Stereochemical Behavior of Pyrrolo-Pyrazole Peptidomimetics Promoting Phase-Selective Supramolecular Organogels.

Supramolecular gels were developed by taking advantage of an assembly of small dipeptides containing pyrrolo-pyrazole scaffolds. The dipeptides were prepared through a robust and ecofriendly synthetic approach from the commercially available starting materials of diazoalkanes and maleimides. By playing with the functionalization of the scaffold, the choice of the natural amino acid, and the stereochemistry, we were able to obtain phase-selective gels. In particular, one peptidomimetic showed gelation ability and thermoreversibility in aromatic solvents at very low concentrations. Rheology tests showed a typical viscoelastic solid profile, indicating the formation of strong gels that were stable under high mechanical deformation. NMR studies were performed, allowing us to determine the conformational and stereochemical features at the base of the supramolecular interactions.

Aggregation-Induced Enhanced Emission of Tetraphenylethene-phenylalanine Hybrids: Synthesis and Characterization.

Aggregation-induced emitting (AIE) luminophores are sensitive and easy-to-handle types of probes that allow driving a stimulus-responsive off/on optical tool through the manipulation of the aggregation behavior. In this work, tetraphenylethene (TPE)-phenylalanine derivatives, characterized by strong aggregation-induced luminescence, were obtained through Suzuki-Miyaura cross-coupling reactions. The reaction proved to be straightforwardly applicable in the single amino acid synthesis as well as in the late-stage peptide functionalization by means of both the classical solution-phase reaction and solid-phase synthesis. A comprehensive structural and analytical investigation highlighted the features driving the self-assembly process and its relationship to AIE efficiency. In particular, we showed that the simple slight (asymmetric) extension of the TPE π-systems results in more efficient and brighter emissions, with respect to the simple TPE system itself.

CHCHD4 binding affects the active site of apoptosis inducing factor (AIF): Structural determinants for allosteric regulation.

Apoptosis-inducing factor (AIF), which is confined to mitochondria of normal healthy cells, is the first identified caspase-independent cell death effector. Moreover, AIF is required for the optimal functioning of the respiratory chain machinery. Recent findings have revealed that AIF fulfills its pro-survival function by interacting with CHCHD4, a soluble mitochondrial protein which promotes the entrance and the oxidative folding of different proteins in the inner membrane space. Here, we report the crystal structure of the ternary complex involving the N-terminal 27-mer peptide of CHCHD4, NAD+, and AIF harboring its FAD (flavin adenine dinucleotide) prosthetic group in oxidized form. Combining this information with biophysical and biochemical data on the CHCHD4/AIF complex, we provide a detailed structural description of the interaction between the two proteins, validated by both chemical cross-linking mass spectrometry analysis and site-directed mutagenesis.

Machine Learning and Texture Analysis of [18F]FDG PET/CT Images for the Prediction of Distant Metastases in Non-Small-Cell Lung Cancer Patients.

The aim of our study was to predict the occurrence of distant metastases in non-small-cell lung cancer (NSCLC) patients using machine learning methods and texture analysis of 18F-labeled 2-deoxy-d-glucose Positron Emission Tomography/Computed Tomography {[18F]FDG PET/CT} images. In this retrospective and single-center study, we evaluated 79 patients with advanced NSCLC who had undergone [18F]FDG PET/CT scan at diagnosis before any therapy. Patients were divided into two independent training (n = 44) and final testing (n = 35) cohorts. Texture features of primary tumors and lymph node metastases were extracted from [18F]FDG PET/CT images using the LIFEx program. Six machine learning methods were applied to the training dataset using the entire panel of features. Dedicated selection methods were used to generate different combinations of five features. The performance of selected machine learning methods applied to the different combinations of features was determined using accuracy, the confusion matrix, receiver operating characteristic (ROC) curves, and area under the curve (AUC). A total of 104 and 78 lesions were analyzed in the training and final testing cohorts, respectively. The support vector machine (SVM) and decision tree methods showed the highest accuracy in the training cohort. Seven combinations of five features were obtained and introduced in the models and subsequently applied to the training and final testing cohorts using the SVM and decision tree. The accuracy and the AUC of the decision tree method were higher than those obtained with the SVM in the final testing cohort. The best combination of features included shape sphericity, gray level run length matrix_run length non-uniformity (GLRLM_RLNU), Total Lesion Glycolysis (TLG), Metabolic Tumor Volume (MTV), and shape compacity. The combination of these features with the decision tree method could predict the occurrence of distant metastases with an accuracy of 74.4% and an AUC of 0.63 in NSCLC patients.

Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors.

Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan-Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan-Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.

Breaking down and building up alpha-synuclein: An insight on its N-terminal domain.

Alpha-synuclein (αSyn) is a small presynaptic protein (14 kDa) that is involved in synucleinopathies including Parkinson's disease (PD). In its native state, the αSyn monomer exists in an unfolded state, and its folding is highly dependent on variations of environmental conditions, mutations and interactions with endogenous and/or exogenous molecules. Recently, there is increasing evidence for a direct interplay between αSyn and microtubules (MTs), whose defects are linked to neurodegenerative diseases, such as PD. Understanding the correlation between αSyn and MTs could be fundamental for the correct comprehension of the undergoing mechanisms of PD. Hence, we chemically synthesized a library of peptides, deriving from both native and PD mutated sequences of the N-terminal domain of αSyn. Their secondary structure was characterized by circular dichroism and Fourier transform infrared (FTIR) experiments, in order to evaluate the effect of PD mutations. Finally, the kinetics of polymerizing tubulin in vitro in the presence of the peptides was evaluated.

Tetraphenylethylene-Based Photoluminescent Self-Assembled Nanoparticles: Preparation and Biological Evaluation.

The conjugation of tetraphenylethylene (TPE) with podophyllotoxin, N-desacetylthiocolchicine, and cabazitaxel through a sebacic acid linker led to the formation of fluorescent nanoparticles. Dynamic light scattering (DLS) and photoluminescence spectroscopy were used for the identification and characterization of the fluorescent nanoparticles. The biological evaluation was determined in three human ovarian (KURAMOCHI, OVCAR3, OVSAHO) and three human breast (MCF7, SKBR 3, and MDA-MB231) cancer cell lines. In the case of cabazitaxel, the nanoparticles maintained the activity of the parent drug, at the low nanomolar range, while exhibiting high blue fluorescence. The internalization of the fluorescent NPs into cells was detected using immunofluorescence assay.

Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer.

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from 18F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent 18F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTVTOT) and whole-body Total Lesion Glycolysis (TLGWB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTVTOT, TLGWB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTVTOT (p = 0.0050), TLGWB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTVTOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTVTOT ≤ 89.5 mL had higher OS than those with MTVTOT > 89.5 mL (p = 0.0063). Combining CoV and MTVTOT, patients with CoV ≤ 0.38 and MTVTOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.

Diagnosis, Management and Theragnostic Approach of Gastro-Entero-Pancreatic Neuroendocrine Neoplasms.

Gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs) constitute an ideal target for radiolabeled somatostatin analogs. The theragnostic approach is able to combine diagnosis and therapy by the identification of a molecular target that can be diagnosed and treated with the same radiolabeled compound. During the last years, advances in functional imaging with the introduction of somatostatin analogs and peptide receptor radionuclide therapy, have improved the diagnosis and treatment of GEP-NENs. Moreover, PET/CT imaging with 18F-FDG represents a complementary tool for prognostic evaluation of patients with GEP-NENs. In the field of personalized medicine, the theragnostic approach has emerged as a promising tool in diagnosis and management of patients with GEP-NENs. The aim of this review is to summarize the current evidence on diagnosis and management of patients with GEP-NENs, focusing on the theragnostic approach.

"What does not kill you mutates and tries again." A study on personality determinants of post-traumatic growth during the COVID-19 pandemic.

Introduction. The COVID-19 pandemic was recognized as a collective trauma and as a major threat to mental health. Recent literature focused on the stress symptomatology or post-traumatic stress disorder associated to the COVID-19 exposure. The concept that people have a natural inclination toward growth, even under stressful and threatening events, gathered less attention. Previous research has analyzed antecedents of post-traumatic growth (PTG) with non-conclusive results. Methods. The present research aimed at including findings on PTG from personality traits, i.e., sense of control and self-mastery, and distal condition of nurturance and support received by others, i.e., cognitive and affective well-being. Analyses were based on 4934 interviews with adults (Mage = 57.81 years, 55.5% women) from the Swiss Household Panel study. Results. Relationships over time emerged between sense of control and self-mastery on PTG and worries, measured after two years, via the mediation of cognitive and affective well-being. Conclusion. Results come from a large study in a design seldom employed in this type of research and can inform both research and interventions.

Heat-Treated Lysozyme Hydrochloride: A Study on Its Structural Modifications and Anti-SARS-CoV-2 Activity.

Lysozyme (E.C. 3.2.1.17), an about 14 kDa protein and pI 11, widely spread in nature, is present in humans mainly in milk, saliva, and intestinal mucus as a part of innate defense mechanisms. It is endowed with antimicrobial activity due to its action as an N-acetylmuramidase, cleaving the 1-4ß glycosidic linkage in the peptidoglycan layer of Gram-positive bacteria. This antimicrobial activity is exerted only against a limited number of Gram-negative bacteria. Different action mechanisms are proposed to explain its activity against Gram-negative bacteria, viruses, and fungi. The antiviral activity prompted the study of a possible application of lysozyme in the treatment of SARS-CoV-2 infections. Among the different sources of lysozyme, the chicken egg albumen was chosen, being the richest source of this protein (c-type lysozyme, 129 amino acids). Interestingly, the activity of lysozyme hydrochloride against SARS-CoV-2 was related to the heating (to about 100 °C) of this molecule. A chemical-physical characterization was required to investigate the possible modifications of native lysozyme hydrochloride by heat treatment. The FTIR analysis of the two preparations of lysozyme hydrochloride showed appreciable differences in the secondary structure of the two protein chains. HPLC and NMR analyses, as well as the enzymatic activity determination, did not show significant modifications.

The cyclic peptide G4CP2 enables the modulation of galactose metabolism in yeast by interfering with GAL4 transcriptional activity.

Genetically-encoded combinatorial peptide libraries are convenient tools to identify peptides to be used as therapeutics, antimicrobials and functional synthetic biology modules. Here, we report the identification and characterization of a cyclic peptide, G4CP2, that interferes with the GAL4 protein, a transcription factor responsible for the activation of galactose catabolism in yeast and widely exploited in molecular biology. G4CP2 was identified by screening CYCLIC, a Yeast Two-Hybrid-based combinatorial library of cyclic peptides developed in our laboratory. G4CP2 interferes with GAL4-mediated activation of galactose metabolic enzymes both when expressed intracellularly, as a recombinant peptide, and when provided exogenously, as a chemically-synthesized cyclic peptide. Our results support the application of G4CP2 in microbial biotechnology and, additionally, demonstrate that CYCLIC can be used as a tool for the rapid identification of peptides, virtually without any limitations with respect to the target protein. The possible biotechnological applications of cyclic peptides are also discussed.

Conformational switch and multiple supramolecular structures of a newly identified self-assembling protein-mimetic peptide from Pseudomonas aeruginosa YeaZ protein.

Protein-mimetic peptides (PMPs) are shorter sequences of self-assembling proteins, that represent remarkable building blocks for the generation of bioinspired functional supramolecular structures with multiple applications. The identification of novel aminoacidic sequences that permit the access to valuable biocompatible materials is an attractive area of research. In this work, in silico analysis of the Pseudomonas aeruginosa YeaZ protein (PaYeaZ) led to the identification of a tetradecapeptide that represents the shortest sequence responsible for the YeaZ-YeaZ dimer formation. Based on its sequence, an innovative 20-meric peptide, called PMP-2, was designed, synthesized, and characterized in terms of secondary structure and self-assembly properties. PMP-2 conserves a helical character and self-assembles into helical nanofibers in non-polar solvents (DMSO and trifluoroethanol), as well as in dilute (0.5 mM) aqueous solutions. In contrast, at higher concentrations (>2 mM) in water, a conformational transition from α-helix to ß-sheet occurs, which is accompanied by the Protein-mimetic peptide aggregation into 2D-sheets and formation supramolecular gel in aqueous environment. Our findings reveal a newly identified Protein-mimetic peptide that could turn as a promising candidate for future material applications.

Rational Design of a Peptidomimetic Inhibitor of Gelsolin Amyloid Aggregation.

Gelsolin amyloidosis (AGel) is characterized by multiple systemic and ophthalmic features resulting from pathological tissue deposition of the gelsolin (GSN) protein. To date, no cure is available for the treatment of any form of AGel. More than ten single-point substitutions in the GSN gene are responsible for the occurrence of the disease and, among them, D187N/Y is the most widespread variant. These substitutions undergo an aberrant proteolytic cascade, producing aggregation-prone peptides of 5 and 8 kDa, containing the Gelsolin Amyloidogenic Core, spanning residues 182-192 (GAC182-192). Following a structure-based approach, we designed and synthesized three novel sequence-specific peptidomimetics (LB-5, LB-6, and LB-7) built on a piperidine-pyrrolidine unnatural amino acid. LB-5 and LB-6, but not LB-7, efficiently inhibit the aggregation of the GAC182-192 amyloidogenic peptides at sub-stoichiometric concentrations. These peptidomimetics resulted also effective in vivo, in a C. elegans-based assay, in counteracting the proteotoxicity of aggregated GAC182-192. These data pave the way to a novel pharmacological strategy against AGel and also validate a toolbox exploitable in other amyloidogenic diseases.

Dual Functionalized Liposomes for Selective Delivery of Poorly Soluble Drugs to Inflamed Brain Regions.

Dual functionalized liposomes were developed to cross the blood−brain barrier (BBB) and to release their cargo in a pathological matrix metalloproteinase (MMP)-rich microenvironment. Liposomes were surface-functionalized with a modified peptide deriving from the receptor-binding domain of apolipoprotein E (mApoE), known to promote cargo delivery to the brain across the BBB in vitro and in vivo; and with an MMP-sensitive moiety for an MMP-triggered drug release. Different MMP-sensitive peptides were functionalized at both ends with hydrophobic stearate tails to yield MMP-sensitive lipopeptides (MSLPs), which were assembled into mApoE liposomes. The resulting bi-functional liposomes (i) displayed a < 180 nm diameter with a negative ζ-potential; (ii) were able to cross an in vitro BBB model with an endothelial permeability of 3 ± 1 × 10−5 cm/min; (iii) when exposed to functional MMP2 or 9, efficiently released an encapsulated fluorescein dye; (iv) showed high biocompatibility when tested in neuronal cultures; and (v) when loaded with glibenclamide, a drug candidate with poor aqueous solubility, reduced the release of proinflammatory cytokines from activated microglial cells.

What molecular imaging of cancer patients can teach us about COVID-19.

COVID-19 pandemic had a great impact on health systems and cancer care worldwide. Patients with cancer who develop COVID-19 are at high risk of severe outcomes and clarifying the determinants of such vulnerability of cancer patients would be of great clinical benefit. While the mechanisms of SARS-CoV-2 infection have been elucidated, the pathogenetic pathways leading to severe manifestations of the disease are largely unknown. Critical manifestations of COVID-19 mainly occur in elderly patients and in patients with serious comorbidities including cancer. Efforts to understand the intersection of pathways between severe manifestations of COVID-19 and cancer may shed light on the pathogenesis of critical illness in COVID-19 patients. Here, we will focus our attention on two major fields of potential intersection between COVID-19 and cancer, namely the dysfunction of immune system and the prothrombotic state that can occur in both COVID-19 and cancer patients, testing whether cancer imaging can provide clues to better understand such interactions.

Fatty Acids/Tetraphenylethylene Conjugates: Hybrid AIEgens for the Preparation of Peptide-Based Supramolecular Gels.

Aggregation-induced emissive materials are gaining particular attention in the last decades due to their wide application in different fields, from optical devices to biomedicine. In this work, compounds having these kinds of properties, composed of tetraphenylethylene scaffold combined with fatty acids of different lengths, were synthesized and characterized. These molecules were found able to self-assemble into different supramolecular emissive structures depending on the chemical composition and water content. Furthermore, they were used as N-terminus capping agents in the development of peptide-based materials. The functionalization of a 5-mer laminin-derived peptide led to the obtainment of luminescent fibrillary materials that were not cytotoxic and were able to form supramolecular gels in aqueous environment.

Multi-eGO: An in silico lens to look into protein aggregation kinetics at atomic resolution.

Protein aggregation into amyloid fibrils is the archetype of aberrant biomolecular self-assembly processes, with more than 50 associated diseases that are mostly uncurable. Understanding aggregation mechanisms is thus of fundamental importance and goes in parallel with the structural characterization of the transient oligomers formed during the process. Oligomers have been proven elusive to high-resolution structural techniques, while the large sizes and long time scales, typical of aggregation processes, have limited the use of computational methods to date. To surmount these limitations, we here present multi-eGO, an atomistic, hybrid structure-based model which, leveraging the knowledge of monomers conformational dynamics and of fibril structures, efficiently captures the essential structural and kinetics aspects of protein aggregation. Multi-eGO molecular dynamics simulations can describe the aggregation kinetics of thousands of monomers. The concentration dependence of the simulated kinetics, as well as the structural features of the resulting fibrils, are in qualitative agreement with in vitro experiments carried out on an amyloidogenic peptide from Transthyretin, a protein responsible for one of the most common cardiac amyloidoses. Multi-eGO simulations allow the formation of primary nuclei in a sea of transient lower-order oligomers to be observed over time and at atomic resolution, following their growth and the subsequent secondary nucleation events, until the maturation of multiple fibrils is achieved. Multi-eGO, combined with the many experimental techniques deployed to study protein aggregation, can provide the structural basis needed to advance the design of molecules targeting amyloidogenic diseases.