Gianna Toffolo

University of Padova, Padua, Veneto, Italy

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Publications (122)570.15 Total impact

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    ABSTRACT: Mass spectrometry-based proteomics can generate highly informative datasets, as profile three-dimensional (3D) LC-MS data: LC-MS separates peptides in two dimensions (time, m/z) minimizing their overlap, and profile acquisition enhances quantification. To exploit both data features, we developed 3DSpectra, a 3D approach embedding a statistical method for peptide border recognition. 3DSpectra efficiently accesses profile data by means of mzRTree, and makes use of a priori metadata, provided by search engines, to quantify the identified peptides. An isotopic distribution model, shaped by a bivariate Gaussian Mixture Model (GMM), which includes a noise component, is fitted to the peptide peaks using the Expectation-Maximization (EM) approach. The EM starting parameters, i.e., the centers and shapes of the Gaussians, are retrieved from the metadata. The borders of the peaks are delimited by the GMM iso-density curves, and noisy or outlying data are discarded from subsequent analysis. The 3DSpectra program was compared to ASAPRatio for a controlled mixture of Isotope-Coded Protein Labels (ICPL) labeled proteins, which were mixed at predefined ratios and acquired in enhanced profile mode, in triplicate. The 3DSpectra software showed significantly higher linearity, quantification accuracy, and precision than did ASAPRatio in this real use case simulation where the true ratios are known, and it also achieved wider peptide coverage and dynamic range.
    Journal of proteomics. 09/2014;
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    ABSTRACT: The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped SNPs. This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data.
    Bioinformatics (Oxford, England). 07/2014;
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    ABSTRACT: Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
    Nature Biotechnology 06/2014; · 32.44 Impact Factor
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    ABSTRACT: AIMS/HYPOTHESIS: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD. METHODS: We exploited a novel algorithm, 'Bag of Naive Bayes', whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK-Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US). RESULTS: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case-control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno. CONCLUSIONS/INTERPRETATION: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.
    Diabetologia 05/2014; · 6.49 Impact Factor
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    ABSTRACT: The glucose story begins with Claude Bernard's discovery of glycogen and milieu interieur, continued with Banting's and Best's discovery of insulin and with Rudolf Schoenheimer's paradigm of dynamic body constituents. Tracers and compartmental models allowed moving to the first quantitative pictures of the system and stimulated important developments in terms of modeling methodology. Three classes of multiscale models, models to measure, models to simulate, and models to control the glucose system, are reviewed in their historical development with an eye to the future.
    IEEE transactions on bio-medical engineering 05/2014; 61(5):1577-92. · 2.15 Impact Factor
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    ABSTRACT: The neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) have been mostly investigated by peripheral motor-evoked potentials (MEPs). New TMS-compatible EEG systems allow a direct investigation of the stimulation effects through the analysis of TMS-evoked potentials (TEPs). We investigated the effects of 1-Hz rTMS over the primary motor cortex (M1) of 15 healthy volunteers on TEP evoked by single pulse TMS over the same area. A second experiment in which rTMS was delivered over the primary visual cortex (V1) of 15 healthy volunteers was conducted to examine the spatial specificity of the effects. Single-pulse TMS evoked four main components: P30, N45, P60 and N100. M1-rTMS resulted in a significant decrease of MEP amplitude and in a significant increase of P60 and N100 amplitude. Such effect was not presented after V1-rTMS. 1-hz rTMS had increased the amount of inhibition following a TMS pulse, as demonstrated by the higher N100 and P60, which are supposed to origin from the GABAb-mediated inhibitory post-synaptic potentials. Our results confirm the reliability of TMS-evoked N100 as a marker of cortical inhibition amount and provide insight into the neuromodulatory effects of 1-Hz rTMS. The present finding could be of relevance for therapeutic and diagnostic purposes.
    NeuroImage 05/2014; · 6.25 Impact Factor
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    ABSTRACT: The simultaneous assessment of insulin action, secretion, and hepatic extraction is key to understanding postprandial glucose metabolism in nondiabetic and diabetic humans. We review the oral minimal method (i.e., models that allow the estimation of insulin sensitivity, β-cell responsivity, and hepatic insulin extraction from a mixed-meal or an oral glucose tolerance test). Both of these oral tests are more physiologic and simpler to administer than those based on an intravenous test (e.g., a glucose clamp or an intravenous glucose tolerance test). The focus of this review is on indices provided by physiological-based models and their validation against the glucose clamp technique. We discuss first the oral minimal model method rationale, data, and protocols. Then we present the three minimal models and the indices they provide. The disposition index paradigm, a widely used β-cell function metric, is revisited in the context of individual versus population modeling. Adding a glucose tracer to the oral dose significantly enhances the assessment of insulin action by segregating insulin sensitivity into its glucose disposal and hepatic components. The oral minimal model method, by quantitatively portraying the complex relationships between the major players of glucose metabolism, is able to provide novel insights regarding the regulation of postprandial metabolism.
    Diabetes 04/2014; 63(4):1203-13. · 7.90 Impact Factor
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    ABSTRACT: Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events. Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3beta) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3beta, with a significant increase at 100 muM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3beta and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3beta, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition. These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3beta phosphorylation and glycogen synthesis.
    BMC Cell Biology 03/2014; 15(1):9. · 2.81 Impact Factor
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    ABSTRACT: The experimental protocol of the perfused rat pancreas is commonly used to evaluate beta-cell function. In this context mathematical models become useful tools through the determination of indexes that allow the assessment of beta-cell function in different experimental groups, and the quantification of the effects of anti-diabetic drugs, secretagogues, or treatments. However, a minimal model applicable to the isolated perfused rat pancreas was unavailable so far. In this work, we adapt the C-peptide minimal model, previously applied to the intravenous glucose tolerance test, to obtain a specific model for the experimental settings of the perfused pancreas. Using the model, it is possible to estimate indexes describing beta-cell responsivity for first (ΦD) and second phase (ΦS, T) of insulin secretion. The model was initially applied to untreated pancreata, and afterwards used for the assessment of pharmacologically relevant agents (the gut hormone GLP1, the potent GLP1-receptor agonist, lixisenatide, and a GPR40/FFAR1 agonist, SAR1), to quantify and differentiate their effect on insulin secretion. Model fit was satisfactory and parameters were estimated with good precision for both untreated and treated pancreata. Model application showed that lixisenatide reaches improvement of beta-cell function similar to GLP1 (11.7 vs 13.1 fold increase in ΦD and 2.3 vs 2.8 fold increase in ΦS) and demonstrated that SAR1 leads to an additional improvement of beta-cell function even in the presence of postprandial GLP1 levels.
    AJP Endocrinology and Metabolism 01/2014; · 4.51 Impact Factor
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    ABSTRACT: In the last years, both sequencing and microarray have been widely used to search for relations between genetic variations and predisposition to complex pathologies such as diabetes or neurological disorders. These studies, however, have been able to explain only a small fraction of disease heritability, possibly because complex pathologies cannot be referred to few dysfunctional genes, but are rather heterogeneous and multi-causal, as a result of a combination of rare and common variants possibly impairing multiple regulatory pathways. Rare variants, though, are difficult to detect, especially when the effects of causal variants are in different directions, i.e. with protective and detrimental effects. Here we propose ABACUS, an Algorithm based on a BivAriate CUmulative Statistic to identify SNPs significantly associated with a disease within predefined sets of SNPs such as pathways or genomic regions. ABACUS is robust to the concurrent presence of SNPs with protective and detrimental effects and of common and rare variants; moreover it is powerful even when few SNPs in the SNP-set are associated with the phenotype. We assessed ABACUS performance on simulated and real data and compared it with three state-of-the-art methods. When ABACUS was applied to type 1 and 2 diabetes data, besides observing a wide overlap with already known associations, we found a number of biologically sound pathways, which might shed light on diabetes mechanism and etiology. ABACUS is available at http://www.dei.unipd.it/~dicamill/pagine/Software.html CONTACT: barbara.dicamillo@dei.unipd.it.
    Bioinformatics 11/2013; · 5.47 Impact Factor
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    ABSTRACT: Among other neuroimaging techniques, functional magnetic resonance imaging (fMRI) can be useful for studying the development of motor fatigue. The aim of this study was to identify differences in cortical neuronal activation in nine subjects on three motor tasks: right-hand movement with minimum, maximum, and post-fatigue maximum finger flexion. fMRI activation maps for each subject and during each condition were obtained by estimating the optimal model of the hemodynamic response function (HRF) out of four standard HRF models and an individual-based HRF model (ibHRF). ibHRF was selected as the optimal model in six out of nine subjects for minimum movement, in five out of nine for maximum movement, and in eight out of nine for post-fatigue maximum movement. As compared to maximum movement, a large reduction in the total number of active voxels (primary sensorimotor area, supplementary motor area and cerebellum) was observed in post-fatigue maximum movement. This is the first approach to the evaluation of long-lasting contraction effort in healthy subjects by means of the fMRI paradigm with the use of an individual-based hemodynamic response. The results may be relevant for defining a baseline in future studies on central fatigue in patients with neuropathological disorders.
    MAGMA Magnetic Resonance Materials in Physics Biology and Medicine 09/2013; · 1.86 Impact Factor
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    ABSTRACT: Electroencephalography and functional magnetic resonance imaging (fMRI) can be combined to noninvasively map abnormal brain activation elicited by epileptic processes. A major aim was to investigate the impact of a subject-specific hemodynamic response function (HRF) to describe the differences across patients versus the use of a standard model. We developed and applied on simulated and real data a method designed to choose optimum HRF model for identifying fMRI activation maps. In simulation, the ability of five models to reproduce data was assessed: four standard and an individual-based HRF model (ibHRF). In clinical data, drug-resistant epileptic patients underwent fMRI to investigate hemodynamic responses evoked by interictal activity. When data are simulated with models different from the standard ones, the results obtained with ibHRF are superior to those obtained with the standard HRFs. Results on real data indicate an increase in extent and degree of activation with the ibHRF in comparison of the results obtainable using standard HRFs. The use of the same HRF in all patients is inappropriate and resolves in biased extension of the activation maps. The new method could represent an useful diagnostic tool for other clinical studies that may be biased because of misspecification of HRF.
    Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 07/2013; · 3.12 Impact Factor
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    ABSTRACT: Background In patients with acute respiratory distress syndrome (ARDS), it is well known that only part of the lungs is aerated and surfactant function is impaired, but the extent of lung damage and changes in surfactant turnover remain unclear. The objective of the study was to evaluate surfactant disaturated-phosphatidylcholine turnover in patients with ARDS using stable isotopes. Methods We studied 12 patients with ARDS and 7 subjects with normal lungs. After the tracheal instillation of a trace dose of 13C-dipalmitoyl-phosphatidylcholine, we measured the 13C enrichment over time of palmitate residues of disaturated-phosphatidylcholine isolated from tracheal aspirates. Data were interpreted using a model with two compartments, alveoli and lung tissue, and kinetic parameters were derived assuming that, in controls, alveolar macrophages may degrade between 5 and 50% of disaturated-phosphatidylcholine, the rest being lost from tissue. In ARDS we assumed that 5–100% of disaturated-phosphatidylcholine is degraded in the alveolar space, due to release of hydrolytic enzymes. Some of the kinetic parameters were uniquely determined, while others were identified as lower and upper bounds. Results In ARDS, the alveolar pool of disaturated-phosphatidylcholine was significantly lower than in controls (0.16 ± 0.04 vs. 1.31 ± 0.40 mg/kg, p < 0.05). Fluxes between tissue and alveoli and de novo synthesis of disaturated-phosphatidylcholine were also significantly lower, while mean resident time in lung tissue was significantly higher in ARDS than in controls. Recycling was 16.2 ± 3.5 in ARDS and 31.9 ± 7.3 in controls (p = 0.08). Conclusion In ARDS the alveolar pool of surfactant is reduced and disaturated-phosphatidylcholine turnover is altered.
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    ABSTRACT: Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
    Nature Methods 02/2013; · 23.57 Impact Factor
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    ABSTRACT: In non-pulsatile cardiopulmonary bypass surgery, middle cerebral artery blood flow velocity (BFV) is characterized by infra-slow oscillations of approximately 0.06Hz, which are paralleled by changes in total EEG power variability (EEG-PV), measured in 2s intervals. Since the origin of these BFV oscillations is not known, we explored their possible causative relationships with oscillations in EEG-PV at around 0.06Hz. We monitored 28 patients undergoing non-pulsatile cardiopulmonary bypass using transcranial Doppler sonography and scalp electroencephalography at two levels of anesthesia - deep (prevalence of burst suppression rhythm) and moderate (prevalence of theta rhythm). Under deep anesthesia, the EEG bursts suppression pattern was highly correlative with BFV oscillations. Hence, a detailed quantitative picture of the coupling between electrical brain activity and BFV was derived, both in deep and moderate anesthesia, via linear and non linear processing of EEG-PV and BFV signals, resorting to widely used measures of signal coupling such as frequency of oscillations, coherence, Granger causality and cross-approximate entropy. Results strongly suggest the existence of coupling between EEG-PV and BFV. In moderate anesthesia EEG-PV mean dominant frequency is similar to frequency of BFV oscillations (0.065±0.010Hz vs 0.045±0.019Hz); coherence between the two signals was significant in about 55% of subjects, and the Granger causality suggested an EEG-PV■BFV causal effect direction. The strength of the coupling increased with deepening anesthesia, as EEG-PV oscillations mean dominant frequency virtually coincided with the BFV peak frequency (0.062±0.017Hz vs 0.060±0.024Hz), and coherence became significant in a larger number (65%) of subjects. Cross-approximate entropy decreased significantly from moderate to deep anesthesia, indicating a higher level of synchrony between the two signals. Presence of a subcortical brain pacemaker that drives vascular infra-slow oscillations in the brain is proposed. These findings allow to suggest an original hypothesis explaining the mechanism underlying infra-slow neurovascular coupling.
    NeuroImage 01/2013; · 6.25 Impact Factor
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    ABSTRACT: Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥92%) and the occipital network, which includes the medial visual cortical areas (≥94%), and consistent for the attention network (≥80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.
    Frontiers in Neuroscience 01/2013; 7:72.
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    ABSTRACT: Electrocortical stimulation remains the standard for functional brain mapping of eloquent areas to prevent postoperative functional deficits. The aim of this study was to investigate whether the short-train technique (monopolar stimulation) and Penfield's technique (bipolar stimulation) would induce different effects on brain oscillatory activity in awake patients, as quantified by electrocorticography (ECoG). The study population was seven patients undergoing brain tumor surgery. Intraoperative bipolar and monopolar electrical stimulation for cortical mapping was performed during awake surgery. ECoG was recorded using 1 × 8 electrode strip. Spectral estimation was calculated using a parametric approach based on an autoregressive model. Wavelet-based time-frequency analysis was then applied to evaluate the temporal evolution of brain oscillatory activity. Both monopolar and bipolar stimulation produced an increment in delta and a decrease in beta powers for the motor and the sensory channels. These phenomena lasted about 4 s. Comparison between monopolar and bipolar stimulation showed no significant difference in brain activity. Given the importance of quantitative signal analysis for evaluating response accuracy, ECoG recording during electrical stimulation is necessary to characterize the dynamic processes underlying changes in cortical responses in vivo. This study is a preliminary approach to the quantitative analysis of post-stimulation ECoG signals.
    Frontiers in Neuroengineering 01/2013; 6:1.

Publication Stats

2k Citations
570.15 Total Impact Points

Institutions

  • 1984–2014
    • University of Padova
      • • Department of Information Engineering
      • • Dipartimento di Scienze Mediche e Chirurgiche
      Padua, Veneto, Italy
  • 2012–2013
    • University of Verona
      • • Department of Neurological and Movement Sciences
      • • Department of Neurological, Neuropsychological, Morphological and Movement Sciences
      Verona, Veneto, Italy
  • 2008–2011
    • University of California, Los Angeles
      • Division of Endocrinology
      Los Angeles, California, United States
  • 2003–2011
    • Mayo Clinic - Rochester
      • Department of Endocrinology, Diabetes, Metabolism and Nutrition
      Rochester, Minnesota, United States
  • 2006–2010
    • Mayo Foundation for Medical Education and Research
      • Division of Endocrinology, Diabetes, Metabolism, and Nutrition
      Scottsdale, AZ, United States
  • 2005–2010
    • Baylor College of Medicine
      • • Department of Pediatrics
      • • Children's Nutrition Research Center
      Houston, TX, United States
  • 2004
    • University of Pavia
      Ticinum, Lombardy, Italy
  • 1987
    • Yale-New Haven Hospital
      New Haven, Connecticut, United States