Protein Aggregation Measurement through Electrical Impedance Spectroscopy

The paper presents a novel methodology to measure the fibril formation in protein solutions. We designed a bench consisting of a sensor having interdigitated electrodes, a PDMS hermetic reservoir and an impedance meter automatically driven by calculator. The impedance data are interpolated with a lumped elements model and their change over time can provide information on the aggregation process. Encouraging results have been obtained by testing the methodology on K-casein, a protein of milk, with and without the addition of a drug inhibiting the aggregation. The amount of sample needed to perform this measurement is by far lower than the amount needed by fluorescence analysis

Article a new epigenetic model to stratify glioma patients according to their immunosuppressive state

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Further-more, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state

Machine learning to improve interpretability of clinical, radiological and panel-based genomic data of glioma grade 4 patients undergoing surgical resection

Background: Glioma grade 4 (GG4) tumors, including astrocytoma IDH-mutant grade 4 and the astrocytoma IDH wt are the most common and aggressive primary tumors of the central nervous system. Surgery followed by Stupp protocol still remains the first-line treatment in GG4 tumors. Although Stupp combination can prolong survival, prognosis of treated adult patients with GG4 still remains unfavorable. The introduction of innovative multi-parametric prognostic models may allow refinement of prognosis of these patients. Here, Machine Learning (ML) was applied to investigate the contribution in predicting overall survival (OS) of different available data (e.g. clinical data, radiological data, or panel-based sequencing data such as presence of somatic mutations and amplification) in a mono-institutional GG4 cohort. Methods: By next-generation sequencing, using a panel of 523 genes, we performed analysis of copy number variations and of types and distribution of nonsynonymous mutations in 102 cases including 39 carmustine wafer (CW) treated cases. We also calculated tumor mutational burden (TMB). ML was applied using eXtreme Gradient Boosting for survival (XGBoost-Surv) to integrate clinical and radiological information with genomic data. Results: By ML modeling (concordance (c)- index = 0.682 for the best model), the role of predicting OS of radiological parameters including extent of resection, preoperative volume and residual volume was confirmed. An association between CW application and longer OS was also showed. Regarding gene mutations, a role in predicting OS was defined for mutations of BRAF and of other genes involved in the PI3K-AKT-mTOR signaling pathway. Moreover, an association between high TMB and shorter OS was suggested. Consistently, when a cutoff of 1.7 mutations/megabase was applied, cases with higher TMB showed significantly shorter OS than cases with lower TMB. Conclusions: The contribution of tumor volumetric data, somatic gene mutations and TBM in predicting OS of GG4 patients was defined by ML modeling

A novel comprehensive clinical stratification model to refine prognosis of glioblastoma patients undergoing surgical resection

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell\u2019s c-index of 0.79 (95% confidence interval [0.76\u20130.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment

Prion protein interaction with soil humic substances: environmental implications

Transmissible spongiform encephalopathies (TSE) are fatal neurodegenerative disorders caused by prions. Animal TSE include scrapie in sheep and goats, and chronic wasting disease (CWD) in cervids. Effective management of scrapie in many parts of the world, and of CWD in North American deer population is complicated by the persistence of prions in the environment. After shedding from diseased animals, prions persist in soil, withstanding biotic and abiotic degradation. As soil is a complex, multi-component system of both mineral and organic components, it is important to understand which soil compounds may interact with prions and thus contribute to disease transmission. Several studies have investigated the role of different soil minerals in prion adsorption and infectivity; we focused our attention on the interaction of soil organic components, the humic substances (HS), with recombinant prion protein (recPrP) material. We evaluated the kinetics of recPrP adsorption, providing a structural and biochemical characterization of chemical adducts using different experimental approaches. Here we show that HS act as potent anti-prion agents in prion infected neuronal cells and in the amyloid seeding assays: HS adsorb both recPrP and prions, thus sequestering them from the prion replication process. We interpreted our findings as highly relevant from an environmental point of view, as the adsorption of prions in HS may affect their availability and consequently hinder the environmental transmission of prion diseases in ruminants

Manganese Enhances Prion Protein Survival in Model Soils and Increases Prion Infectivity to Cells

Prion diseases are considered to be transmissible. The existence of sporadic forms of prion diseases such as scrapie implies an environmental source for the infectious agent. This would suggest that under certain conditions the prion protein, the accepted agent of transmission, can survive in the environment. We have developed a novel technique to extract the prion protein from soil matrices. Previous studies have suggested that environmental manganese is a possible risk factor for prion diseases. We have shown that exposure to manganese is a soil matrix causes a dramatic increase in prion protein survival (∼10 fold) over a two year period. We have also shown that manganese increases infectivity of mouse passaged scrapie to culture cells by 2 logs. These results clearly verify that manganese is a risk factor for both the survival of the infectious agent in the environment and its transmissibility

The mechanisms of humic substances self-assembly with biological molecules: The case study of the prion protein

Humic substances (HS) are the largest constituent of soil organic matter and are considered as a key component of the terrestrial ecosystem. HS may facilitate the transport of organic and inorganic molecules, as well as the sorption interactions with environmentally relevant proteins such as prions. Prions enter the environment through shedding from live hosts, facilitating a sustained incidence of animal prion diseases such as Chronic Wasting Disease and scrapie in cervid and ovine populations, respectively. Changes in prion structure upon environmental exposure may be significant as they can affect prion infectivity and disease pathology. Despite its relevance, the mechanisms of prion interaction with HS are still not completely understood. The goal of this work is to advance a structural-level picture of the encapsulation of recombinant, non-infectious, prion protein (PrP) into different natural HS. We observed that PrP precipitation upon addition of HS is mainly driven by a mechanism of “salting-out” whereby PrP molecules are rapidly removed from the solution and aggregate in insoluble adducts with humic molecules. Importantly, this process does not alter the protein folding since insoluble PrP retains its α-helical content when in complex with HS. The observed ability of HS to promote PrP insolubilization without altering its secondary structure may have potential relevance in the context of “prion ecology”. These results suggest that soil organic matter interacts with prions possibly without altering the protein structures. This may facilitate prions preservation from biotic and abiotic degradation leading to their accumulation in the environment

Synthetic prions with novel strain-specified properties

Prions are infectious proteins that possess multiple self-propagating structures. The information for strains and structural specific barriers appears to be contained exclusively in the folding of the pathological isoform, PrP(Sc). Many recent studies determined that de novo prion strains could be generated in vitro from the structural conversion of recombinant (rec) prion protein (PrP) into amyloidal structures. Our aim was to elucidate the conformational diversity of pathological recPrP amyloids and their biological activities, as well as to gain novel insights in characterizing molecular events involved in mammalian prion conversion and propagation. To this end we generated infectious materials that possess different conformational structures. Our methodology for the prion conversion of recPrP required only purified rec full-length mouse (Mo) PrP and common chemicals. Neither infected brain extracts nor amplified PrP(Sc) were used. Following two different in vitro protocols recMoPrP converted to amyloid fibrils without any seeding factor. Mouse hypothalamic GT1 and neuroblastoma N2a cell lines were infected with these amyloid preparations as fast screening methodology to characterize the infectious materials. Remarkably, a large number of amyloid preparations were able to induce the conformational change of endogenous PrPC to harbor several distinctive proteinase-resistant PrP forms. One such preparation was characterized in vivo habouring a synthetic prion with novel strain specified neuropathological and biochemical properties