Key targets for multi-target ligands designed to combat neurodegeneration

This article is based upon work from COST Action CM1103 “Structure-based drug design for diagnosis and treatment of neurological diseases: dissecting and modulating complex function in the monoaminergic systems of the brain”, supported by COST (European Cooperation in Science and Technology). The authors thank the participants in COST Action for productive collaborations. M. Majekova acknowledges the support of VEGA 2/0033/14, and M. Medina the support of MINECO, Spain (BIO2013-42978-P)Growing evidence supports the view that neurodegenerative diseases have multiple and common mechanisms in their aetiologies. These multifactorial aspects have changed the broadly common assumption that selective drugs are superior to ‘dirty drugs’ for use in therapy. This drives the research in studies of novel compounds that might have multiple action mechanisms. In neurodegeneration, loss of neuronal signaling is a major cause of the symptoms, so preservation of neurotransmitters by inhibiting the breakdown enzymes is a first approach. Acetylcholinesterase (AChE) inhibitors are the drugs preferentially used in AD and that one of these, rivastigmine, is licensed also for PD. Several studies have shown that monoamine oxidase (MAO) B, located mainly in glial cells, increases with age and is elevated in Alzheimer (AD) and Parkinson’s Disease’s (PD). Deprenyl, a MAO B inhibitor, significantly delays the initiation of levodopa treatment in PD patients. These indications underline that AChE and MAO are considered a necessary part of multi-target designed ligands (MTDL). However, both of these targets are simply symptomatic treatment so if new drugs are to prevent degeneration rather than compensate for loss of neurotransmitters, then oxidative stress and mitochondrial events must also be targeted. MAO inhibitors can protect neurons from apoptosis by mechanisms unrelated to enzyme inhibition. Understanding the involvement of MAO and other proteins in the induction and regulation of the apoptosis in mitochondria will aid progress towards strategies to prevent the loss of neurons. In general, the oxidative stress observed both in PD and AD indicate that antioxidant properties are a desirable part of MTDL molecules. After two or more properties are incorporated into one molecule, the passage from a lead compound to a therapeutic tool is strictly linked to its pharmacokinetic and toxicity. In this context the interaction of any new molecules with cytochrome P450 and other xenobiotic metabolic processes is a crucial point. The present review covers the biochemistry of enzymes targeted in the design of drugs against neurodegeneration and the cytochrome P450-dependent metabolism of MTDLs.Publisher PDFPeer reviewe

ALEPH: a network-oriented approach for the generation of fragment-based libraries and for structure interpretation.

The analysis of large structural databases reveals general features and relationships among proteins, providing useful insight. A different approach is required to characterize ubiquitous secondary-structure elements, where flexibility is essential in order to capture small local differences. The ALEPH software is optimized for the analysis and the extraction of small protein folds by relying on their geometry rather than on their sequence. The annotation of the structural variability of a given fold provides valuable information for fragment-based molecular-replacement methods, in which testing alternative model hypotheses can succeed in solving difficult structures when no homology models are available or are successful. ARCIMBOLDO_BORGES combines the use of composite secondary-structure elements as a search model with density modification and tracing to reveal the rest of the structure when both steps are successful. This phasing method relies on general fold libraries describing variations around a given pattern of β-sheets and helices extracted using ALEPH. The program introduces characteristic vectors defined from the main-chain atoms as a way to describe the geometrical properties of the structure. ALEPH encodes structural properties in a graph network, the exploration of which allows secondary-structure annotation, decomposition of a structure into small compact folds, generation of libraries of models representing a variation of a given fold and finally superposition of these folds onto a target structure. These functions are available through a graphical interface designed to interactively show the results of structure manipulation, annotation, fold decomposition, clustering and library generation. ALEPH can produce pictures of the graphs, structures and folds for publication purposes

Toxigenic fungi and mycotoxins in a climate change scenario: Ecology, genomics, distribution, prediction and prevention of the risk

Toxigenic fungi and mycotoxins are very common in food crops, with noticeable differences in their host specificity in terms of pathogenicity and toxin contamination. In addition, such crops may be infected with mixtures of mycotoxigenic fungi, resulting in multi-mycotoxin contamination. Climate represents the key factor in driving the fungal community structure and mycotoxin contamination levels pre- and post-harvest. Thus, there is significant interest in understanding the impact of interacting climate change-related abiotic factors (especially increased temperature, elevated CO2 and extremes in water availability) on the relative risks of mycotoxin contamination and impacts on food safety and security. We have thus examined the available information from the last decade on relative risks of mycotoxin contamination under future climate change scenarios and identified the gaps in knowledge. This has included the available scientific information on the ecology, genomics, distribution of toxigenic fungi and intervention strategies for mycotoxin control worldwide. In addition, some suggestions for prediction and prevention of mycotoxin risks are summarized together with future perspectives and research needs for a better understanding of the impacts of climate change scenarios

The grapevine guard cell-related VvMYB60 transcription factor is involved in the regulation of stomatal activity and is differentially expressed in response to ABA and osmotic stress

- Background: Under drought, plants accumulate the signaling hormone abscisic acid (ABA), which induces the rapid closure of stomatal pores to prevent water loss. This event is trigged by a series of signals produced inside guard cells which finally reduce their turgor. Many of these events are tightly regulated at the transcriptional level, including the control exerted by MYB proteins. In a previous study, while identifying the grapevine R2R3 MYB family, two closely related genes, VvMYB30 and VvMYB60 were found with high similarity to AtMYB60, an Arabidopsis guard cell-related drought responsive gene. - Results: Promoter-GUS transcriptional fusion assays showed that expression of VvMYB60 was restricted to stomatal guard cells and was attenuated in response to ABA. Unlike VvMYB30, VvMYB60 was able to complement the loss-of-function atmyb60-1 mutant, indicating that VvMYB60 is the only true ortholog of AtMYB60 in the grape genome. In addition, VvMYB60 was differentially regulated during development of grape organs and in response to ABA and drought-related stress conditions. - Conclusions: These results show that VvMYB60 modulates physiological responses in guard cells, leading to the possibility of engineering stomatal conductance in grapevine, reducing water loss and helping this species to tolerate drought under extreme climatic conditions

SEQUENCE SLIDER: expanding polyalanine fragments for phasing with multiple side-chain hypotheses.

Fragment-based molecular-replacement methods can solve a macromolecular structure quasi-ab initio. ARCIMBOLDO, using a common secondary-structure or tertiary-structure template or a library of folds, locates these with Phaser and reveals the rest of the structure by density modification and autotracing in SHELXE. The latter stage is challenging when dealing with diffraction data at lower resolution, low solvent content, high β-sheet composition or situations in which the initial fragments represent a low fraction of the total scattering or where their accuracy is low. SEQUENCE SLIDER aims to overcome these complications by extending the initial polyalanine fragment with side chains in a multisolution framework. Its use is illustrated on test cases and previously unknown structures. The selection and order of fragments to be extended follows the decrease in log-likelihood gain (LLG) calculated with Phaser upon the omission of each single fragment. When the starting substructure is derived from a remote homolog, sequence assignment to fragments is restricted by the original alignment. Otherwise, the secondary-structure prediction is matched to that found in fragments and traces. Sequence hypotheses are trialled in a brute-force approach through side-chain building and refinement. Scoring the refined models through their LLG in Phaser may allow discrimination of the correct sequence or filter the best partial structures for further density modification and autotracing. The default limits for the number of models to pursue are hardware dependent. In its most economic implementation, suitable for a single laptop, the main-chain trace is extended as polyserine rather than trialling models with different sequence assignments, which requires a grid or multicore machine. SEQUENCE SLIDER has been instrumental in solving two novel structures: that of MltC from 2.7 Å resolution data and that of a pneumococcal lipoprotein with 638 residues and 35% solvent content

RoboCrane: a system for providing a power and a communication link between lunar surface and lunar caves for exploring robots

Lava caves are the result of a geological process related to the cooling of basaltic lava flows. On the Moon, this process may lead to caves several kilometers long and diameters of hundreds of meters. Access to lava tubes can be granted through skylights, a vertical pit between the lava tube and the lunar surface. This represents an outstanding opportunity for long-term missions, for future permanent human settlements, and for accessing pristine samples of lava, secondary minerals and volatiles. Given this, the ESA launched a campaign through the Open Space Innovation Platform calling for ideas that would tackle the many challenges of exploring lava pits. Five projects, including Robocrane, were selected. Solar light and direct line of sight (for communications) with the lunar surface are not available inside lava tubes. This is a problem for any robot (or swarm of robots) exploring the lava tubes. Robocrane tackles both problems by deploying an element (called the Charging head, or CH) at the bottom of the skylight by means of a crane. This CH behaves as a battery charger and a communication relay for the exploring robots. The required energy is extracted from the crane’s solar panel (on the surface) and driven to the bottom of the skylight through an electrical wire running in parallel to the crane hoisting wire. Using a crane allows the system to deal with unstable terrain around the skylight rim and protect the wires from abrasion from the rocky surface and the pit rim. The charger in the CH is wireless so that the charging process can begin as soon as any of the robots get close enough to the CH. This avoids complex and time-consuming docking operations, aggravated by the skylight floor orography. The crane infrastructure can also be used to deploy the exploring robots inside the pit, reducing their design constraints and mass budget, as the robots do not need to implement their own self-deployment system. Finally, RoboCrane includes all the sensors and actuators for remote operation from a ground station. RoboCrane has been designed in a parametric tool so it can be dynamically and rapidly adjusted to input-variable changes, such as the number of exploring robots, their electrical characteristics, and crane reach, etc.Agencia Estatal de Investigación | Ref. RTI2018-099682-A-I0

Dissecting the Role of Mesenchymal Stem Cells in Idiopathic Pulmonary Fibrosis:Cause or Solution

Idiopathic pulmonary fibrosis (IPF) is one of the most aggressive forms of idiopathic interstitial pneumonias, characterized by chronic and progressive fibrosis subverting the lung’s architecture, pulmonary functional decline, progressive respiratory failure, and high mortality (median survival 3 years after diagnosis). Among the mechanisms associated with disease onset and progression, it has been hypothesized that IPF lungs might be affected either by a regenerative deficit of the alveolar epithelium or by a dysregulation of repair mechanisms in response to alveolar and vascular damage. This latter might be related to the progressive dysfunction and exhaustion of the resident stem cells together with a process of cellular and tissue senescence. The role of endogenous mesenchymal stromal/stem cells (MSCs) resident in the lung in the homeostasis of these mechanisms is still a matter of debate. Although endogenous MSCs may play a critical role in lung repair, they are also involved in cellular senescence and tissue ageing processes with loss of lung regenerative potential. In addition, MSCs have immunomodulatory properties and can secrete anti-fibrotic factors. Thus, MSCs obtained from other sources administered systemically or directly into the lung have been investigated for lung epithelial repair and have been explored as a potential therapy for the treatment of lung diseases including IPF. Given these multiple potential roles of MSCs, this review aims both at elucidating the role of resident lung MSCs in IPF pathogenesis and the role of administered MSCs from other sources for potential IPF therapies

Interacting climate change factors (CO2 and temperature cycles) effects on growth, secondary metabolite gene expression and phenotypic ochratoxin A production by Aspergillus carbonarius strains on a grape-based matrix

Little is known on the impact that climate change (CC) may have on Aspergillus carbonarius and Ochratoxin A (OTA) contamination of grapes, especially in the Mediterranean region where in CC scenarios temperature are expected to increase by +2–5 °C and CO2 from 400 to 800/1200 ppm. This study examined the effect of (i) current and increased temperature in the alternating 11.5 h dark/12.5 h light cycle (15–28 °C vs 18–34 °C), representative of the North Apulia area, South Italy and (ii) existing and predicted CO2 concentrations (400 vs 1000 ppm), on growth, expression of biosynthetic genes (AcOTApks, AcOTAnrps, AcOTAhal, AcOTAp450, AcOTAbZIP) and regulatory genes of Velvet complex (laeA/veA/velB, “velvet complex”) involved in OTA biosynthesis and OTA phenotypic production by three strains of A. carbonarius. The experiments made on a grape-based matrix showed that elevated CO2 resulted in a general stimulation of growth and OTA production. These results were also supported by the up-regulation of both structural and regulatory genes involved in the OTA biosynthesis. Our work has shown for the first time that elevated CO2 concentration in the Mediterranean region may result in an increased risk of OTA contamination in the wine production chain

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. The paper presents the CONTREX European project and its preliminary results. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample