Characterization of the yeast flora on the surface of grape berries in Israel

Yeast populations were collected from the surface of berries of three grape cultivars during three seasons, from fruit set to maturity. They were studied by RAPD and ap-PCR, each with two primer pairs. In the population, identical isolates were found only rarely on 13 % of the bunches in 1997 and on 58 % of the berries in 1999. From RAPD and ap-PCR, a dendrogram with clusters of similarity was established. Eleven representatives from clusters of the white yeast dendrogram were identified by traditional methods as 10 different yeast species, one of which has not been isolated from grape berry surfaces before. The population size was smaller for Colombard than for Cabernet Sauvignon and Muscat of Alexandria berries.

Metagenomics approaches for the detection and surveillance of emerging and recurrent plant pathogens

Globalization has a dramatic effect on the trade and movement of seeds, fruits and vegetables, with a corresponding increase in economic losses caused by the introduction of transboundary plant pathogens. Current diagnostic techniques provide a useful and precise tool to enact surveillance protocols regarding specific organisms, but this approach is strictly targeted, while metabarcoding and shotgun metagenomics could be used to simultaneously detect all known pathogens and potentially new ones. This review aims to present the current status of high-throughput sequencing (HTS) diagnostics of fungal and bacterial plant pathogens, discuss the challenges that need to be addressed, and provide direction for the development of methods for the detection of a restricted number of related taxa (specific surveillance) or all of the microorganisms present in a sample (general surveillance). HTS techniques, particularly metabarcoding, could be useful for the surveillance of soilborne, seedborne and airborne pathogens, as well as for identifying new pathogens and determining the origin of outbreaks. Metabarcoding and shotgun metagenomics still suffer from low precision, but this issue can be limited by carefully choosing primers and bioinformatic algorithms. Advances in bioinformatics will greatly accelerate the use of metagenomics to address critical aspects related to the detection and surveillance of plant pathogens in plant material and foodstuffs

Functional connectivity analysis using whole brain and regional network metrics in MS patients

In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing

Continuous short-term structural network reorganisation beyond atrophy in patients with RRMS [Abstract]

Background and aim: Longitudinal assessment of structural brain changes is important to track the clinical course of multiple sclerosis (MS), but an exact quantification of the diffuse tissue damage is highly challenging. We aimed to identify short-term structural dynamics by measuring grey matter (GM) network connectivity patterns and comparing these with established morphological measures of GM integrity. Methods: For our prospectively designed study, we collected data from January 2013 through December 2014. In total, forty-five structural MRI datasets from relapsing-remitting MS patients in the relapse free phase of the disease (mean age: 42 ± 12.1 years; median EDSS 1.5 (0 - 2.5); mean disease duration 3.5 ± 6.5 years) were acquired using 3T MRI. Each patient was followed up every 8 weeks for 8 months and all patients were enrolled at two German university hospitals. Longitudinal brain atrophy was analyzed using SIENA (part of FSL), while FreeSurfer was used to investigate cortical thickness changes over time. GM connectivity patterns were reconstructed from cortical thickness correlation matrix between anatomical regions, as derived from the AAL atlas, and a network analysis was conducted using graph theoretical approaches. Results: Our study shows a significant longitudinal structural network reorganisation in the absence of cortical thinning and brain atrophy already over a period of 4 months. We demonstrate an increased local (clustering coefficient (F(4,41) = 3.547, p < 0.001), local efficiency (F(4,41) = 3.0874, p < 0.01)) and modular connectivity pattern (modularity (F(4,41) =2.612, p < 0.01)). Conversely a concomitant break-down of long-range connectivity occurred (assortativity (F(4,41) = 3.0654, p < 0.01) and small-world index (F(4,41) = 3.687, p < 0.001)). No regional or global atrophy signs were detected in the applied morphometric analysis. Conclusions and relevance: Our GM network analysis demonstrates a short-term increase in local connectivity and a decrease in long-range paths in MS patients in the relapse free state of the disease, in the absence of atrophy or clinical progression. Structural reorganisation patterns with co-occurrence of detrimental and adaptive reorganisation processes might be important sensitive measurable fingerprints of the disease that can be used in clinical practice

Longitudinal structural network reorganisation in early relapsing-remitting multiple sclerosis [Abstract]

Background: Multiple sclerosis (MS) is characterized by relapses and remissions indicating damage and compensatory processes occurring early in the disease. Over time, cortical pathology is highly relevant for disability, while brain networks evolve towards a disconnected organization as the disease progresses. However, it is poorly understood how and when pathology impacts cortical networks and in particular, how the network responds to damage in the very beginning of the disease. Aim: To address cortical pathology by quantifying structural connectivity patterns over 12 months in patients with early relapsing-remitting MS. Methods: Here we investigated cortical grey matter networks longitudinally as derived from structural 3 Tesla MRI in 92 patients in the initial phase of the disease (65 female / 27 male; mean age: 32.9 ± 9.9 years; mean disease duration: 12.1 ± 14.5 months) and in 101 healthy controls (59 female / 42 male; mean age: 19.7 ± 0.9 years). Longitudinal brain volume atrophy was analyzed using SIENA and cortical thickness changes were quantified using FreeSurfer. Brain networks were computed based on cortical thickness inter-regional correlations between anatomical regions and fed into graph theoretical analysis. Finally, subgroup analyses were performed between patients with “no evidence of disease activity” (NEDA) during this period and those with disease activity (EDA). Results: Over one year, increased local cortical connectivity and an emerging modular-constructed network were detected in patients - a pattern reported to be associated with adaptation, efficiency and compensation. These longitudinal dynamics were attested in both patients with NEDA and EDA, indicating continuous cortical reorganisation independent of disease activity. This local and modular cortical reorganisation was not detected in healthy controls over the same period of time and emerged beyond measureable signs of atrophy using established morphometric tools. Conclusion: Our findings demonstrate that despite initiation of neuroinflammatory damage, substantial structural adaptation processes emerge cortically in the early disease stage. This subtle reorganisation of the cortex architecture is quantifiable by structural MRI in patients with and without disease activity, suggesting a principal response of the network evolving from the onset of this chronic disease. Disclosure: The authors declare no conflict of interests

Continuous reorganisation of cortical information flow in MS patients: a longitudinal effective connectivity study [Abstract]

Background: Brain reorganisation processes are essential for the long-term outcome in patients with multiple sclerosis (MS). Effective connectivity (EC) as derived from functional MRI, can be analysed to estimate reorganisation processes and directional information flows between cortical regions. These measures could provide the missing link for modelling the long-term disease course between tissue damage and repair or adaptation. Aim: To obtain longitudinal measurements of EC and information flows in MS patients at short-term intervals focusing on the main anatomical brain regions and to investigate the link between the connectivity strength and clinical impairment. Methods: Twelve MS patients (mean age: 41.7 ± 11.5 years) underwent 3 Tesla structural and resting state functional MRI at five different time points over one year (approximately every 12 weeks). Twelve healthy subjects (mean age: 33.5 ± 9.6 years) served as controls (HC). For the analytical framework, two novel approaches for EC quantification were used. Causal Bayesian Network (CBN) and Time Domain Partial Directed Coherence (TPDC) were applied for the description of the information flows between frontal, prefrontal, temporal, occipital, and parietal lobe; cerebellum and deep grey matter nuclei (DGMN) were also analysed. Results: Specific longitudinal EC patterns have been attested in the studied regions. Information flows from DGMN, frontal, prefrontal and temporal to the other studied regions showed a continuous increase over time, whereas the directed connections from parietal and occipital lobes and from the cerebellum did not change over time as confirmed by both applied methods. No longitudinal changes of EC were attested in HC. The longitudinal connectivity increase in the prefrontal-frontal and fronto-cerebellar pathway showed a significant inverse correlation to EDSS (Expanded Disability Status Scale). Moreover, the EC change from the frontal lobe to the cerebellum showed a significant inverse correlation to patients’ fatigue score. Conclusion: Our data depicts a continuous longitudinal increase in EC in patients with MS substantiated by two novel methodological approaches. Furthermore, the dynamics of the fronto-cerebellar connections are linked to clinical impairment and possibly essential for the long-term outcome

IRS-2 Deficiency Impairs NMDA Receptor-Dependent Long-term Potentiation

The beneficial effects of insulin and insulin-like growth factor I on cognition have been documented in humans and animal models. Conversely, obesity, hyperinsulinemia, and diabetes increase the risk for neurodegenerative disorders including Alzheimer's disease (AD). However, the mechanisms by which insulin regulates synaptic plasticity are not well understood. Here, we report that complete disruption of insulin receptor substrate 2 (Irs2) in mice impairs long-term potentiation (LTP) of synaptic transmission in the hippocampus. Basal synaptic transmission and paired-pulse facilitation were similar between the 2 groups of mice. Induction of LTP by high-frequency conditioning tetanus did not activate postsynaptic N-methyl-D-aspartate (NMDA) receptors in hippocampus slices from Irs2−/− mice, although the expression of NR2A, NR2B, and PSD95 was equivalent to wild-type controls. Activation of Fyn, AKT, and MAPK in response to tetanus stimulation was defective in Irs2−/− mice. Interestingly, IRS2 was phosphorylated during induction of LTP in control mice, revealing a potential new component of the signaling machinery which modulates synaptic plasticity. Given that IRS2 expression is diminished in Type 2 diabetics as well as in AD patients, these data may reveal an explanation for the prevalence of cognitive decline in humans with metabolic disorders by providing a mechanistic link between insulin resistance and impaired synaptic transmission