped under the term "protein conformational diseases", including most of the neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, the prion encephalopathies and Huntington's disease, as well as cystic fibrosis, sickle cell anemia and other less common conditions. The hallmark event in these diseases is a change in the secondary and/or tertiary structure of a normal, functional protein, leading to the formation of protein aggregates with various supramolecular organizations. In most cases the aggregates are organized in structurally well-defined fibrils forming amyloid deposits. The crucial feature of the amyloidogenic proteins is their structural instability induced either by mutations, post-translational modifications, or local conditions, such as pH, temperature, and co-solutes. The conformational change may promote the disease either by gain of a toxic activity or by the lack of biological function of the natively folded protein. As different molecular mechanisms are involved in the formation of the various forms of protein aggregates, the laboratory diagnostic approach remains frequently elusive

Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset

Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases. This is particularly important in the study of barely separable populations according to MRI data, such as subjects with ASD compared to controls with typical development (TD). Here, we show how the implementation of a harmo- nization protocol on brain structural features unlocks the case-control ML separation capability in the analysis of a multi-center MRI dataset. This effect is demonstrated on the ABIDE data collection, involving subjects encompassing a wide age range. After data harmonization, the overall ASD vs. TD discrimination capability by a Random Forest (RF) classifier improves from a very low performance (AUC = 0.58 +/- 0.04) to a still low, but reasonably significant AUC = 0.67 +/- 0.03. The performances of the RF classifier have been evaluated also in the age-specific subgroups of children, adolescents and adults, obtaining AUC = 0.62 +/- 0.02, AUC = 0.65 +/- 0.03 and AUC = 0.69 +/- 0.06, respectively. Specific and consistent patterns of anatomical differences related to the ASD condition have been identified for the three different age subgroups

Effect of micromixer design on lipid nanocarriers manufacturing for the delivery of proteins and nucleic acids

Lipid-based nanocarriers have emerged as helpful tools to deliver sensible biomolecules such as proteins and oligonucleotides. To have a fast and robust microfluidic-based nanoparticle synthesis method, the setup of versatile equipment should allow for the rapid transfer to scale costeffectively while ensuring tunable, precise and reproducible nanoparticle attributes. The present work aims to assess the effect of different micromixer geometries on the manufacturing of lipid nanocarriers taking into account the influence on the mixing efficiency by changing the fluid–fluid interface and indeed the mass transfer. Since the geometry of the adopted micromixer varies from those already published, a Design of Experiment (DoE) was necessary to identify the operating (total flow, flow rate ratio) and formulation (lipid concentration, lipid molar ratios) parameters affecting the nanocarrier quality. The suitable application of the platform was investigated by producing neutral, stealth and cationic liposomes, using DaunoXome®, Myocet®, Onivyde® and Onpattro® as the benchmark. The effect of condensing lipid (DOTAP, 3–10–20 mol%), coating lipids (DSPE-PEG550 and DSPE-PEG2000), as well as structural lipids (DSPC, eggPC) was pointed out. A very satisfactory encapsulation efficiency, always higher than 70%, was successfully obtained for model biomolecules (myoglobin, short and long nucleic acids)

Synthesis, structural characterization and effect on human granulocyte intracellular cAMP levels of abscisic acid analogs

The phytohormone abscisic acid (ABA), in addition to regulating physiological functions in plants, is alsoproduced and released by several mammalian cell types, including human granulocytes, where it stim-ulates innate immune functions via an increase of the intracellular cAMP concentration ([cAMP]i).We synthesized several ABA analogs and evaluated the structure\u2013activity relationship, by the system-atical modification of selected regions of these analogs. The resulting molecules were tested for their abil-ity to inhibit the ABA-induced increase of [cAMP]i in human granulocytes. The analogs with modifiedconfigurations at C-20and C-30abrogated the ABA-induced increase of the [cAMP]i and also inhibited sev-eral pro-inflammatory effects induced by exogenous ABA on granulocytes and monocytes. Accordingly,these analogs could be suitable as novel putative anti-inflammatory compounds. \ua9 2014 Elsevier Ltd. All rights reserved