Upinder S. Bhalla, Ph.D.
Professor & Dean, NCBS, Bengaluru, India
Watching the network change during the formation of associative memory
The process of learning is measured through behavioural changes, but it is of enormous interest to understand its cellular and network basis. We used 2-photon imaging of hippocampal CA1 pyramidal neuron activity in mice to monitor such changes during the acquisition of a trace conditioning task. One of the questions in such learning is how the network retains a trace of a brief conditioned stimulus (a sound), until the arrival of a delayed unconditioned stimulus (a puff of air to the eye). During learning, the mice learn to blink when the tone is presented, well before the arrival of the air puff.
The mice learnt this task in 20-50 trials. We observed that in this time-frame the cells in the network changed the time of their peak activity, such that their firing times tiled the interval between sound and air puff. Thus the cells seem to form a relay of activity. We also observed an evolution in functional connectivity in the network, as measured by groupings of correlated cells. These groupings were stable till the learning protocol commenced, and then changed. Thus we have been able to observe two aspects of network learning: changes in activity (relay firing), and changes in connectivity (correlation groups).
Gaute Einevoll, Ph.D.
Professor of Physics, Department of Mathematical Sciences & Technology, Norwegian University of Life Sciences (UMB)
Multiscale modeling of cortical network activity: Key challenges
Gaute T. Einevoll Computational Neuroscience Group, Norwegian University of Life Sciences, 1432 Ås, Norway; Norwegian National Node of the International Neuroinformatics Coordinating Facility (INCF)
Several challenges must be met in the development of multiscale models of cortical network activity. In the presentation I will, based on ongoing work in our group (http://compneuro.umb.no/ ) on multiscale modeling of cortical columns, outline some of them. In particular,
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Combined modeling schemes for neuronal, glial and vascular dynamics [1,2],
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Development of sets of interconnected models describing system at different levels of biophysical detail [3-5],
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Multimodal modeling, i.e., how to model what you can measure [6-12],
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How to model when you don’t know all the parameters, and
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Development of neuroinformatics tools and routines to do simulations efficiently and accurately [13,14].
References:
- L. Øyehaug, I. Østby, C. Lloyd, S.W. Omholt, and G.T. Einevoll: Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms, J Comput Neurosci 32, 147-165 (2012)
- I. Østby, L. Øyehaug, G.T. Einevoll, E. Nagelhus, E. Plahte, T. Zeuthen, C. Lloyd, O.P. Ottersen, and S.W. Omholt: Astrocytic mechanisms explaining neural-activity-induced shrinkage of extraneuronal space, PLoS Comp Biol 5, e1000272 (2009)
- T. Heiberg, B. Kriener, T. Tetzlaff, A. Casti, G.T. Einevoll, and H.E. Plesser: Firing-rate models can describe the dynamics of the retina-LGN connection, J Comput Neurosci (2013)
- T. Tetzlaff, M. Helias, G.T. Einevoll, and M. Diesmann: Decorrelation of neural-network activity by inhibitory feedback, PLoS Comp Biol 8, e10002596 (2012).
- E. Nordlie, T. Tetzlaff, and G.T. Einevoll: Rate dynamics of leaky integrate-and-fire neurons with strong synapses, Frontiers in Comput Neurosci 4, 149 (2010)
- G.T. Einevoll, F. Franke, E. Hagen, C. Pouzat, K.D. Harris: Towards reliable spike-train recording from thousands of neurons with multielectrodes, Current Opinion in Neurobiology 22, 11-17 (2012)
- H. Linden, T Tetzlaff, TC Potjans, KH Pettersen, S Grun, M Diesmann, GT Einevoll: Modeling the spatial reach of LFP, Neuron 72, 859-872 (2011).
- H. Linden, K.H. Pettersen, and G.T. Einevoll: Intrinsic dendritic filtering gives low-pass power spectra of local field potentials, J Computational Neurosci 29, 423-444 (2010)
- K.H. Pettersen and G.T. Einevoll: Amplitude variability and extracellular low-pass filtering of neuronal spikes, Biophysical Journal 94, 784-802 (2008).
- K.H. Pettersen, E. Hagen, and G.T. Einevoll: Estimation of population firing rates and current source densities from laminar electrode recordings, J Comput Neurosci 24, 291-313 (2008).
- K. Pettersen, A. Devor, I. Ulbert, A.M. Dale and G.T. Einevoll. Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities, Journal of Neuroscience Methods 154, 116-133 (2006).
- G.T. Einevoll, K. Pettersen, A. Devor, I. Ulbert, E. Halgren and A.M. Dale: Laminar Population Analysis: Estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex, Journal of Neurophysiology 97, 2174-2190 (2007).
- LFPy: A tool for simulation of extracellular potentials (http://compneuro.umb.no)
- E. Nordlie, M.-O. Gewaltig, H. E. Plesser: Towards reproducible descriptions of neuronal network models, PLoS Comp Biol 5, e1000456 (2009).
Kal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA
A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry
With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways. Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists. As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery. While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories. Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site. As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty. In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases. We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase. We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.
Aswath Balakrishnan, Kapaettu Satyamoorthy and Manjunath B Joshi
Introduction
Insulin resistance is a hall mark of metabolic disorders such as diabetes. Reduced insulin response in vasculature leads to disruption of IR/Akt/eNOS signaling pathway resulting in vasoconstriction and subsequently to cardiovascular diseases. Recent studies have demonstrated that inflammatory regulator interleukin-6 (IL-6), as one of the potential mediators that can link chronic inflammation with insulin resistance. Accumulating evidences suggest a significant role of epigenetic mechanisms such as DNA methylation in progression of metabolic disorders. Hence the present study aimed to understand the role of epigenetic mechanisms involved during IL-6 induced vascular insulin resistance and its consequences in cardiovascular diseases.
Materials and Methods
Human umbilical vein endothelial cells (HUVEC) and Human dermal microvascular endothelial cells (HDMEC) were used for this study. Endothelial cells were treated in presence or absence of IL-6 (20ng/ml) for 36 hours and followed by insulin (100nM) stimulation for 15 minutes. Using immunoblotting, cell lysates were stained for phosphor- and total Akt levels to measure insulin resistance. To investigate changes in DNA methylation, cells were treated with or without neutrophil conditioned medium (NCM) as a physiological source of inflammation or IL-6 (at various concentrations) for 36 hours. Genomic DNA was processed for HPLC analysis for methyl cytosine content and cell lysates were analyzed for DNMT1 (DNA (cytosine-5)-methyltransferase 1) and DNMT3A (DNA (cytosine-5)-methyltransferase 3A) levels using immunoblotting.
Results
Endothelial cells stimulated with insulin exhibited an increase in phosphorylation of Aktser 473 in serum free conditions but such insulin response was not observed in cells treated with IL-6, suggesting chronic exposure of endothelial cells to IL-6 leads to insulin resistance. HPLC analysis for global DNA methylation resulted in decreased levels of 5-methyl cytosine in cells treated with pro-inflammatory molecules (both by NCM and IL-6) as compared to untreated controls. Subsequently, analysis in cells treated with IL-6 showed a significant decrease in DNMT1 levels but not in DNMT3A. Other pro-inflammatory marker such as TNF-α did not exhibit such changes.
Conclusion
Our study suggests: a) Chronic treatment of endothelial cells with IL-6 results in insulin resistance b) Neutrophil conditioned medium and IL-6 decreases methyl cytosine levels c) DNMT1 but not DNMT3a levels are reduced in cells treated with IL-6.