Modeling SILAC Data to Assess Protein Turnover in a Cellular Model of Diabetic Nephropathy.

Protein turnover rate is finely regulated through intracellular mechanisms and signals that are still incompletely understood but that are essential for the correct function of cellular processes. Indeed, a dysfunctional proteostasis often impacts the cell's ability to remove unfolded, misfolded, degraded, non-functional, or damaged proteins. Thus, altered cellular mechanisms controlling protein turnover impinge on the pathophysiology of many diseases, making the study of protein synthesis and degradation rates an important step for a more comprehensive understanding of these pathologies. In this manuscript, we describe the application of a dynamic-SILAC approach to study the turnover rate and the abundance of proteins in a cellular model of diabetic nephropathy. We estimated protein half-lives and relative abundance for thousands of proteins, several of which are characterized by either an altered turnover rate or altered abundance between diabetic nephropathic subjects and diabetic controls. Many of these proteins were previously shown to be related to diabetic complications and represent therefore, possible biomarkers or therapeutic targets. Beside the aspects strictly related to the pathological condition, our data also represent a consistent compendium of protein half-lives in human fibroblasts and a rich source of important information related to basic cell biology.

Stochastic modeling of human papillomavirusearly promoter gene regulation.

High risk forms of human papillomaviruses (HPVs) promote cancerous lesions and are implicated in almost all cervical cancer. Of particular relevance to cancer progression is regulation of the early promoter that controls gene expression in the initial phases of infection and can eventually lead to pre-cancer progression. Our goal was to develop a stochastic model to investigate the control mechanisms that regulate gene expression from the HPV early promoter. Our model integrates modules that account for transcriptional, post-transcriptional, translational and post-translational regulation of E1 and E2 early genes to form a functioning gene regulatory network. Each module consists of a set of biochemical steps whose stochastic evolution is governed by a chemical Master Equation and can be simulated using the Gillespie algorithm. To investigate the role of noise in gene expression, we compared our stochastic simulations with solutions to ordinary differential equations for the mean behavior of the system that are valid under the conditions of large molecular abundances and quasi-equilibrium for fast reactions. The model produced results consistent with known HPV biology. Our simulation results suggest that stochasticity plays a pivotal role in determining the dynamics of HPV gene expression. In particular, the combination of positive and negative feedback regulation generates stochastic bursts of gene expression. Analysis of the model reveals that regulation at the promoter affects burst amplitude and frequency, whereas splicing is more specialized to regulate burst frequency. Our results also suggest that splicing enhancers are a significant source of stochasticity in pre-mRNA abundance and that the number of viruses infecting the host cell represents a third important source of stochasticity in gene expression.

A novel triple-tracer approach to assess postprandial protein turnover.

Insulin and nutrients have profound effects on proteome homeostasis. Currently no reliable methods are available to measure postprandial protein turnover. A triple-tracer method was developed using phenylalanine stable isotope tracers to estimate appearance rates of ingested (Ra meal) and endogenous phenylalanine and the rate of phenylalanine disposal (Rd). This was compared with the "traditional" dual-tracer method, using one (1-CM)- and two (2-CM)-compartment models. For both methods, [13C6]phenylalanine was given orally, and [15N]phenylalanine was constantly infused; the triple-tracer method added [2H5]phenylalanine, infused at rates to mimic meal [13C6]phenylalanine appearance. Additionally, incorporation of meal-derived phenylalanine into specific proteins was measured after purification by two-dimensional electrophoresis. The triple-tracer approach reduced modeling errors, allowing improved reconstruction of Ra meal with a tracer-to-tracee ratio that was more constant and better estimated Rd. The 2-CM better described phenylalanine kinetics and Rd than 1-CM. Thus, the triple-tracer approach using 2-CM is superior for measuring non-steady-state postprandial protein turnover. This novel approach also allows measurement of postprandial synthesis rates of specific plasma proteins. We offer a valid non-steady-state model to measure postprandial protein turnover and synthesis of plasma proteins that can safely be applied in adults, children, and pregnant women.

A four-compartment compartmental model to assess net whole body protein breakdown using a pulse of phenylalanine and tyrosine stable isotopes in humans.

The stable isotopes of phenylalanine (Phe) and tyrosine (Tyr) are often used to study whole body protein metabolism in humans. Noncompartmental approaches give limited physiological insight in the compartmental characteristics. We therefore developed a compartmental mathematical model of Phe/Tyr metabolism to describe protein fluxes by using stable tracer dynamic data in plasma following intravenous bolus of l-[ring-13C6]Phe and l-[ring-2H4]Tyr in healthy subjects. The model consists of four compartments describing Phe/Tyr kinetics. Because the model is a priori nonidentifiable, it is quantified in terms of two uniquely identifiable submodels representing two limit case scenarios, based on known physiology. The two submodels, identified by using the software SAAM II, fit well the experimental data of all individuals and provide an unbiased overview of the metabolic pathway in terms of intervals of validity of the non-uniquely identifiable variables. The model provides estimates of the flux from Phe to Tyr [4.1 ± 1.0 µmol·kg fat-free mass (FFM)-1·h-1 (mean ± SE)] and intervals of validity of the flux and pool estimates. Our preferred submodel yielded protein breakdown flux (50.5 ± 5.2 µmol·kg FFM-1·h-1), net protein breakdown (4.1 ± 1.0 µmol·kg FFM-1·h-1), Tyr from Phe hydroxylation (~12%), hydroxylated Phe (~8%), and flux ratio of Tyr to Phe arising from protein catabolism (0.68), consistent with available literature. The other submodel suggest that the assumptions made by noncompartmental analysis are consistently underestimated. Our accurate and detailed model for estimating Phe/Tyr metabolic pathways in humans might be essential to applications in a variety of scenarios describing whole body protein synthesis and breakdown in health and disease.

Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method.

BACKGROUND: Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. RESULTS: Here, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level. CONCLUSIONS: Detailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation.

ABACUS: an entropy-based cumulative bivariate statistic robust to rare variants and different direction of genotype effect.

MOTIVATION: In the past 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 multicausal, 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. RESULTS: Here, we propose ABACUS, an Algorithm based on a BivAriate CUmulative Statistic to identify single nucleotide polymorphisms (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. AVAILABILITY AND IMPLEMENTATION: ABACUS is available at http://www.dei.unipd.it/∼dicamill/pagine/Software.html.

Compression and fast retrieval of SNP data.

MOTIVATION: 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 single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data. RESULTS: We present a novel algorithm and file format for compressing and retrieving SNP data, specifically designed for large-scale association studies. Our algorithm is based on two main ideas: (i) compress linkage disequilibrium blocks in terms of differences with a reference SNP and (ii) compress reference SNPs exploiting information on their call rate and minor allele frequency. Tested on two SNP datasets and compared with several state-of-the-art software tools, our compression algorithm is shown to be competitive in terms of compression rate and to outperform all tools in terms of time to load compressed data. AVAILABILITY AND IMPLEMENTATION: Our compression and decompression algorithms are implemented in a C++ library, are released under the GNU General Public License and are freely downloadable from http://www.dei.unipd.it/~sambofra/snpack.html.

Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis.

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification.

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.

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells.

BACKGROUND: 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. RESULTS: Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3ß) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3ß, with a significant increase at 100 µM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3ß 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 GSK3ß, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition. CONCLUSIONS: These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3ß phosphorylation and glycogen synthesis.

Low-frequency rTMS inhibitory effects in the primary motor cortex: Insights from TMS-evoked potentials.

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. There was no effect after V1-rTMS. 1-Hz rTMS appears to increase the amount of inhibition following a TMS pulse, as demonstrated by the higher N100 and P60, which are thought to originate from GABAb-mediated inhibitory post-synaptic potentials. Our results confirm the reliability of the TMS-evoked N100 as a marker of cortical inhibition and provide insight into the neuromodulatory effects of 1-Hz rTMS. The present finding could be of relevance for therapeutic and diagnostic purposes.

Minimal modeling of insulin secretion in the perfused rat pancreas: a drug effect case study.

The experimental protocol of the perfused rat pancreas is commonly used to evaluate ß-cell function. In this context, mathematical models become useful tools through the determination of indexes that allow the assessment of ß-cell function in different experimental groups and the quantification of the effects of antidiabetic drugs, secretagogues, or treatments. However, a minimal model applicable to the isolated perfused rat pancreas has so far been unavailable. In this work, we adapt the C-peptide minimal model applied previously 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 ß-cell responsivity for first (ΦD) and second phase (ΦS, T) of insulin secretion. The model was initially applied to untreated pancreata and afterward used for the assessment of pharmacologically relevant agents (the gut hormone GLP-1, the potent GLP-1 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 ß-cell function similarly to GLP-1 (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 ß-cell function in the presence of postprandial GLP-1 levels.

Effect of voluntary repetitive long-lasting muscle contraction activity on the BOLD signal as assessed by optimal hemodynamic response function.

OBJECTIVE: 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. MATERIALS AND METHODS: 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). RESULTS: 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. CONCLUSION: 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.

The human brain pacemaker: Synchronized infra-slow neurovascular coupling in patients undergoing non-pulsatile cardiopulmonary bypass.

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.

Integrating literature-constrained and data-driven inference of signalling networks.

MOTIVATION: Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks. RESULTS: We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling. We obtain a model with superior data fit in the human liver cancer HepG2 and propose potential missing pathways. AVAILABILITY: CNORfeeder is in the process of being submitted to Bioconductor and in the meantime available at www.cellnopt.org. CONTACT: saezrodriguez@ebi.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kinetics and intracellular compartmentalization of HTLV-1 gene expression: nuclear retention of HBZ mRNAs.

Human T-cell leukemia virus type 1 (HTLV-1) codes for 9 alternatively spliced transcripts and 2 major regulatory proteins named Tax and Rex that function at the transcriptional and posttranscriptional levels, respectively. We investigated the temporal sequence of HTLV-1 gene expression in primary cells from infected patients using splice site-specific quantitative RT-PCR. The results indicated a two-phase kinetics with the tax/rex mRNA preceding expression of other viral transcripts. Analysis of mRNA compartmentalization in cells transfected with HTLV-1 molecular clones demonstrated the strict Rex-dependency of the two-phase kinetics and revealed strong nuclear retention of HBZ mRNAs, supporting their function as noncoding transcripts. Mathematical modeling underscored the importance of a delay between the functions of Tax and Rex, which was supported by experimental evidence of the longer half-life of Rex. These data provide evidence for a temporal pattern of HTLV-1 expression and reveal major differences in the intracellular compartmentalization of HTLV-1 transcripts.

Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data.

BACKGROUND: Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously. RESULTS: In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data. BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis. CONCLUSIONS: The significantly higher classification accuracy obtained by BoNB, together with the significance of the biomarkers identified from the Type 1 Diabetes dataset, prove the effectiveness of BoNB as an algorithm for both classification and biomarker selection from genome-wide SNP data. AVAILABILITY: Source code of the BoNB algorithm is released under the GNU General Public Licence and is available at http://www.dei.unipd.it/~sambofra/bonb.html.

The disposition index: from individual to population approach.

To correctly evaluate the glucose control system, it is crucial to account for both insulin sensitivity and secretion. The disposition index (DI) is the most widely accepted method to do so. The original paradigm (hyperbolic law) consists of the multiplicative product of indices related to insulin sensitivity and secretion, but more recently, an alternative formula has been proposed with the exponent α (power function law). Traditionally, curve-fitting approaches have been used to evaluate the DI in a population: the algorithmic implementations often introduce some critical issues, such as the assumption that one of the two indices is error free or the effects of the log transformation on the measurement errors. In this work, we review the commonly used approaches and show that they provide biased estimates. Then we propose a novel nonlinear total least square (NLTLS) approach, which does not need to use the approximations built in the previously proposed alternatives, and show its superiority. All of the traditional fit procedures, including NLTLS, account only for uncertainty affecting insulin sensitivity and secretion indices when they are estimated from noisy data. Thus, they fail when part of the observed variability is due to inherent differences in DI values between individuals. To handle this inevitable source of variability, we propose a nonlinear mixed-effects approach that describes the DI using population hyperparameters such as the population typical values and covariance matrix. On simulated data, this novel technique is much more reliable than the curve-fitting approaches, and it proves robust even when no or small population variability is present in the DI values. Applying this new approach to the analysis of real IVGTT data suggests a value of α significantly smaller than 1, supporting the importance of testing the power function law as an alternative to the simpler hyperbolic law.

Time-frequency analysis of short-lasting modulation of EEG induced by intracortical and transcallosal paired TMS over motor areas.

Dynamic changes in spontaneous electroencephalogram (EEG) rhythms can be seen to occur with a high rate of variability. An innovative method to study brain function is by triggering oscillatory brain activity with transcranial magnetic stimulation (TMS). EEG-TMS coregistration was performed on five healthy subjects during a 1-day experimental session that involved four steps: baseline acquisition, unconditioned single-pulse TMS, intracortical inhibition (ICI, 3 ms) paired-pulse TMS, and transcallosal stimulation over left and right primary motor cortex (M1). A time-frequency analysis based on the wavelet method was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS. Single, paired, and transcallosal TMS applied on the sensorimotor areas induced rapid desynchronization over the frontal and central-parietal electrodes mainly in the alpha and beta bands, followed by a rebound of synchronization, and rapid synchronization of delta and theta activity. Wavelet analysis after a perturbation approach is a novel way to investigate modulation of oscillatory brain activity. The main findings are consistent with the concept that the human motor system may be based on networklike oscillatory cortical activity and might be modulated by single, paired, and transcallosal magnetic pulses applied to M1, suggesting a phenomenon of fast brain activity resetting and triggering of slow activity.

Electroencephalography in patients with cirrhosis.

BACKGROUND & AIMS: Electroencephalography has not been completely quantified in patients with cirrhosis. We investigated the electroencephalogram (EEG) dynamics in patients with cirrhosis. METHODS: We performed closed-eye EEGs on 175 patients with cirrhosis (age, 55 ± 11 years; 24% Child-Pugh class A, 48% class B, and 285 class C), conducted clinical and psychometric assessments for hepatic encephalopathy (HE), and followed the patients for 1 year. EEG characteristics were assessed in the frequency domain, in the frontal (F3-F4) and parietal (P3-P4) derivations. Intrahemispheric (frontoparietal, right, and left) and interhemispheric (F3-F4 and P3-P4) coherence were computed. The EEGs of 50 healthy volunteers (age, 56 ± 17 years) served as controls. RESULTS: Compared with controls, the EEGs of patients with cirrhosis had a reduced frequency in the posterior derivations (P3/P4 mean dominant frequency, 9.1 ± 1.8 and 8.9 ± 1.7 Hz vs 10.4 ± 1.3 and 10.2 ± 1.3 Hz, respectively; P < .01) and an increase in interhemispheric parietal relative coherence within the theta band (22.3% ± 5.5% vs 18.9% ± 3.5%; P < .01). These features were more prominent in patients with Child class C and in patients with a history of overt HE; they correlated with hyperammonemia and hyponatremia. The decrease in EEG frequency, along with the increase in interhemispheric theta coherence in the posterior derivations, was inversely associated with survival and the occurrence of overt HE during the follow-up period. CONCLUSIONS: In patients with cirrhosis, alterations in the EEG were significantly associated with the severity of liver disease and HE; the EEG might be used in determining prognosis.