[Show abstract][Hide abstract] 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; · 3.93 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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
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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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: email@example.com.
[Show abstract][Hide abstract] 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; · 1.35 Impact Factor
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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.
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.
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).
In ARDS the alveolar pool of surfactant is reduced and disaturated-phosphatidylcholine turnover is altered.
[Show abstract][Hide abstract] 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.