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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).

Abhijeet Kate, Arpana G Panicker, Diana Writer, Giridharan P, Keshav K V Ramamoorthy, Saji George, Shailendra K Sonawane

Protoplast fusion and transformation: A tool for activation of latent gene clusters

In the quest to discover new bioactive leads for unmet medical needs, actinomycetes present a treasure trove of undiscovered molecules. The ability of actinomycetes to produce antibiotics and other bioactive secondary metabolites has been underestimated due to sparse studies of cryptic gene clusters. These gene clusters can be tapped to explore scaffolds hidden in them. The up-regulation of the dormant genes is one of the most important areas of interest in the bioactive compounds discovery from microbial resources. Genome shuffling is a powerful tool for the activation of such gene clusters. Lei Yu, et al.1, reported enhancement of the lactic acid production in Lactobacillus rhamnosus through genome shuffling brought about by protoplast fusion. D. A. Hopwood et al.2 suggested that an interspecific recombination between strains producing different secondary metabolites, generate producers of ‘hybrid’ antibiotics. They also mentioned that an intraspecific fusion of actinomycetes protoplast bring about random and high frequency recombination. Protoplasts can also be used as recipients for isolated DNA, again in the presence of polyethylene glycol (PEG). In our study we had undertaken random genome shuffling by protoplast fusion of two, rather poorly expressed actinomycetes strains A (Figure 1) & B (Figure 2), mediated by PEG; and also by naked DNA transformation of Strain A protoplast with the DNA of Strain B. We generated eight protoplast fusants and seven transformants from parents considering their morphological difference from the two parent strains. These 15 recombinants were checked for their same colony morphologies for five generations to ensure phenotypic stability. Antibiotic resistance pattern was established by using antibiotic octodisc to generate a marker profile of the recombinants and the parent strains. Eight fusants (AP-18, AP-25, AP-2, AP-11, AP-14, AP-19, AP-11 and AP-27) and four transformants (TAP-30, TAP-31, TAP-32 and TAP-33) (Table 1) have shown a different antibiotic sensitivity pattern as compared to the parent strains. We envisage that these recombinants harbor shuffled gene clusters. To support array of conditions to express such shuffled/cryptic genes the recombinants were fermented in 11 different nutrient stress variants. The extracts generated were subjected to metabolite profiling by HPLC-ELSD, bioactivity screening for cytotoxicity and anti-infective capabilities. Two fusants AP-11 (Figure 3) and AP-25; one transformant TAP-32 (in growth media MBA-5 and MBA-7) displayed antifungal activity unlike parent strains (Table 2) Fusant AP-11 (Table 5) exhibited significant cell growth inhibition of five different cancer cell lines. The parents Strain A and Strain B did not exhibit any cell growth inhibition of these cell lines (Table 5). The metabolite profiling of fusant AP-11 and transformant TAP-32 was done by HPLC-ELSD. AP-11 showed the presence of five additional peaks (Figure 5 & Figure 6); TAP-32 extract from medium MBA-5 (Figure 7 & Figure 8) showed the presence of four additional peaks and TAP-32 extract from MBA-7 (Figure 9 & Figure 10) showed 14 additional peaks as compared to parent strains in similar medium and media controls. The study indicated that protoplast fusion and transformation have not only caused morphological changes but also shuffled genes responsible for synthesis of bioactive molecules. Further characterization of these new peaks is warranted.

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Jaydeep Unni, Ph.D.
Sr. Project Manager, Robert Bosch Healthcare Systems, Palo Alto, CA

Remote Patient Monitoring – Challenges and Opportunities

Remote Patient Monitoring (RPM) is gaining importance and acceptance with rising number of chronic disease conditions and with increase in the aging population. As instances of Heart diseases, Diabetes etc are increasing the demand for these technologies are increasing. RPM devices typically collect patient vital sign data and in some case also patient responses to health related questions. Thus collected data is then transmitted through various modalities (wireless/Bluetooth/cellular) to Hospitals/Doctor’s office for clinical evaluation. With these solutions Doctors are able to access patient’s vital data ‘any time any where’ thus enabling them to intervene on a timely and effective manner. For older adult population chronic disease management, post-acute care management and safety monitoring are areas were RPM finds application. That said, there are significant challenges in adoption of Remote Patient Monitoring including patient willingness and compliance for adoption, affordability, availability of simpler/smarter technology to mention a few.  But experts contend that if implemented correctly Remote Patient Monitoring can contain healthcare expenditure by reducing avoidable hospitalization while greatly improving quality of care.

Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.

Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law

The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal  ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate  the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory  rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal  ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits  power law.

Gokul Das, Ph.D.
Co-Director, Breast Disease Site Research Group, Roswell Park Cancer Institute, Buffalo, NY

Probing Estrogen Receptor−Tumor Suppressor p53 Interaction in Cancer: From Basic Research to Clinical Trial

Tumor suppressor p53 and estrogen receptor have opposite roles in the onset and progression of breast cancer. p53 responds to a variety of cellular of stresses by restricting the proliferation and survival of abnormal cells. Estrogen receptor plays an important role in normal mammary gland development and the preservation of adult mammary gland function; however, when deregulated it becomes abnormally pro-proliferative and greatly contributes to breast tumorigenesis. The biological actions of estrogens are mediated by two genetically distinct estrogen receptors (ERs): ER alpha and ER beta. In addition to its expression in several ER alpha-positive breast cancers and normal mammary cells, ER beta is usually present in ER alpha-negative cancers including triple-negative breast cancer. In spite of genetically being wild type, why p53 is functionally debilitated in breast cancer has remained unclear. Our recent finding that ER alpha binds directly to p53 and inhibits its function has provided a novel mechanism for inactivating genetically wild type p53 in human cancer. Using a combination of proliferation and apoptosis assays, RNAi technology, quantitative chromatin immunoprecipitation (qChIP), and quantitative real-time PCR (qRT-PCR), in situ proximity ligation assay (PLA), and protein expression analysis in patient tissue micro array (TMA), we have demonstrated binding of ER alpha to p53 and have delineated the domains on both the proteins necessary for the interaction. Importantly, ionizing radiation inhibits the ER-p53 interaction in vivo both in human cancer cells and human breast tumor xenografts in mice. In addition, antiestrogenstamoxifen and faslodex/fulvestrant (ICI 182780) disrupt the ER-p53 interaction and counteract the repressive effect of ER alpha on p53, whereas 17β-estradiol (E2) enhances the interaction. Intriguingly, E2 has diametrically opposite effects on corepressor recruitment to a p53-target gene promoter versus a prototypic ERE-containing promoter. Thus, we have uncovered a novel mechanism by which estrogen could be providing a strong proliferative advantage to cells by dual mechanisms: enhancing expression of ERE-containing pro-proliferative genes while at the same time inhibiting transcription of p53-dependent anti-proliferative genes. Consistently, ER alpha enhances cell cycle progression and inhibits apoptosis of breast cancer cells. Correlating with these observations, our retrospective clinical study shows that presence of wild type p53 in ER-positive breast tumors is associated with better response to tamoxifen therapy. These data suggest ER alpha-p53 interaction could be one of the mechanisms underlying resistance to tamoxifen therapy, a major clinical challenge encountered in breast cancer patients. We have launched a prospective clinical trial to analyze ER-p53 interaction in breast cancer patient tumors at Roswell Park Cancer Institute. Our more recent finding that ER beta has opposite functions depending on the mutational status of p53 in breast cancer cells is significant in understanding the hard-to-treat triple-negative breast cancer and in developing novel therapeutic strategies against it. Our integrated approach to analyze ER-p53 interaction at the basic, translational, and clinical research levels has major implications in the diagnosis, prognosis, and treatment of breast cancer.

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.

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