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).
Aditya Murthy, Ph.D.
Associate Professor, Centre For Neuroscience, Indian Institute of Science, Bangalore, India
Since Karl Lashley’s seminal work on the formulation of serial order, numerous models assume simultaneous representation of competitive elements of a sequence, to account for serial order effects in different types of behavior like typing, speech, etc. Such models follow two basic assumptions: (1) more than one plan representation can be simultaneously active in a planning layer; (2) the most active plan is chosen in another layer called the competitive choice layer. Using the oculomotor system I will describe behavioral and neurophysiological experiments that tests the two critical predictions of such queuing models, providing evidence that basal ganglia in monkeys and humans instantiate a form of queuing that transforms parallel movement representations into more serial representations, allowing for the expression of sequential saccadic eye movements.
Sanjeeva Srivastava, Ph.D.
Assistant Professor, Proteomics Lab, IIT-Bombay, India
Identification of Potential Early Diagnostic Biomarkers for Gliomas and Various Infectious Diseases using Proteomic Technologies
The spectacular advancements achieved in the field of proteomics research during the last decade have propelled the growth of proteomics for clinical research. Recently, comprehensive proteomic analyses of different biological samples such as serum or plasma, tissue, CSF, urine, saliva etc. have attracted considerable attention for the identification of protein biomarkers as early detection surrogates for diseases (Ray et al., 2011). Biomarkers are biomolecules that can be used for early disease detection, differentiation between closely related diseases with similar clinical manifestations as well as aid in scrutinizing disease progression. Our research group is performing in-depth analysis of alteration in human proteome in different types of brain tumors and various pathogenic infections to obtain mechanistic insight about the disease pathogenesis and host immune responses, and identification of surrogate protein markers for these fatal human diseases.
Applying 2D-DIGE in combination with MALDI-TOF/TOF MS we have analyzed the serum and tissue proteome profiles of glioblastoma multiforme; the most common and lethal adult malignant brain tumor (Gollapalli et al., 2012) (Figure 1). Results obtained were validated by employing different immunoassay-based approaches. In serum proteomic analysis we have identified some interesting proteins like haptoglobin, ceruloplasmin, vitamin-D binding protein etc. Moreover, proteomic analysis of different grades (grade-I to IV) of gliomas and normal brain tissue was performed and differential expressions of quite a few proteins such as SIRT2, GFAP, SOD, CDC42 have been identified, which have significant correlation with the tumor growth. While proteomic analysis of cerebrospinal fluid from low grade (grade I & II) vs. high grade (grade III & IV) gliomas revealed modulation of CSF levels of apolipoprotein E, dickkopf related protein 3, vitamin D binding protein and albumin in high grade gliomas. The prospective candidates identified in our studies provide a mechanistic insight of glioma pathogenesis and identification of potential biomarkers. We are also studying the role of JAK/STAT interactome and therapeutic potential of STAT3 inhibitors in gliomas using proteomics approach. Several candidates of the JAK/STAT interactome were identified with altered expression and a significant correlation was observed between STAT3 and PDK1 transcript expression level.
We have also investigated the changes in human serum proteome in different infectious diseases including falciparum and vivax malaria (Ray et al., 2012a; Ray et al., 2012b), dengue (Ray et al., 2012c) and leptospirosis (Srivastava et al., 2012). Although, quite a few serum proteins were found to be commonly altered in different infectious diseases and might be a consequence of inflammation mediated acute phase response signaling, uniquely modulated candidates were identified in each pathogenic infection indicating the some inimitable responses. Further, a panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models employing PLSDA and other classification methods to predict the clinical phenotypic classes and 91.37% overall prediction accuracy was achieved (Figure 2). ROC curve analysis was carried out to evaluate the individual performance of classifier proteins. The excellent discrimination among the different disease groups on the basis of differentially expressed proteins demonstrates the potential diagnostic implications of this analytical approach.
Keywords: Diagnostic biomarkers, Gliomas, Infectious Diseases, Proteomics, Serum proteome
Acknowledgments: This disease biomarker discovery research was supported by Department of Biotechnology, India grant (No. BT/PR14359/MED/30/916/2010), Board of Research in Nuclear Sciences (BRNS) DAE young scientist award (2009/20/37/4/BRNS) and a startup grant 09IRCC007 from the IIT Bombay. The active support from Advanced Center for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Hospital (TMH), and Seth GS Medical College and KEM Hospital Mumbai, India in clinical sample collection process is gratefully acknowledged.
References :
- Ray S, Reddy PJ, Jain R, Gollapalli K. Moiyadi A, Srivastava S. Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead. Proteomics 11: 2139-61, 2011.
- Gollapalli K, Ray S, Srivastava R, Renu D, Singh P, Dhali S, Dikshit JB, Srikanth R, Moiyadi A, Srivastava S. Investigation of serum proteome alterations in human glioblastoma multiforme. Proteomics 12(14): 2378-90, 2012.
- Ray S, Renu D, Srivastava R, Gollapalli K, Taur S, Jhaveri T, Dhali S, Chennareddy S, Potla A, Dikshit JB, Srikanth R, Gogtay N, Thatte U, Patankar S, Srivastava S. Proteomic investigation of falciparum and vivax malaria for identification of surrogate protein markers. PLoS One 7(8): e41751, 2012a.
- Ray S, Kamath KS, Srivastava R, Raghu D, Gollapalli K, Jain R, Gupta SV, Ray S, Taur S, Dhali S, Gogtay N, Thatte U, Srikanth R, Patankar S, Srivastava S. Serum proteome analysis of vivax malaria: An insight into the disease pathogenesis and host immune response. J Proteomics 75(10): 3063-80, 2012b.
- Srivastava R, Ray S, Vaibhav V, Gollapalli K, Jhaveri T, Taur S, Dhali S, Gogtay N, Thatte U, Srikanth R, Srivastava S. Serum profiling of leptospirosis patients to investigate proteomic alterations. J Proteomics 76: 56-68, 2012.
- Ray S, Srivastava R, Tripathi K, Vaibhav V, Srivastava S. Serum proteome changes in dengue virus-infected patients from a dengue-endemic area of India: towards new molecular targets? OMICS 16(10): 527-36, 2012c.
* Correspondence: Dr. Sanjeeva Srivastava, Department of Biosciences and Bioengineering, IIT Bombay, Mumbai 400 076, India: E-mail: sanjeeva@iitb.ac.in; Phone: +91-22-2576-7779, Fax: +91-22-2572-3480
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.
Tejaswini Subbannayya, Nandini A. Sahasrabuddhe, Arivusudar Marimuthu, Santosh Renuse, Gajanan Sathe, Srinivas M. Srikanth, Mustafa A. Barbhuiya, Bipin Nair, Juan Carlos Roa, Rafael Guerrero-Preston, H. C. Harsha, David Sidransky, Akhilesh Pandey, T. S. Keshava Prasad and Aditi Chatterjee
Proteomic profiling of gallbladder cancer secretome – a source for circulatory biomarker discovery
Gallbladder cancer (GBC) is the fifth most common cancer of the gastrointestinal tract and one of the common malignancies that occur in the biliary tract (Misra et al. 2006; Lazcano-Ponce et al. 2001). It has a poor prognosis with survival of less than 5 years in 90% of the cases (Misra et al. 2003). The etiology is ill-defined. Several risk factors have been reported including cholelithiasis, obesity, female gender and exposure to carcinogens (Eslick 2010; Kumar et al. 2006). Poor prognosis in GBC is mainly due to late presentation of the disease and lack of reliable biomarkers for early diagnosis. This emphasizes the need to identify and characterize cancer biomarkers to aid in the diagnosis and prognosis of GBC. Secreted proteins are an important class of molecules which can be detected in body fluids and has been targeted for biomarker discovery. There are challenges faced in the proteomic interrogation of body fluids especially plasma such as low abundance of tumor secreted proteins, high complexity and high abundance of other proteins that are not released by the tumor cells (Tonack et al. 2009). Profiling of conditioned media from the cancer cell lines can be used as an alternate means to identify secreted proteins from tumor cells (Kashyap et al. 2010; Marimuthu et al. 2012). We analyzed the invasive property of 7 GBC cell lines (SNU-308, G-415, GB-d1, TGBC2TKB, TGBC24TKB, OCUG-1 and NOZ). Four cell lines were selected for analysis of the cancer secretome based on the invasive property of the cells. We employed isobaric tags for relative and absolute quantitation (iTRAQ) labeling technology coupled with high resolution mass spectrometry to identify and characterize secretome from the panel of 4GBC cancer cells mentioned above. In total, we have identified around 2,000 proteins of which 175 were secreted at differential abundance across all the four cell lines. This secretome analysis will act as a reservoir of candidate biomarkers. Currently, we are investigating and validating these candidate markers from GBC cell secretome. Through this study, we have shown mass spectrometry-based quantitative proteomic analysis as a robust approach to investigate secreted proteins in cancer cells.