A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases.

Early regulators of disease may increase understanding of disease mechanisms and serve as markers for presymptomatic diagnosis and treatment. However, early regulators are difficult to identify because patients generally present after they are symptomatic. We hypothesized that early regulators of T cell-associated diseases could be found by identifying upstream transcription factors (TFs) in T cell differentiation and by prioritizing hub TFs that were enriched for disease-associated polymorphisms. A gene regulatory network (GRN) was constructed by time series profiling of the transcriptomes and methylomes of human CD4(+) T cells during in vitro differentiation into four helper T cell lineages, in combination with sequence-based TF binding predictions. The TFs GATA3, MAF, and MYB were identified as early regulators and validated by ChIP-seq (chromatin immunoprecipitation sequencing) and small interfering RNA knockdowns. Differential mRNA expression of the TFs and their targets in T cell-associated diseases supports their clinical relevance. To directly test if the TFs were altered early in disease, T cells from patients with two T cell-mediated diseases, multiple sclerosis and seasonal allergic rhinitis, were analyzed. Strikingly, the TFs were differentially expressed during asymptomatic stages of both diseases, whereas their targets showed altered expression during symptomatic stages. This analytical strategy to identify early regulators of disease by combining GRNs with genome-wide association studies may be generally applicable for functional and clinical studies of early disease development.

Network effects of synaptic modifications.

In this paper, we use computational models of varying complexity to investigate the role of synaptic modifications for cortical network properties. In particular, we study how the dynamics can be regulated by neuromodulators, intrinsic noise and chemical agents. We focus on the complex neurodynamics and its modulation, and how this is related to the neural circuitry, where connectivity enhancement and pruning is considered. The emphasis is on the overall network structures, with feedforward and feedback loops between excitatory and inhibitory neurons at several layers and distances, and less details at the synaptic level. Our models aim at linking processes at a molecular and cellular (microscale), with processes at a network level (mesoscale), which in turn are linked to the mental processes and cognitive functions (macroscale). We also discuss the relevance of these results for clinical and experimental neuroscience, with applications to learning, memory, arousal, and mental disorders.

Spontaneously active cells induce state transitions in a model of olfactory cortex.

The existence of neurons with intrinsic oscillations does not in itself explain the synchronization of local populations of neurons, but it is likely to pace population rhythms when the neurons are suitably coupled by chemical and/or electrical synapses. In the present study, we have investigated the role of spontaneously active cells as noisy or pacemaker units in setting global oscillations in a three-layered cortical model. The presence of a small number of noisy (spontaneously active) units induce oscillations at the network level in the range of the gamma rhythm. The number of noisy units in the network and their type (excitatory or inhibitory or excitatory and inhibitory together) determines the emergence of regular oscillations or aperiodic (chaotic) behaviour. It also determines the onset of the global behaviour. On replacing a noisy unit by a pacemaker unit, similar gamma oscillations were generated. With both noisy and pacemaker units, we found that certain characteristics of the spontaneous activity determine the delay period for the onset of global activity. Preliminary studies have been carried out with spontaneously active units having a chaotic dynamics but the results are much similar to that with a noisy burst. Different functional roles have been suggested for cortical oscillations, such as determining global functional states and specifying connectivity during development. Oscillations at different frequency bands, in particular in the gamma band (around 40 Hz), have also been associated with memory and attention. The presence of spontaneously active neurons, either with noisy or oscillatory activity, could be responsible for global oscillations in the absence of external stimuli in certain cortical areas in the mature brain.

Effects of non-synaptic neuronal interaction in cortex on synchronization and learning.

During neural communication by means of action potentials, small electromagnetic (EM) fields are also generated. We use a three layered cortical neural network model to study the effects of EM fields and gap junctions on spatio-temporal network activity. We investigate the possible role of these effects in synchronizing activity, a phenomenon which has been observed in the olfactory cortex and the hippocampus. The simulation results support the notion that fast synchronization of activity in distant parts of the neural network are made possible by means of EM fields and/or gap junctions. The results also indicate that these effects, to a certain extent, are beneficial to system performance.

On the coevolution of cognition and consciousness.

In this article it is argued that an evolutionary perspective leads to the view that adaptation and learning is a widespread and old property of living organisms, even as old as life itself. Cognition, defined as knowledge processing mediated by a centralised nervous system, is suggested mainly to be based on the same principles as non-neural adaptive processes. The emergence of conscious cognition, however, is seen as a major transition in the evolution of life, although it appears in different degrees and at various stages in evolution. Both cognition and consciousness depend on the organisation and complexity of the organism, primarily with regard to the nervous system. Computational and neurophysiological approaches are discussed, in particular some experimental attempts to determine anatomical, physiological and physical correlates to consciousness. It is argued that an evolutionary perspective suggests an interactionistic solution to the mind-brain problem, i.e. the question of subjective experience. In an interactionistic perspective consciousness can be understandable as a biological phenomenon. It can be regarded as a driving force in evolution, amplifying and improving the adaptive and cognitive processes of an organism.

Global effects of fluctuations in neural information processing.

We are interested in how the complex dynamics of the brain, which may include oscillations, chaos and noise, can affect the efficiency of neural information processing. Here, we consider the amplification and functional role of fluctuations, expressed as chaos or noise in the system. Using computer simulations of a neural network model of the olfactory cortex, we demonstrate how microscopic fluctuations can result in global effects at the network level. In particular, we show that the rate of information processing in associative memory tasks can be maximized for optimal noise levels. Noise can also induce transitions between different dynamical states, related to learning and memory. A chaotic-like behavior, induced by noise or by an increase in neuronal excitability, can enhance system performance if it is transient and converges to a limit cycle memory state. The level of accuracy required for correct pattern association further affects the rate of information processing. We discuss how neuromodulatory control of the cortical dynamics can shift the balance between rate and accuracy optimization, as well as between sensitivity and stability.

Cholinergic modulation of cortical oscillatory dynamics.

1. The effect of cholinergic modulation on cortical oscillatory dynamics was studied in a computational model of the piriform (olfactory) cortex. The model included the cholinergic suppression of neuronal adaptation, the cholinergic suppression of intrinsic fiber synaptic transmission, the cholinergic enhancement of interneuron activity, and the cholinergic suppression of inhibitory synaptic transmission. 2. Electroencephalographic (EEG) recordings and field potential recordings from the piriform cortex were modeled with a simplified network in which cortical pyramidal cells were represented by excitatory input/output functions with gain parameters dependent on previous activity. The model incorporated distributed excitatory afferent input and excitatory connections between units. In addition, the model contained two sets of inhibitory units mediating inhibition with different time constants and different reversal potentials. This model can match effectively the patterns of cortical EEG and field potentials, showing oscillatory dynamics in both the gamma (30-80 Hz) and theta (3-10 Hz) frequency range. 3. Cholinergic suppression of neuronal adaptation was modeled by reducing the change in gain associated with previous activity. This caused an increased number of oscillations within the network in response to shock stimulation of the lateral olfactory tract, effectively replicating the effect of carbachol on the field potential response in physiological experiments. 4. Cholinergic suppression of intrinsic excitatory synaptic transmission decreased the prominence of gamma oscillations within the network, allowing theta oscillations to predominate. Coupled with the cholinergic suppression of neuronal adaptation, this caused the network to shift from a nonoscillatory state into an oscillatory state of predominant theta oscillations. This replicates the longer term effect of carbachol in experimental preparations on the EEG potential recorded from the cortex in vivo and from brain-slice preparations of the hippocampus in vitro. Analysis of the model suggests that these oscillations depend upon the time constant of neuronal adaptation rather than the time constant of inhibition or the activity of bursting neurons. 5. Cholinergic modulation may be involved in switching the dynamics of this cortical region between those appropriate for learning and those appropriate for recall. During recall, the spread of activity along intrinsic excitatory connections allows associative memory function, whereas neuronal adaptation prevents the spread of activity between different patterns. During learning, the recall of previously stored patterns is prevented by suppression of intrinsic excitatory connections, whereas the response to the new patterns is enhanced by suppression of neuronal adaptation.

Noise-enhanced performance in a cortical associative memory model.

Spontaneous neuronal activity and synaptic noise are well-known phenomena, but their biological significance has not yet been assessed. Using a computer model of the olfactory cortex we show that such activity, expressed as temporal noise in the model, can reduce recall time in associative memory tasks. We investigate both additive and multiplicative noise, and find optimal noise levels for which the recall time reaches a minimum. In addition, we demonstrate that noise can induce state transitions, such that the system is pushed from one attractor state to another. For high enough noise levels the dynamics can change dramatically and, for example, switch from an oscillatory to a chaos-like behavior. We discuss these findings in light of their significance for neural information processing.

Computer models of the brain--how far can they take us?

The recent developments in computer capacity and algorithms, together with a tremendous growth of data in neuroscience have dramatically improved the possibilities of modeling and simulating certain brain structures and activities with a considerable degree of realism. Although there is still a long way to go, some claim that we will one day be able to create artificial "brains" with similar capacity to the human brain, perhaps even surpassing it. Here we focus on these perspectives, discussing the potentials and limitations of today's computer models, and how far they might be able to take us.

Site dependent time optimization of protein synthesis with special regard to accuracy.

The efficiency of protein synthesis is determined by its rate, accuracy, and energy consumption. With the energy consumption fixed, we optimize the system with respect to time and accuracy. Using an analytic model for a simple system and computer simulations for more complex systems, where also the possibility of errors is included, we demonstrate how different parts of the messenger RNA influence the protein production rate differently. The first part of the coding sequence is of major importance, since the availability of empty initiation sites is crucial, and queuing back to that region may interfere with initiation. The elongation rate at different positions depends on codon usage, on the concentrations of substrate and co-factors, and on the kinetic rate constants, including those of the proofreading branch(es). Ribosomal proofreading is a time consuming process and by allowing for more errors in the beginning of a protein, it is possible to increase the production rate of that protein. We calculate the mean translation time per functioning protein for various translation accuracies, and discuss the different strategies open to living cells.

Maintenance of accuracy during amino acid starvation.

The kinetics of the tRNA cycle is in itself capable of keeping the translational error level almost unaffected by amino acid starvation. There is no need to assume any yet unknown mechanism or property. Kinetic analysis shows that the concentration of aminoacyl-tRNA can stay high even for large reductions in aminoacylation, since the pool of uncharged tRNA normally is very small. An enhanced binding of uncharged tRNA to the ribosome could increase the effect and produce an extremely efficient error damping. A similar result is obtained when EF-Tu is partially inhibited by ppGpp.

Theoretical modelling of protein synthesis.

This article provides an overview of the use of mathematical and computer modelling in furthering the understanding of protein synthesis. In particular, we discuss issues such as the nature of the rate limiting step(s), error rates, tRNA-codon adaptation, codon bias, attenuation control, and problems of selection and error corrections, focussing on their theoretical treatment.

Translation rate modification by preferential codon usage: intragenic position effects.

We present a model for calculating the protein production rate as a function of the translation rate. The model takes into account that the elongation rate along an mRNA molecule is non-uniform as a result of different tRNA availabilities for different codons. Initiation of ribosomes on an mRNA is normally the rate-limiting step in the translation process, and blocking of the initiation site can be avoided if the codons closest to this site allow fast translation by the ribosome. Hence, different selective forces may act on the choice of synonymous codons in the initiation region than elsewhere on a given mRNA. We show that the elongation rate along the whole mRNA influences the production rate of abundant proteins, whereas only the elongation rate in the initiation region is of importance for the production rate of rare proteins. We also present an analysis of the codon distribution along known mRNAs coding for abundant and rare proteins.

Error propagation in E. coli protein synthesis.

A new approach to the error catastrophe theory, proposed by Leslie Orgel, is presented here. Our model is a development of previous models, but differs in several respects: the overall activity is assumed to be dependent on the error level, the effect of errors in the translating system, giving rise to additional errors in the succeeding generation of products, is explicitly included as a special term in our model, and scavenging enzymes are assumed to break down and eliminate products with a loose structure. Their efficiency is dependent on the error level. The model also takes into account the dilution of incorrect ribosomes and enzymes, and is described by a time-dependence in terms of ribosome/enzyme generations. The model and the contribution to the time development are discussed in the light of experiments on E. coli treated with streptomycin.

The tRNA cycle and its relation to the rate of protein synthesis.

With the aid of a kinetic model, we have investigated how the adaptation between the various components of the tRNA cycle and the codon frequencies affects the rate of protein synthesis. Depending on the relative amounts of total tRNA, synthetase and ribosomes, the optimal correlations vary between a situation where all tRNA species are either present in equal amounts or are present in amounts proportional to the square-root of the corresponding codon frequencies, and a situation where the amounts of the different tRNA species present are linearly proportional to the codon frequencies.