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).
Rohit Manchanda, Ph.D.
Professor, Biomedical Engineering Group, IIT-Bombay, India
Modelling the syncytial organization and neural control of smooth muscle: insights into autonomic physiology and pharmacology
We have been studying computationally the syncytial organization and neural control of smooth muscle in order to help explain certain puzzling findings thrown up by experimental work. This relates in particular to electrical signals generated in smooth muscles, such as synaptic potentials and spikes, and how these are explicable only if three-dimensional syncytial biophysics are taken fully into account. In this talk, I shall provide an illustration of outcomes and insights gleaned from such an approach. I shall first describe our work on the mammalian vas deferens, in which an analysis of the effects of syncytial coupling led us to conclude that the experimental effects of a presumptive gap junction uncoupler, heptanol, on synaptic potentials were incompatible with gap junctional block and could best be explained by a heptanol-induced inhibition of neurotransmitter release, thus compelling a reinterpretation of the mechanism of action of this agent. I shall outline the various lines of evidence, based on indices of syncytial function, that we adduced in order to reach this conclusion. We have now moved on to our current focus on urinary bladder biophysics, where the questions we aim to address are to do with mechanisms of spike generation. Smooth muscle cells in the bladder exhibit spontaneous spiking and spikes occur in a variety of distinct shapes, making their generation problematic to explain. We believe that the variety in shapes may owe less to intrinsic differences in spike mechanism (i.e., in the complement of ion channels participating in spike production) and more to features imposed by syncytial biophysics. We focus especially on the modulation of spike shape in a 3-D coupled network by such factors as innervation pattern, propagation in a syncytium, electrically finite bundles within and between which the spikes spread, and some degree of pacemaker activity by a sub-population of the cells. I shall report two streams of work that we have done, and the tentative conclusions these have enabled us to reach: (a) using the NEURON environment, to construct the smooth muscle syncytium and endow it with synaptic drive, and (b) using signal-processing approaches, towards sorting and classifying the experimentally recorded spikes.
K. P. Mohanakumar, Ph.D.
Chief Scientist, Cell Biology & Physiology Division, Indian Institute of Chemical Biology, Kolkata
Neuroprotective and neurodestructive effects of Ayurvedic drug constituents: Parkinson’s disease
The present study reports the good and the bad entities in an Indian traditional medicine used for treating Parkinson’s disease (PD). A prospective clinical trial on the effectiveness of Ayurvedic medication in a population of PD patients revealed significant benefits, which has been attributed to L-DOPA present in the herbs [1]. Later studies revealed better benefits with one of the herbs alone, compared to pure L-DOPA in a clinical trial conducted in UK [2], and in several studies conducted on animal models of PD in independent laboratories world over [3-5]. We have adapted strategies to segregate molecules from the herb, and then carefully removed L-DOPA contained therein, and tested each of these sub-fractions for anti-PD activity in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rotenone and 6-hydroxydopamine -induced parkinsonian animal models, and transgenic mitochondrial cybrids. We report here two classes of molecules contained in the herb, one of which possessed severe pro-parkinsonian (phenolic amine derivatives) and the other having excellent anti-parkinsonian potential (substituted tetrahydroisoquinoline derivatives). The former has been shown to cause severe dopamine depletion in the striatum of rodents, when administered acutely or chronically. It also caused significant behavioral aberrations, leading to anxiety and depression [6]. The latter class of molecules administered in PD animal model [7], caused reversal of behavioral dysfunctions and significant attenuation of striatal dopamine loss. These effects were comparable or better than the effects of the anti-PD drugs, selegiline or L-DOPA. The mechanism of action of the molecule has been found to be novel, at the postsynaptic receptor signaling level, as well as cellular α-synuclein oligomerization and specifically at mitochondria. The molecule helped in maintaining mitochondrial ETC complex activity and stabilized cellular respiration, and mitochondrial fusion-fission machinery with specific effect on the dynamin related protein 1. Although there existed significant medical benefits that could be derived to patients due to the synergistic actions of several molecules present in a traditional preparation, accumulated data in our hands suggest complicated mechanisms of actions of Ayurvedic medication. Our results also provide great hope for extracting, synthesizing and optimizing the activity of anti-parkinsonian molecules present in traditional Ayurvedic herbs, and for designing novel drugs with novel mechanisms of action.
- N, Nagashayana, P Sankarankutty, MRV Nampoothiri, PK Mohan and KP Mohanakumar, J Neurol Sci. 176, 124-7, 2000.
- Katzenschlager R, Evans A, Manson A, Patsalos PN, Ratnaraj N, Watt H, Timmermann L, Van der Giessen R, Lees AJ. J Neurol Neurosurg Psychiatry.75, 1672-7, 2004.
- Manyam BV, Dhanasekaran M, Hare TA. Phytother Res. 18, 706-12, 2004.
- Kasture S, Pontis S, Pinna A, Schintu N, Spina L, Longoni R, Simola N, Ballero M, Morelli M. Neurotox Res. 15, 111-22, 2009.
- Lieu CA, Kunselman AR, Manyam BV, Venkiteswaran K, Subramanian T. Parkinsonism Relat Disord.16, 458-65, 2010.
- T Sengupta and KP Mohanakumar, Neurochem Int. 57, 637-46, 2010.
- T Sengupta, J Vinayagam, N Nagashayana, B Gowda, P Jaisankar and KP Mohanakumar, Neurochem Res 36, 177-86, 2011
Nader Pourmand, Ph.D.
Director, UCSC Genome Technology Center,University of California, Santa Cruz
Biosensor and Single Cell Manipulation using Nanopipettes
Approaching sub-cellular biological problems from an engineering perspective begs for the incorporation of electronic readouts. With their high sensitivity and low invasiveness, nanotechnology-based tools hold great promise for biochemical sensing and single-cell manipulation. During my talk I will discuss the incorporation of electrical measurements into nanopipette technology and present results showing the rapid and reversible response of these subcellular sensors to different analytes such as antigens, ions and carbohydrates. In addition, I will present the development of a single-cell manipulation platform that uses a nanopipette in a scanning ion-conductive microscopy technique. We use this newly developed technology to position the nanopipette with nanoscale precision, and to inject and/or aspirate a minute amount of material to and from individual cells or organelle without comprising cell viability. Furthermore, if time permits, I will show our strategy for a new, single-cell DNA/ RNA sequencing technology that will potentially use nanopipette technology to analyze the minute amount of aspirated cellular material.
Michelle Hermiston, MD, Ph.D.
Assistant Professor, Department of Pediatrics University of California San Francisco, USA
Interrogating Signaling Networks at the Single Cell Level In Primary Human Patient Samples
Multiparameter phosphoflow cytometry is a highly sensitive proteomic approach that enables monitoring of biochemical perturbations at the single cell level. By combining antisera to cell surface markers and key intracellular proteins, perturbations in signaling networks, cell survival and apoptosis mediators, cell cycle regulators, and/or modulators of other cellular processes can be analyzed in a highly reproducible and sensitive manner in the basal state and in response to stimulation or drug treatment. Advantages of this approach include the ability to identify the biochemical consequences of genetic and/or epigenetic changes in small numbers of cells, to map potential interplay between various signaling networks simultaneously in a single cell, and to interrogate potential mechanisms of drug resistance or response in a primary patient sample. Application of this technology to patients with acute lymphoblastic leukemia or the autoimmune disease systemic lupus erythematosus (SLE) will be discussed.
Lalitha Subramanian, Ph.D.
Chief Scientific Officer & VP, Services at Scienomics, USA
Nanoscale Simulations – Tackling Form and Formulation Challenges in Drug Development and Drug Delivery
Lalitha Subramanian, Dora Spyriouni, Andreas Bick, Sabine Schweizer, and Xenophon Krokidis Scienomics
The discovery of a compound which is potent in activity against a target is a major milestone in Pharmaceutical and Biotech industry. However, a potent compound is only effective as a therapeutic agent when it can be administered such that the optimal quantity is transported to the site of action at an optimal rate. The active pharmaceutical ingredient (API) has to be tested for its physicochemical properties before the appropriate dosage form and formulation can be designed. Some of the commonly evaluated parameters are crystal forms and polymorphs, solubility, dissolution behavior, stability, partition coefficient, water sorption behavior, surface properties, particle size and shape, etc. Pharmaceutical development teams face the challenge of quickly and efficiently determining a number of properties with small quantities of the expensive candidate compounds. Recently the trend has been to screen these properties as early as possible and often the candidate compounds are not available in sufficient quantities. Increasingly, these teams are leveraging nanoscale simulations similar to those employed by drug discovery teams for several decades. Nanoscale simulations are used to predict the behavior using very little experimental data and only if this is promising further experiments are done. Another aspect where nanoscale simulations are being used in drug development and drug delivery is to get insights into the behavior of the system so that process failures can be remediated and formulation performance can be improved. Thus, the predictive screening and the in-depth understanding leads to experimental efficiency resulting in far-reaching business impacts.
With specific examples, this talk will focus on the different types of nanoscale simulations used to predict properties of the API in excipients and also provide insight into system behavior as a function of shelf life, temperature, mechanical stress, etc.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.