XIAP promotes the expansion and limits the contraction of CD8 T cell response through cell extrinsic and intrinsic mechanisms respectively.

XIAP is an endogenous inhibitor of cell death and inactivating mutations of XIAP are responsible for X-linked lymphoproliferative disease (XLP-2) and primary immunodeficiency, but the mechanism(s) behind these contradictory outcomes have been unclear. We report that during infection of macrophages and dendritic cells with various intracellular bacteria, XIAP restricts cell death and secretion of IL-1ß but promotes increased activation of NFκB and JNK which results in elevated secretion of IL-6 and IL-10. Poor secretion of IL-6 by Xiap-deficient antigen presenting cells leads to poor expansion of recently activated CD8 T cells during the priming phase of the response. On the other hand, Xiap-deficient CD8 T cells displayed increased proliferation and effector function during the priming phase but underwent enhanced contraction subsequently. Xiap-deficient CD8 T cells underwent skewed differentiation towards short lived effectors which resulted in poor generation of memory. Consequently Xiap-deficient CD8 T cells failed to provide effective control of bacterial infection during re-challenge. These results reveal the temporal impact of XIAP in promoting the fitness of activated CD8 T cells through cell extrinsic and intrinsic mechanisms and provide a mechanistic explanation of the phenotype observed in XLP-2 patients.

Isolates of Salmonella typhimurium circumvent NLRP3 inflammasome recognition in macrophages during the chronic phase of infection.

Inflammasome signaling results in cell death and release of cytokines from the IL-1 family, which facilitates control over an infection. However, some pathogens such as Salmonella typhimurium (ST) activate various innate immune signaling pathways, including inflammasomes, yet evade these cell death mechanisms, resulting in a chronic infection. Here we investigated inflammasome signaling induced by acute and chronic isolates of ST obtained from different organs. We show that ST isolated from infected mice during the acute phase displays an increased potential to activate inflammasome signaling, which then undergoes a protracted decline during the chronic phase of infection. This decline in inflammasome signaling was associated with reduced expression of virulence factors, including flagella and the Salmonella pathogenicity island I genes. This reduction in cell death of macrophages induced by chronic isolates had the greatest impact on the NLRP3 inflammasome, which correlated with a reduction in caspase-1 activation. Furthermore, rapid cell death induced by Casp-1/11 by ST in macrophages limited the subsequent activation of cell death cascade proteins Casp-8, RipK1, RipK3, and MLKL to prevent the activation of alternative forms of cell death. We observed that the lack of the ability to induce cell death conferred a competitive fitness advantage to ST only during the acute phase of infection. Finally, we show that the chronic isolates displayed a significant attenuation in their ability to infect mice through the oral route. These results reveal that ST adapts during chronic infection by circumventing inflammasome recognition to promote the survival of both the host and the pathogen.

Pulse Intake Improves Nutrient Density among US Adult Consumers.

The objective was to examine trends in pulse (dry beans, dry peas, chickpeas and lentils) intake over a 10-year period and to compare nutrient intakes of pulse consumers and non-consumers to better understand the impact of pulse consumption on diet quality in the US population. NHANES 2003-2014 data for respondents (≥19 years) with 2 days of intake was used to evaluate trends in pulse intake. Pulse consumers were identified as those NHANES respondents who consumed pulses on one or both days. Differences in energy adjusted nutrient intakes between non-consumers and consumers were assessed. There were no significant trends in pulse intakes for the total population or for pulse consumers over the 10-year period. In 2013-2014, approximately 27% of adults consumed pulses with an intake of 70.9 ± 2.5 g/day over 2 days, just slightly <0.5 cup equivalents/day. At all levels of consumption, consumers had higher (p < 0.01) energy adjusted intakes of fiber, folate, magnesium. Higher energy adjusted intakes for potassium, zinc, iron and choline and lower intakes of fat were observed for consumers than for non-consumers at intakes ≥69.4 ± 1.01 g/day. These data suggest that pulse consumption in the US population may result in better diet quality with diets that are more nutrient dense than those without pulses.

Impact of sociodemographic factors on the consumption of tubers in Brazil.

BACKGROUND: Although tubers play a significant role in Brazilian agriculture, very little is known about the intake of tubers among the Brazilian population. The objective of this study was to characterize the intake of tubers across Brazil. The types of tubers consumed were quantified, and the impact of geographic and sociodemographic factors was assessed. METHODS: This cross-sectional study is based on dietary intake data of 33,504 subjects obtained from the Brazilian National Dietary Survey. All tuber containing foods were identified, and the contribution of different tubers to overall tuber consumption in Brazil was quantified. Descriptive analyses assessed the impact of macroregion and sociodemographic characteristics on tuber consumption, and differences in intake were assessed using statistical tests. Lastly, the dietary intakes of tuber consumers and non-consumers were compared after adjusting for energy and covariates to determine if there were any major differences in dietary intakes between the two groups. RESULTS: Fifty-five percent of the Brazilian population consumed tubers, which differed by macroregion. The intake of tubers among consumers also differed between macroregions. Overall, rural areas reported significantly higher mean daily intakes of tubers (122 g/day) among tuber consumers than urban areas (95 g/day). Mandioca and potato were the most commonly consumed tubers (59 and 43% prevalence, respectively, on any of the 2 days), while the highest daily intakes amongst tuber consumers across Brazil were noted for sweet potato (156 g/day) and potato (95 g/day). On a macroregion level, among tuber consumers, mandioca had the highest prevalence of consumption in the North (94%), Northeast (83%), and Central-West (68%), while consumption of potatoes was most prevalent in the Southeast (63%) and South (62%). Compared to women, small but significantly higher tuber intakes were noted for males (108 vs. 85 g/day). There were no significant differences in intakes among income quintiles. After adjusting for energy and other covariates, nutrient intakes between tuber and non-tuber consumers were not meaningfully different, with the exception of sodium (+ 6.0% comparing non-tuber to tuber consumers), iron (+ 6.1%), zinc (+ 5.7%), vitamin C (+ 8.3%), riboflavin (+ 9.0%), and folate (+ 7.9%). CONCLUSIONS: Tuber consumption is influenced by regional and sociodemographic characteristics of the Brazilian population. When looking at energy-adjusted nutrient intakes, diets of tuber consumers have resulted in somewhat lower intakes of some micronutrients, namely riboflavin, folate, vitamin C, iron, sodium, and zinc.

Network mechanism for insect olfaction.

Early olfactory pathway responses to the presentation of an odor exhibit remarkably similar dynamical behavior across phyla from insects to mammals, and frequently involve transitions among quiescence, collective network oscillations, and asynchronous firing. We hypothesize that the time scales of fast excitation and fast and slow inhibition present in these networks may be the essential element underlying this similar behavior, and design an idealized, conductance-based integrate-and-fire model to verify this hypothesis via numerical simulations. To better understand the mathematical structure underlying the common dynamical behavior across species, we derive a firing-rate model and use it to extract a slow passage through a saddle-node-on-an-invariant-circle bifurcation structure. We expect this bifurcation structure to provide new insights into the understanding of the dynamical behavior of neuronal assemblies and that a similar structure can be found in other sensory systems.

The extended Granger causality analysis for Hodgkin-Huxley neuronal models.

How to extract directions of information flow in dynamical systems based on empirical data remains a key challenge. The Granger causality (GC) analysis has been identified as a powerful method to achieve this capability. However, the framework of the GC theory requires that the dynamics of the investigated system can be statistically linearized; i.e., the dynamics can be effectively modeled by linear regressive processes. Under such conditions, the causal connectivity can be directly mapped to the structural connectivity that mediates physical interactions within the system. However, for nonlinear dynamical systems such as the Hodgkin-Huxley (HH) neuronal circuit, the validity of the GC analysis has yet been addressed; namely, whether the constructed causal connectivity is still identical to the synaptic connectivity between neurons remains unknown. In this work, we apply the nonlinear extension of the GC analysis, i.e., the extended GC analysis, to the voltage time series obtained by evolving the HH neuronal network. In addition, we add a certain amount of measurement or observational noise to the time series to take into account the realistic situation in data acquisition in the experiment. Our numerical results indicate that the causal connectivity obtained through the extended GC analysis is consistent with the underlying synaptic connectivity of the system. This consistency is also insensitive to dynamical regimes, e.g., a chaotic or non-chaotic regime. Since the extended GC analysis could in principle be applied to any nonlinear dynamical system as long as its attractor is low dimensional, our results may potentially be extended to the GC analysis in other settings.

A computational investigation of electrotonic coupling between pyramidal cells in the cortex.

The existence of electrical communication among pyramidal cells (PCs) in the adult cortex has been debated by neuroscientists for several decades. Gap junctions (GJs) among cortical interneurons have been well documented experimentally and their functional roles have been proposed by both computational neuroscientists and experimentalists alike. Experimental evidence for similar junctions among pyramidal cells in the cortex, however, has remained elusive due to the apparent rarity of these couplings among neurons. In this work, we develop a neuronal network model that includes observed probabilities and strengths of electrotonic coupling between PCs and gap-junction coupling among interneurons, in addition to realistic synaptic connectivity among both populations. We use this network model to investigate the effect of electrotonic coupling between PCs on network behavior with the goal of theoretically addressing this controversy of existence and purpose of electrotonically coupled PCs in the cortex.

Neural networks of different species, brain areas and states can be characterized by the probability polling state.

Cortical networks are complex systems of a great many interconnected neurons that operate from collective dynamical states. To understand how cortical neural networks function, it is important to identify their common dynamical operating states from the probabilistic viewpoint. Probabilistic characteristics of these operating states often underlie network functions. Here, using multi-electrode data from three separate experiments, we identify and characterize a cortical operating state (the "probability polling" or "p-polling" state), common across mouse and monkey with different behaviors. If the interaction among neurons is weak, the p-polling state provides a quantitative understanding of how the high dimensional probability distribution of firing patterns can be obtained by the low-order maximum entropy formulation, effectively utilizing a low dimensional stimulus-coding structure. These results show evidence for generality of the p-polling state and in certain situations its advantage of providing a mathematical validation for the low-order maximum entropy principle as a coding strategy.

Coating M-CSF on plastic surface results in the generation of increased numbers of macrophages in vitro.

Macrophages are one of the important cell types in the innate immune system that are present in various anatomical regions of the body and promote early control of pathogens. The relative proportion of macrophages in various lymphoid and non-lymphoid regions is small, and as such it is tedious to purify these cells to homogeneity. Culture of bone marrow precursors with macrophage colony-stimulating factor (M-CSF) results in their differentiation to macrophages, however this procedure results in low numbers of differentiated macrophages. Herein we reveal a new approach of generating increased numbers of differentiated macrophages from bone marrow precursors. We show that M-CSF delivered in a plate-bound form results in the differentiation of significantly more macrophages in comparison to soluble M-CSF. Furthermore, the macrophages differentiated with plate-bound M-CSF display increased metabolic activity and cell death following infection with pathogens.

Effective dispersion in the focusing nonlinear Schrödinger equation.

For waves described by the focusing nonlinear Schrödinger equation (FNLS), we present an effective dispersion relation (EDR) that arises dynamically from the interplay between the linear dispersion and the nonlinearity. The form of this EDR is parabolic for a robust family of "generic" FNLS waves and equals the linear dispersion relation less twice the total wave action of the wave in question multiplied by the square of the nonlinearity parameter. We derive an approximate form of this EDR explicitly in the limit of small nonlinearity and confirm it using the wave-number-frequency spectral (WFS) analysis, a Fourier-transform based method used for determining dispersion relations of observed waves. We also show that it extends to the FNLS the universal EDR formula for the defocusing Majda-McLaughlin-Tabak (MMT) model of weak turbulence. In addition, unexpectedly, even for some spatially periodic versions of multisolitonlike waves, the EDR is still a downward shifted linear-dispersion parabola, but the shift does not have a clear relation to the total wave action. Using WFS analysis and heuristic derivations, we present examples of parabolic and nonparabolic EDRs for FNLS waves and also waves for which no EDR exists.

Dendritic computations captured by an effective point neuron model.

Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework.

A Role for Electrotonic Coupling Between Cortical Pyramidal Cells.

Many brain regions communicate information through synchronized network activity. Electrical coupling among the dendrites of interneurons in the cortex has been implicated in forming and sustaining such activity in the cortex. Evidence for the existence of electrical coupling among cortical pyramidal cells, however, has been largely absent. A recent experimental study measured properties of electrical connections between pyramidal cells in the cortex deemed "electrotonic couplings." These junctions were seen to occur pair-wise, sparsely, and often coexist with electrically-coupled interneurons. Here, we construct a network model to investigate possible roles for these rare, electrotonically-coupled pyramidal-cell pairs. Through simulations, we show that electrical coupling among pyramidal-cell pairs significantly enhances coincidence-detection capabilities and increases network spike-timing precision. Further, a network containing multiple pairs exhibits large variability in its firing pattern, possessing a rich coding structure.

Maximum entropy principle analysis in network systems with short-time recordings.

In many realistic systems, maximum entropy principle (MEP) analysis provides an effective characterization of the probability distribution of network states. However, to implement the MEP analysis, a sufficiently long-time data recording in general is often required, e.g., hours of spiking recordings of neurons in neuronal networks. The issue of whether the MEP analysis can be successfully applied to network systems with data from short-time recordings has yet to be fully addressed. In this work, we investigate relationships underlying the probability distributions, moments, and effective interactions in the MEP analysis and then show that, with short-time recordings of network dynamics, the MEP analysis can be applied to reconstructing probability distributions of network states that is much more accurate than the one directly measured from the short-time recording. Using spike trains obtained from both Hodgkin-Huxley neuronal networks and electrophysiological experiments, we verify our results and demonstrate that MEP analysis provides a tool to investigate the neuronal population coding properties for short-time recordings.

Determination of effective synaptic conductances using somatic voltage clamp.

The interplay between excitatory and inhibitory neurons imparts rich functions of the brain. To understand the synaptic mechanisms underlying neuronal computations, a fundamental approach is to study the dynamics of excitatory and inhibitory synaptic inputs of each neuron. The traditional method of determining input conductance, which has been applied for decades, employs the synaptic current-voltage (I-V) relation obtained via voltage clamp. Due to the space clamp effect, the measured conductance is different from the local conductance on the dendrites. Therefore, the interpretation of the measured conductance remains to be clarified. Using theoretical analysis, electrophysiological experiments, and realistic neuron simulations, here we demonstrate that there does not exist a transform between the local conductance and the conductance measured by the traditional method, due to the neglect of a nonlinear interaction between the clamp current and the synaptic current in the traditional method. Consequently, the conductance determined by the traditional method may not correlate with the local conductance on the dendrites, and its value could be unphysically negative as observed in experiment. To circumvent the challenge of the space clamp effect and elucidate synaptic impact on neuronal information processing, we propose the concept of effective conductance which is proportional to the local conductance on the dendrite and reflects directly the functional influence of synaptic inputs on somatic membrane potential dynamics, and we further develop a framework to determine the effective conductance accurately. Our work suggests re-examination of previous studies involving conductance measurement and provides a reliable approach to assess synaptic influence on neuronal computation.

Dynamical and Coupling Structure of Pulse-Coupled Networks in Maximum Entropy Analysis.

Maximum entropy principle (MEP) analysis with few non-zero effective interactions successfully characterizes the distribution of dynamical states of pulse-coupled networks in many fields, e.g., in neuroscience. To better understand the underlying mechanism, we found a relation between the dynamical structure, i.e., effective interactions in MEP analysis, and the anatomical coupling structure of pulse-coupled networks and it helps to understand how a sparse coupling structure could lead to a sparse coding by effective interactions. This relation quantitatively displays how the dynamical structure is closely related to the anatomical coupling structure.

Mechanisms underlying contrast-dependent orientation selectivity in mouse V1.

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties-one of which is that contrast invariance in the orientation selectivity (OS) of the neurons' firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties-for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance-providing insights for future studies on contrast dependence (invariance).

The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic.

Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire neurons with delta-pulse coupling. While we show that the balanced state robustly persists in this system within a broad range of parameters, we mathematically prove that the largest Lyapunov exponent of this type of neuronal networks is negative. Therefore, the irregular firing activity can exist in the system without the chaotic dynamics. That is the irregularity of balanced neuronal networks need not arise from chaos.

Cascade model of wave turbulence.

We propose a cascade model of wave turbulence designed to simplify the study of this phenomenon in the way that shell models simplify the study of Navier-Stokes turbulence. The model consists of resonant quartets, in which some modes are driven and damped and others are shared by pairs of quartets and transferring energy between them, mimicking the natural energy transfer mechanism in weakly turbulent waves. A set of detailed-balance conditions singles out the case of the cascade model in equilibrium, for which we can explicitly derive a Gaussian equilibrium measure and a maximum-entropy principle using a Kolmogorov forward equation. Away from equilibrium, we can approximate the second-moment dynamics of the mode amplitudes using kinetic equations. In a nonequilibrium steady state, we can also approximate the higher moments of the driven-damped mode amplitudes and characterize the distribution of the shared-mode amplitudes as Gaussian. For this latter distribution, we find an information-theoretic argument, akin to entropy maximization, which lets us conclude that arbitrary initial shared-mode amplitude distributions approach Gaussian form in forward time. The cascade model may provide insight into mechanisms governing weakly turbulent wave systems and perhaps afford computational savings as compared to direct numerical simulations of the corresponding wavelike equations.

Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information.

The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems-it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.