Explorative visual analytics on interval-based genomic data and their metadata

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSEunder GPLv3 open-source license

Fungal volatile organic compounds: emphasis on their plant growth-promoting

Fungal volatile organic compounds (VOCs) commonly formed bioactive interface between plants and countless of microorganisms on the above- and below-ground plant-fungus interactions. Fungal-plant interactions symbolize intriguingly biochemical complex and challenging scenarios that are discovered by metabolomic approaches. Remarkably secondary metabolites (SMs) played a significant role in the virulence and existence with plant-fungal pathogen interaction; only 25% of the fungal gene clusters have been functionally identified, even though these numbers are too low as compared with plant secondary metabolites. The current insights on fungal VOCs are conducted under lab environments and to apply small numbers of microbes; its molecules have significant effects on growth, development, and defense system of plants. Many fungal VOCs supported dynamic processes, leading to countless interactions between plants, antagonists, and mutualistic symbionts. The fundamental role of fungal VOCs at field level is required for better understanding, so more studies will offer further constructive scientific evidences that can show the cost-effectiveness of ecofriendly and ecologically produced fungal VOCs for crop welfare

Distinctive expansion of gene families associated with plant cell wall degradation, secondary metabolism, and nutrient uptake in the genomes of grapevine trunk pathogens

BackgroundTrunk diseases threaten the longevity and productivity of grapevines in all viticulture production systems. They are caused by distantly-related fungi that form chronic wood infections. Variation in wood-decay abilities and production of phytotoxic compounds are thought to contribute to their unique disease symptoms. We recently released the draft sequences of Eutypa lata, Neofusicoccum parvum and Togninia minima, causal agents of Eutypa dieback, Botryosphaeria dieback and Esca, respectively. In this work, we first expanded genomic resources to three important trunk pathogens, Diaporthe ampelina, Diplodia seriata, and Phaeomoniella chlamydospora, causal agents of Phomopsis dieback, Botryosphaeria dieback, and Esca, respectively. Then we integrated all currently-available information into a genome-wide comparative study to identify gene families potentially associated with host colonization and disease development.ResultsThe integration of RNA-seq, comparative and ab initio approaches improved the protein-coding gene prediction in T. minima, whereas shotgun sequencing yielded nearly complete genome drafts of Dia. ampelina, Dip. seriata, and P. chlamydospora. The predicted proteomes of all sequenced trunk pathogens were annotated with a focus on functions likely associated with pathogenesis and virulence, namely (i) wood degradation, (ii) nutrient uptake, and (iii) toxin production. Specific patterns of gene family expansion were described using Computational Analysis of gene Family Evolution, which revealed lineage-specific evolution of distinct mechanisms of virulence, such as specific cell wall oxidative functions and secondary metabolic pathways in N. parvum, Dia. ampelina, and E. lata. Phylogenetically-informed principal component analysis revealed more similar repertoires of expanded functions among species that cause similar symptoms, which in some cases did not reflect phylogenetic relationships, thereby suggesting patterns of convergent evolution.ConclusionsThis study describes the repertoires of putative virulence functions in the genomes of ubiquitous grapevine trunk pathogens. Gene families with significantly faster rates of gene gain can now provide a basis for further studies of in planta gene expression, diversity by genome re-sequencing, and targeted reverse genetic approaches. The functional validation of potential virulence factors will lead to a more comprehensive understanding of the mechanisms of pathogenesis and virulence, which ultimately will enable the development of accurate diagnostic tools and effective disease management

Aging-related tau astrogliopathy (ARTAG):harmonized evaluation strategy

Pathological accumulation of abnormally phosphorylated tau protein in astrocytes is a frequent, but poorly characterized feature of the aging brain. Its etiology is uncertain, but its presence is sufficiently ubiquitous to merit further characterization and classification, which may stimulate clinicopathological studies and research into its pathobiology. This paper aims to harmonize evaluation and nomenclature of aging-related tau astrogliopathy (ARTAG), a term that refers to a morphological spectrum of astroglial pathology detected by tau immunohistochemistry, especially with phosphorylation-dependent and 4R isoform-specific antibodies. ARTAG occurs mainly, but not exclusively, in individuals over 60 years of age. Tau-immunoreactive astrocytes in ARTAG include thorn-shaped astrocytes at the glia limitans and in white matter, as well as solitary or clustered astrocytes with perinuclear cytoplasmic tau immunoreactivity that extends into the astroglial processes as fine fibrillar or granular immunopositivity, typically in gray matter. Various forms of ARTAG may coexist in the same brain and might reflect different pathogenic processes. Based on morphology and anatomical distribution, ARTAG can be distinguished from primary tauopathies, but may be concurrent with primary tauopathies or other disorders. We recommend four steps for evaluation of ARTAG: (1) identification of five types based on the location of either morphologies of tau astrogliopathy: subpial, subependymal, perivascular, white matter, gray matter; (2) documentation of the regional involvement: medial temporal lobe, lobar (frontal, parietal, occipital, lateral temporal), subcortical, brainstem; (3) documentation of the severity of tau astrogliopathy; and (4) description of subregional involvement. Some types of ARTAG may underlie neurological symptoms; however, the clinical significance of ARTAG is currently uncertain and awaits further studies. The goal of this proposal is to raise awareness of astroglial tau pathology in the aged brain, facilitating communication among neuropathologists and researchers, and informing interpretation of clinical biomarkers and imaging studies that focus on tau-related indicators

Quantitative EEG as a predictive biomarker for Parkinson disease dementia

OBJECTIVE: We evaluated quantitative EEG (QEEG) measures as predictive biomarkers for the development of dementia in Parkinson disease (PD). Preliminary work shows that QEEG measures correlate with current PD cognitive state. A reliable predictive QEEG biomarker for PD dementia (PD-D) incidence would be valuable for studying PD-D, including treatment trials aimed at preventing cognitive decline in PD. METHODS: A cohort of subjects with PD in our brain donation program utilizes annual premortem longitudinal movement and cognitive evaluation. These subjects also undergo biennial EEG recording. EEG from subjects with PD without dementia with follow-up cognitive evaluation was analyzed for QEEG measures of background rhythm frequency and relative power in δ, θ, α, and β bands. The relationship between the time to onset of dementia and QEEG and other possible predictors was assessed by using Cox regression. RESULTS: The hazard of developing dementia was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p < 0.001). Hazard ratios (HRs) were also significant for > median θ bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for δ, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant. CONCLUSION: The QEEG measures of background rhythm frequency and relative power in the θ band are potential predictive biomarkers for dementia incidence in PD. These QEEG biomarkers may be useful in complementing neuropsychological testing for studying PD-D incidence

Quantitative EEG as a predictive biomarker for Parkinson disease dementia

Objective: We evaluated quantitative EEG (QEEG) measures as predictive biomarkers for the development of dementia in Parkinson disease (PD). Preliminary work shows that QEEG measures correlate with current PD cognitive state. A reliable predictive QEEG biomarker for PD dementia (PD-D) incidence would be valuable for studying PD-D, including treatment trials aimed at preventing cognitive decline in PD. Methods: A cohort of subjects with PD in our brain donation program utilizes annual premortem longitudinal movement and cognitive evaluation. These subjects also undergo biennial EEG recording. EEG from subjects with PD without dementia with follow-up cognitive evaluation was analyzed for QEEG measures of background rhythm frequency and relative power in ω, θ, α, and β bands. The relationship between the time to onset of dementia and QEEG and other possible predictors was assessed by using Cox regression. Results: The hazard of developing dementia was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p \u3c 0.001). Hazard ratios (HRs) were also significant for \u3e median θ bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for ω, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant. Conclusion: The QEEG measures of background rhythm frequency and relative power in the θ band are potential predictive biomarkers for dementia incidence in PD. These QEEG biomarkers may be useful in complementing neuropsychological testing for studying PD-D incidence. © 2011 by AAN Enterprises, Inc

Both early and late cognitive dysfunction affects the electroencephalogram in Parkinson\u27s disease

We sought to define quantitative electroencephalographic (EEG) measures as biomarkers of both early and late cognitive decline in Parkinson\u27s disease (PD). PD subjects classified as cognitively normal (PD-CogNL), mild cognitive impairment (PD-MCI), and dementia (PD-D) were studied. Cognitive status and neuropsychological testing was correlated with background rhythm and frequency band EEG power across five frequency bands. We conclude that global EEG measures have potential use as biomarkers in the study of both early and late cognitive deterioration in PD, including for evaluating its treatment. PD-MCI has mean quantitative EEG characteristics that represent an intermediate electrophysiological state between PD-CogNL and PD-D. © 2007 Elsevier Ltd. All rights reserved

An autosomal dominant ataxia maps to 19q13: Allelic heterogeneity of SCA13 or novel locus?

The autosomal dominant spinocerebellar ataxias (ADCAs) represent a growing and heterogeneous disease phenotype. Clinical characterization of a three-generation Filipino family segregating a dominant ataxia revealed cerebellar signs and symptoms. After elimination of known spinocerebellar ataxia (SCA) loci, a genome-wide linkage scan revealed a disease locus in a 4-cM region of 19q13, with a 3.89 lod score. This region overlaps and reduces the SCA13 locus. However, this ADCA is clinically distinguishable from SCA13