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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,

1. Combined modeling schemes for neuronal, glial and vascular dynamics [1,2],

2. Development of sets of interconnected models describing system at different levels of biophysical detail [3-5],

3. Multimodal modeling, i.e., how to model what you can measure [6-12],

4. How to model when you don’t know all the parameters, and

5. Development of neuroinformatics tools and routines to do simulations efficiently and accurately [13,14].

References:

1. 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)
2. 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)
3. 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)
4. T. Tetzlaff, M. Helias, G.T. Einevoll, and M. Diesmann: Decorrelation of neural-network activity by inhibitory feedback, PLoS Comp Biol 8, e10002596 (2012).
5. 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)
6. 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)
7. 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).
8. 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)
9. K.H. Pettersen and G.T. Einevoll: Amplitude variability and extracellular low-pass filtering of neuronal spikes, Biophysical Journal 94, 784-802 (2008).
10. 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).
11. 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).
12. 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).
13. LFPy: A tool for simulation of extracellular potentials (http://compneuro.umb.no)
14. E. Nordlie, M.-O. Gewaltig, H. E. Plesser: Towards reproducible descriptions of neuronal network models, PLoS Comp Biol 5, e1000456 (2009).

Ayyappan Nair, Ph.D.
Head, Business Development (Technologies, Discovery Biology), Anthem Biosciences & DavosPharma, New Jersey, USA

Inhibition of NF-κB regulated gene expression by chrysoeriol suppresses tumorigenesis in breast cancer cells

Amrutha K¹, Pandurangan Nanjan¹, Sanu K Shaji¹, Damu Sunilkumar¹, Subhalakshmi K¹, Rashmi U Nair¹, Lakshmi Rajakrishna², Asoke Banerji¹, Ayyappan Ramesh Nair^1*,2

1. School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Clappana P.O., Kollam – 690 525, Kerala, India
2. Anthem Biosciences, No 49, Canara Bank Road, Bommasandra Industrial Area, Phase 1,  Hosur Road, Bangalore – 560 099, Karnataka, India

Abstract:  A large number of effective cancer-preventing compounds inhibit the activation of nuclear factor-κ B (NF-κB).  It has been previously demonstrated that some flavonoids that are a vital component of our diet inhibits this pathway. As a consequence, many flavonoids inhibit genes involved in various aspects of tumorigenesis and have thus emerged as potential chemopreventive candidates for cancer treatment. We studied the effect of 17 different flavonoids, including the highly evaluated quercetin on the NF-κB pathway, and on the expression of MMP-9 and COX-2 (two NF-κB regulated genes involved in metastasis) in the highly invasive human breast cancer cell line MDA-MB-231.  The findings suggest that not all the quercetin like flavone backbone compounds inhibit the NF-κB pathway, and that the highly hydoxylated flavonols quercetagetin and gossypetin did not inhibit this pathway, nor did it inhibit the expression of MMP-9 and COX-2.  This indicates a correlation between inhibition of NF-κB and subsequent suppression of these NF-κB regulated genes. Here, we also report the novel observation that the not so well characterized methoxylated flavone chrysoeriol inhibited the NF-κB pathway, and was most potent in reducing the expression of MMP-9 and COX-2.  Based on these observations, the cellular effects of chrysoeriol were evaluated in MDA-MB-231.  Chrysoeriol caused cell cycle arrest at G2/M, inhibited migration and invasion, and caused cell death of macrophages that contributed to migration of these cancer cells.  These effects of chrysoeriol make it a potential therapeutic candidate for breast cancer metastasis.

Claudia AM Wheeler-Kingshott, Ph.D.
University Reader in Magnetic Resonance Physics, Department of Neuroinflammation, UCL Institute of Neurology, London, UK

Abstract

Detecting neuronal activity in vivo non-invasively is possible with a number of techniques. Amongst these, in 1990 functional magnetic resonance imaging (fMRI) was proposed as a technique that has a great ability to spatially map brain activity by exploiting the blood oxygenation level dependent (BOLD) contrast mechanism [1, 2]. In fact, neuronal activation triggers a demand for oxygen and induces a localised increase in blood flow and blood volume, which actually exceeds the metabolic needs. This in turns causes an increase of oxyhaemoglobin in the venous compartment, which is a transient phenomenon and is accompanied by a transient change (decrease) in the concentration of deoxyhaemoglobin. Due to its paramagnetic properties, the amount of deoxyhaemoglobin present in the venous blood affects the local magnetic field seen by the spins (protons) and determines the local properties of the MR signal. A decrease in deoxyhaemoglobin during neuronal activity, therefore, induces local variations of this magnetic field that increases the average transverse relaxation time of tissue, measured via the T2* parameter [3]. This means that there is an increase of the MR signal (of the order of a few %, typically <5%) linked to metabolic changes happening during brain function. Activation can be inferred at different brain locations by performing tasks while acquiring the MR signal and comparing periods of rest to periods of activity.

The macroscopic changes of the BOLD signal are well characterised, while the reason for the increased blood supply, exceeding demands, needs further thoughts. Here we will discuss two approaches for explaining the BOLD phenomenon, one that links it to adenosine triphosphate production [4] and enzyme saturation, the other that relates it to the very slow diffusion of oxygen through the blood-brain-barrier with a consequent compensatory high demand of oxygen [5]. Some evidence of restricted oxygen diffusion has been shown by means of hypercapnia [6], although it is not excluded that both mechanisms may be present.

Overall, the BOLD signal changes theory and its physiological basis will be presented and discussed.

References

1. Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A, 1990. 87(24): p. 9868-72.
2. Kwong, K.K., et al., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A, 1992. 89(12): p. 5675-9.
3. Bandettini PA, et al. Spin-echo and gradient-echo EPI of human brain activation using BOLD contrast: a comparative study at 1.5 T. NMR Biomed. 1994 Mar;7(1-2):12-20
4.  Fox, P.T., et al., Nonoxidative glucose consumption during focal physiologic neural activity. Science, 1988. 241(4864): p. 462-4.
5. Gjedde, A., et al. Reduction of functional capillary density in human brain after stroke. J Cereb Blood Flow Metab, 1990. 10(3): p. 317-26.
6. Hoge, R.D., et al., Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proc Natl Acad Sci U S A, 1999. 96(16): p. 9403-8.

Binu K A^a, Jem Prabhakar^b, Thara S^c and Lakshmi S^d,∗

^aDepartment of Clinical Diagnostics Services and Translational Research, Malabar Cancer Centre, Thalassery, Kerala, India.
^bDivision of Surgical Oncology, Division of Pathology
^dDivision of Cancer Research, Regional Cancer Centre, Thiruvananthapuram, Kerala, India.

Introduction

AIB1, a member of the nuclear co activators, promotes the transcriptional activity of multiple nuclear receptors such as the ER and other transcription factors. Chemokines produced by stromal cells have potential to influence ERα-positive breast cancer progression to metastasis. CXCR4 is the physiological receptor for SDF1, together shown to stimulate the chemotactic and invasive behavior of breast cancer cells to serve as a homing mechanism to sites of metastasis. We propose that over expression of AIB1 in breast cancer cells leads to increased SDF1 and CXCR4 expression, which induces invasion and metastasis of cancer cells.

Materials and Methods
Breast tumor and normal breast tissues from patients in Regional Cancer Centre, Thiruvananthapuram were used for study. The modulatory effect of AIB1 was studied in MCF-7 cells with AIB1 siRNA transfection along with treatment of 17β-Estradiol (E2), 4-hydroxytamoxifen (4OHT), combinations of E2 and 4OHT. The gene expression pattern and protein localization were assessed by RT-PCR and immunofluorescence microscopy respectively. The metastatic and invasive properties were assessed by wound healing assay. Quantitative colocalization analyses were done to assess the association of proteins using Pearson’s correlation coefficient.

Result and Conclusion
The mRNA and protein level expression of AIB1, CXCR4 and SDF1 were higher in tumor samples than in normal samples. AIB1 was localized to the nuclei whereas CXCR4 and SDF1 immunoreactivity were observed in the cytoplasm and to a lesser extent in the nuclei of tumor epithelial cells. In tumor samples the gene level expressions of AIB1 showed significant positive correlations with SDF1(r = 0.213, p = 0.018). CXCR4 showed significant positive correlation with SDF1 in gene (r = 0.498, p = 0.000) and protein levels(r = 0.375, p = 0.002). Quantitative colocalization analyses showed a marked reduction in expression of CXCR4 and SDF1 in siAIB1MCF-7 cells than MCF-7 cells with different treatment groups. Wound healing assay shows reduced wound healing in siAIB1 treated MCF-7 cells.

In recent years, targeting specific cancer pathways and key molecules to arrest tumor growth and achieve tumor eradication have proven a challenge; due to acquired resistance and homing of cancer cells to various metastatic sites. The present study revealed that silencing AIB1 can prevent the over expression of SDF1 and CXCR4. Co activator levels determine the basal and estrogen-inducible expression of SDF1, a secreted protein that controls breast cancer cell proliferation and invasion through autocrine and paracrine mechanisms (Hall et al. 2003). The effects of CXCR4 overexpression has been correlated with SDF1 mediated activation of downstream signaling via ERK1/2 and p38 MAPK and with an enhancement of ER-mediated gene expression (Rhodes et al. 2011). It is possible that over expression of AIB1 as a stimulant involved in the expression of CXCR4 might up-regulate the expression of prometastatic and angiogenic genes. Thus based on these observations it can be concluded that SDF1/CXCR4 overexpression, with significant association with AIB1 expression, itself contribute to the development of mammary cancer and metastatic progression.

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