The relationship between pathological brain activity and functional network connectivity in glioma patients.

PURPOSE: Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS: We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS: As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION: Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.

Multimodal multilayer network centrality relates to executive functioning.

Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.

The longitudinal relation between executive functioning and multilayer network topology in glioma patients.

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.

Fock-space approach to stochastic susceptible-infected-recovered models.

We investigate the stochastic susceptible-infected-recovered (SIR) model of infectious disease dynamics in the Fock-space approach. In contrast to conventional SIR models based on ordinary differential equations for the subpopulation sizes of S, I, and R individuals, the stochastic SIR model is driven by a master equation governing the transition probabilities among the system's states defined by SIR occupation numbers. In the Fock-space approach the master equation is recast in the form of a real-valued Schrödinger-type equation with a second quantization Hamiltonian-like operator describing the infection and recovery processes. We find exact analytic expressions for the Hamiltonian eigenvalues for any population size N. We present small- and large-N results for the average numbers of SIR individuals and basic reproduction number. For small N we also obtain the probability distributions of SIR states, epidemic sizes and durations, which cannot be found from deterministic SIR models. Our Fock-space approach to stochastic SIR models introduces a powerful set of tools to calculate central quantities of epidemic processes, especially for relatively small populations where statistical fluctuations not captured by conventional deterministic SIR models play a crucial role.

Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

The Influence of Lime Solution in Kneading Water Substitution on Cement Roughcast and Mortar Coating.

The understanding of the mechanical fixation behavior of coatings is crucial for a better comprehension of the bonding systems, especially at the interface between the mortar and the substrate. Physical adherence is related, among other things, to the contents of the materials used in the roughcast and mortar coatings, due to the colloidal water penetration into the pores of the substrate. This work evaluated the influence of different lime solution additions replacing the kneading water in the preparation of roughcast and mortar coatings. Two types of substrates were investigated:ceramic bricks and concrete blocks. Three wall masonry panels were constructed, with dimensions of 220 × 180 cm2, one of concrete block and two of ceramic bricks, followed by the application of roughcast and mortar coating with an average thickness of 5 mm and 20 mm, respectively. Direct tensile bond strength tests were performed and the results, with a 95% confidence level, showed that substrate ceramic and treatment in the roughcast exhibited a better behavior regarding the distribution of the tensile bond strength of the tested specimens. However, no significant differences of the amount of addition used (0%, 5%, 10% and 15%) on the tensile bond strength were observed.

Geometry and molecular dynamics of the Hamiltonian mean-field model in a magnetic field.

The Hamiltonian mean-field model is investigated in the presence of a field. The self-consistent equations for the magnetization and the energy per particle are derived, and the field effect on the caloric curve is presented. The analytical geometric approach to Hamiltonian dynamics, under the hypothesis of quasi-isotropy, allows us to calculate the field effect on the energy-dependent microcanonical mean Ricci curvature and its fluctuations. Notably, the method proved suitable to identify that stable and metastable solutions of the Lyapunov exponent exhibit intriguing distinct curvature behavior very close to the critical point at extremely low field values. In addition, finite-size molecular dynamics (MD) simulations are used to observe the evolution of the magnetization and their components, including the stability properties of the solutions. Most importantly, comparison of finite-size MD calculations of the Lyapunov exponent and related properties with those via the geometric approach unveil the sensible dependence of these microcanonical quantities on energy, number of particles, and field, before a quasisaturation behavior at high fields. Finally, relaxation properties from out-of-equilibrium initial conditions are discussed in light of MD simulations.

Dynamic functional connectivity as a neural correlate of fatigue in multiple sclerosis.

BACKGROUND: More than 80% of multiple sclerosis (MS) patients experience symptoms of fatigue. MS-related fatigue is only partly explained by structural (lesions and atrophy) and functional (brain activation and conventional static functional connectivity) brain properties. OBJECTIVES: To investigate the relationship of dynamic functional connectivity (dFC) with fatigue in MS patients and to compare dFC with commonly used clinical and MRI parameters. METHODS: In 35 relapsing-remitting MS patients (age: 42.83 years, female/male: 20/15, disease duration: 11 years) and 19 healthy controls (HCs) (age: 41.38 years, female/male: 11/8), fatigue was measured using the CIS-20r questionnaire at baseline and at 6-month follow-up. All subjects underwent structural and resting-state functional MRI at baseline. Global static functional connectivity (sFC) and dynamic functional connectivity (dFC) were calculated. dFC was assessed using a sliding-window approach by calculating the summed difference (diff) and coefficient of variation (cv) across windows. Moreover, regional connectivity between regions previously associated with fatigue in MS was estimated (i.e. basal ganglia and regions of the Default Mode Network (DMN): medial prefrontal, posterior cingulate and precuneal cortices). Hierarchical regression analyses were performed with forward selection to identify the most important correlates of fatigue at baseline. Results were not corrected for multiple testing due to the exploratory nature of the study. RESULTS: Patients were more fatigued than HCs at baseline (p = 0.001) and follow-up (p = 0.002) and fatigue in patients was stable over time (p = 0.213). Patients had significantly higher baseline global dFC than HCs, but no difference in basal ganglia-DMN dFC. In the regression model for baseline fatigue in patients, basal ganglia-DMN dFC-cv (standardized ß = -0.353) explained 12.5% additional variance on top of EDSS (p = 0.032). Post-hoc analysis revealed higher basal ganglia-DMN dFC-cv in non-fatigued patients compared to healthy controls (p = 0.013), whereas fatigued patients and healthy controls showed similar basal ganglia-DMN dFC. CONCLUSIONS: Less dynamic connectivity between the basal ganglia and the cortex is associated with greater fatigue in MS patients, independent of disability status. Within patients, lower dynamics of these connections could relate to lower efficiency and increased fatigue. Increased dynamics in non-fatigued patients compared to healthy controls might represent a network organization that protects against fatigue or signal early network dysfunction.

Fock-space methods for diffusion: Capturing volume exclusion via fermionic statistics.

Volume exclusion and single-file diffusion play an important role at very small scales, such as those associated with molecular machines, ion channels, and transport in zeolites, while introducing fundamental differences compared to Brownian motion, such as changes to the power-law relation between the mean square displacement and time. In this work we map the chemical master equation for excluded diffusion onto a Schrödinger equation via annihilation and creation ladder operators with fermionic statistics, together with linear and symbolic algebra with the resulting Fock-space representation to describe the effect of volume-exclusion processes in finite one-dimensional chains. We contrast the dynamics with the nonexclusive (bosonic) diffusion for crowded, intermediate, and dilute particle populations. Motivated by shuttling in molecular machines, we proceed to investigate differences in exit time distributions introduced by volume exclusion, incorporating the presence of transport bias. More generally, this study demonstrates how one can analyze volume-excluded transport for small stochastic systems, without the need for stochastic simulation and ensemble averaging, simply by considering anticommutation relations and fermionic statistics in a Fock-space representation of the stochastic dynamics.

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches.

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. â€‹Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

Using curvature to infer COVID-19 fractal epidemic network fragility and systemic risk

The damage of the novel Coronavirus disease (COVID-19) is reaching unprecedented scales. There are numerous classical epidemiology models trying to quantify epidemiology metrics. Usually, to forecast epidemics, classical approaches need parameter estimations, such as the contagion rate or the basic reproduction number. Here, we propose a data-driven, parameter-free, geometric approach to access the emergence of a pandemic state by studying the Forman-Ricci and Ollivier-Ricci network curvatures. Discrete Ollivier-Ricci curvature has been used successfully to forecast risk in financial networks and we suggest that those results can provide analogous results for COVID-19 epidemic time-series. We first compute both curvatures in a toy-model of epidemic time-series with delays, which allows us to create epidemic networks. By doing so, we are able to verify that the Ollivier-Ricci and Forman-Ricci curvatures can be a parameter-free estimate for identifying a pandemic state in the simulated epidemic. On this basis, we then compute both Forman-Ricci and Ollivier-Ricci curvatures for real epidemic networks built from COVID-19 epidemic time-series available at the World Health Organization (WHO). Both curvatures allow us to detect early warning signs of the emergence of the pandemic. The advantage of our method lies in providing an early geometrical data marker for the pandemic state, regardless of parameter estimation and stochastic modelling. This work opens the possibility of using discrete geometry to study epidemic networks.

Topological phase transitions in functional brain networks.

Functional brain networks are often constructed by quantifying correlations between time series of activity of brain regions. Their topological structure includes nodes, edges, triangles, and even higher-dimensional objects. Topological data analysis (TDA) is the emerging framework to process data sets under this perspective. In parallel, topology has proven essential for understanding fundamental questions in physics. Here we report the discovery of topological phase transitions in functional brain networks by merging concepts from TDA, topology, geometry, physics, and network theory. We show that topological phase transitions occur when the Euler entropy has a singularity, which remarkably coincides with the emergence of multidimensional topological holes in the brain network. The geometric nature of the transitions can be interpreted, under certain hypotheses, as an extension of percolation to high-dimensional objects. Due to the universal character of phase transitions and noise robustness of TDA, our findings open perspectives toward establishing reliable topological and geometrical markers for group and possibly individual differences in functional brain network organization.

Scanning electron microscopy with energy dispersive spectroscopy and Raman and infrared spectroscopic study of tilleyite Ca5Si2O7(CO3)(2-)Y.

The mineral tilleyite-Y, a carbonate-silicate of calcium, has been studied by scanning electron microscopy with chemical analysis using energy dispersive spectroscopy (EDX) and Raman and infrared spectroscopy. Multiple carbonate stretching modes are observed and support the concept of non-equivalent carbonate units in the tilleyite structure. Multiple Raman and infrared bands in the OH stretching region are observed, proving the existence of water in different molecular environments in the structure of tilleyite. Vibrational spectroscopy offers new information on the mineral tilleyite.

SEM, EDS and vibrational spectroscopic study of dawsonite NaAl(CO3)(OH)2.

In this work we have studied the mineral dawsonite by using a combination of scanning electron microscopy with EDS and vibrational spectroscopy. Single crystals show an acicular habitus forming aggregates with a rosette shape. The chemical analysis shows a phase composed of C, Al, and Na. Two distinct Raman bands at 1091 and 1068 cm(-1) are assigned to the CO3(2-) ν1 symmetric stretching mode. Multiple bands are observed in both the Raman and infrared spectra in the antisymmetric stretching and bending regions showing that the symmetry of the carbonate anion is reduced and in all probability the carbonate anions are not equivalent in the dawsonite structure. Multiple OH deformation vibrations centred upon 950 cm(-1) in both the Raman and infrared spectra show that the OH units in the dawsonite structure are non-equivalent. Raman bands observed at 3250, 3283 and 3295 cm(-1) are assigned to OH stretching vibrations. The position of these bands indicates strong hydrogen bonding of the OH units in the dawsonite structure. The formation of the mineral dawsonite has the potential to offer a mechanism for the geosequestration of greenhouse gases.

SEM, EDX and vibrational spectroscopic study of the mineral tunisite NaCa2Al4(CO3)4Cl(OH)8.

The mineral tunisite has been studied by using a combination of scanning electron microscopy with energy dispersive X-ray fluorescence and vibrational spectroscopy. Chemical analysis shows the presence of Na, Ca, Al and Cl. SEM shows a pure single phase. An intense Raman band at 1127 cm(-1) is assigned to the carbonate ν1 symmetric stretching vibration and the Raman band at 1522 cm(-1) is assigned to the ν3 carbonate antisymmetric stretching vibration. Infrared bands are observed in similar positions. Multiple carbonate bending modes are found. Raman bands attributable to AlO stretching and bending vibrations are observed. Two Raman bands at 3419 and 3482 cm(-1) are assigned to the OH stretching vibrations of the OH units. Vibrational spectroscopy enables aspects of the molecular structure of the carbonate mineral tunisite to be assessed.

A vibrational spectroscopic study of tengerite-(Y) Y2(CO3)3 2-3H2O.

The mineral tengerite-(Y) has been studied by vibrational spectroscopy. Multiple carbonate stretching modes are observed and support the concept of non-equivalent carbonate units in the tengerite-(Y) structure. Intense sharp bands at 464, 479 and 508 cm(-1) are assigned to YO stretching modes. Raman bands at 765 and 775 cm(-1) are assigned to the CO3(2-) ν4 bending modes and Raman bands at 589, 611, 674 and 689 cm(-1) are assigned to the CO3(2-) ν2 bending modes. Multiple Raman and infrared bands in the OH stretching region are observed, proving the existence of water in different molecular environments in the structure of tengerite-(Y).

Fock space, symbolic algebra, and analytical solutions for small stochastic systems.

Randomness is ubiquitous in nature. From single-molecule biochemical reactions to macroscale biological systems, stochasticity permeates individual interactions and often regulates emergent properties of the system. While such systems are regularly studied from a modeling viewpoint using stochastic simulation algorithms, numerous potential analytical tools can be inherited from statistical and quantum physics, replacing randomness due to quantum fluctuations with low-copy-number stochasticity. Nevertheless, classical studies remained limited to the abstract level, demonstrating a more general applicability and equivalence between systems in physics and biology rather than exploiting the physics tools to study biological systems. Here the Fock space representation, used in quantum mechanics, is combined with the symbolic algebra of creation and annihilation operators to consider explicit solutions for the chemical master equations describing small, well-mixed, biochemical, or biological systems. This is illustrated with an exact solution for a Michaelis-Menten single enzyme interacting with limited substrate, including a consideration of very short time scales, which emphasizes when stiffness is present even for small copy numbers. Furthermore, we present a general matrix representation for Michaelis-Menten kinetics with an arbitrary number of enzymes and substrates that, following diagonalization, leads to the solution of this ubiquitous, nonlinear enzyme kinetics problem. For this, a flexible symbolic maple code is provided, demonstrating the prospective advantages of this framework compared to stochastic simulation algorithms. This further highlights the possibilities for analytically based studies of stochastic systems in biology and chemistry using tools from theoretical quantum physics.

Assessment of the role of heparan sulfate in high molecular weight kininogen binding to human umbilical vein endothelial cells.

The assembly and activation of the kinin forming system components on human umbilical vein endothelial cells (HUVEC) have been studied in great detail. Proteins such as gC1qR, cytokeratin-1 and u-PAR have been identified to be responsible for Zn2+-dependent binding of high molecular weight kininogen (HK) to HUVEC. Heparan sulfate has also been shown to have a major role in Zn2+-dependent binding of HK to the endothelial cell line, Ea.hy 926. In this study, we have analyzed the possible contribution of heparan sulfate to high molecular weight kininogen binding to HUVEC using multiple approaches. The presence of heparan sulfate on HUVEC was analyzed by staining with an antibody specific for heparan sulfate. Incubation of the cells with bacterial heparinases removed the heparan sulfate from the cell surface to the level seen with a control antibody, however, the Zn2+-dependent binding of HK was not affected. Further, blocking of heparan sulfate with a specific antibody to heparan sulfate even after digestion with heparinases did not reduce HK binding whereas antibodies to the proteins gC1qR and cytokeratin-1 consistently reduced the binding of HK to the endothelial cells. The binding intensities of FITC-labeled HK were similar in heparinase-treated and -untreated HUVEC. The rate of kallikrein formation by the assembly of factor XII, HK and PK were similar in both heparinase-treated and non-treated HUVEC. All of these data indicate that heparan sulfate does not contribute significantly to HK binding to HUVEC.

Suppression of Cox-2 and TNF-alpha mRNA in endotoxin tolerance: effect of cycloheximide, antinomycin D, and okadaic acid.

Lipopolysaccharide (LPS)-tolerant human promonocytic THP-1 cells produce decreased levels of inflammatory mediators such as eicosanoids and tumor necrosis factor alpha (TNFalpha) in response to LPS. We hypothesized that transcriptional repression by newly synthesized proteins may be a mechanism for the reduced cellular response to a secondary challenge with LPS. THP-1 cells were desensitized after a 3.5 h or 20 h pre-exposure to LPS (1 microg/mL) and subsequently challenged with LPS (10 microg/mL). In cells rendered tolerant by exposure to LPS for 20 h, LPS-induced expression of cyclooxygenase (Cox)-2 and TNFalpha mRNA was suppressed. Cycloheximide (10 microM) prevented the transcriptional down-regulation of Cox-2 mRNA and to a lesser extent, TNFalpha mRNA, in LPS-tolerant cells. Transcriptional arrest with actinomycin D stabilized steady-state expression of Cox-2 mRNA in naive and tolerant cells but destabilized TNFalpha mRNA expression in LPS-tolerant cells. The observation that in naive cells Cox-2 and TNFalpha mRNA levels subside at 3 to 4 h after LPS (10 microg/mL or 1 microg/mL) suggested that LPS tolerance may occur earlier. Therefore, in subsequent experiments, the effect of LPS pretreatment for only 3.5 h was examined. This abbreviated tolerance regimen diminished secondary LPS-induced Cox-2 mRNA expression but had a lesser effect on TNFalpha mRNA expression. However, cycloheximide augmented both Cox-2 and TNFalpha mRNA expression in this group. Also, the serine/threonine phosphatase inhibitor okadaic acid augmented Cox-2 and TNFalpha mRNA expression in the LPS-tolerant cells. Although LPS-induced TNFalpha production in LPS-tolerant cells was suppressed relative to the naive cells, okadaic acid induced comparable levels of TNFalpha in tolerant and naive cells. These findings support the concept that LPS tolerance is associated with induction of proteins that alter expression of certain genes. Expression of Cox-2 mRNA appears to be particularly sensitive to down-regulation and, to a lesser extent, TNFalpha mRNA. However, this seems to vary depending on the LPS pretreatment regimen. The ability of a phosphatase inhibitor to induce TNFalpha and expression of Cox-2 and TNFalpha mRNA in LPS tolerance suggests that there may be alterations in phosphorylation status of signaling pathways, transcriptional mechanisms, or post-transcriptional mRNA stability.

Tolerance to LPS decreases macrophage G protein content.

The effects of tolerizing doses of LPS on mRNA and protein levels of three different G protein subunits were investigated to understand the mechanism(s) responsible for the reduction in Gialpha protein content in LPS tolerance. Tolerance was induced in rats using Salmonella enteritidis LPS (intraperitoneal route) with a single dose of 100 microg/kg. Peritoneal macrophages were harvested 6 and 24 h later. In some studies, a second dose of LPS 500 microg/kg was given on the following day, and peritoneal macrophages were harvested 5 days after the first injection. Macrophage RNA or a crude membrane fraction was prepared from macrophages, and the mRNA level or the protein content for Gialpha3, Gialpha2, and Gsalpha was analyzed using Northern or Western blots, respectively. Compared with the control levels, the message for Gialpha3 was reduced (p < .025) at 6 and 24 h and 5 day time periods after LPS treatment. The Gialpha2 mRNA was increased relative to the control levels (p < .05) at 6 h and 5 days after LPS treatment, respectively, and Gsalpha message was not significantly changed. The half-life of Gialpha3 mRNA was not significantly different in control versus tolerant macrophages. The Gialpha3 mRNA and membrane protein were not significantly changed by incubation with LPS for intervals up to 6 h in vitro. Macrophage membrane Gialpha3 and Gialpha1 and 2 protein content from tolerant rats were reduced compared with the controls at 6 and 24 h, respectively (p < .05). These studies are consistent with our previous observations of selective changes in macrophage Gialpha protein content in LPS tolerance and raise the possibility that this may affect signal transduction events in these cells.