Our research examines how stressors such as chronic diseases alter the dynamics and plasticity of neurons and neural microcircuits.
We focus on the neurodegenerative motor neuron disease, Amyotrophic Lateral Sclerosis (ALS), commonly known as Lou Gehrig’s disease. ALS is characterized by progressive loss of motor neurons, the final common pathway neurons which drive all skeletal musculature. The diverse causes and progression rates of ALS have hampered its early diagnosis and effective treatment. Through the development of a comprehensive roadmap of disease dynamics beginning at the neonatal stage, we seek to establish early biomarkers and devise ways to delay the onset and progression of neurodegeneration in ALS.
Our ongoing work addresses three key questions:
What makes neurons selectively vulnerable to degeneration?
Selective vulnerability is a hallmark of neurodegenerative diseases. However in ALS, not all motor neurons are equally vulnerable. For instance motor neurons controlling eye muscles and those innervating muscle spindles (gamma motor neurons) are resistant to degeneration. Even within vulnerable motor pools, there is preferential death of large motor neurons which are important to produce forceful muscle contractions. Our research explores what properties are different between the vulnerable and resistant motor neurons. Our working hypothesis is that cell-specific markers of disease resistance can offer strategies to delay degeneration of vulnerable neurons.
How are neural circuits reconfigured by the disease?Normally, neurons regulate their excitability relative to network function – an ability known as homeostatic plasticity. However, neurons often become hyperexcitable during disease development which alters their set point operation. Arguably this change is instigated by a disruption of network homeostasis within so-called vulnerable microcircuits. We are in search of short- and long-range circuit elements including sensory, premotor and cortical neurons as well as astrocytes, microglia and oligodendrocytes, which can contribute to network dyshomeostasis. Delineating the altered physiology and molecular signatures of these other cells can suggest targets for effective treatment.
What are the dynamic signatures of disease development?
Diseases are elusive and mal-adaptive. To understand such complexity, our unique approach combines mathematical modeling of disease dynamics. Mathematical modeling offers a powerful methodology to integrate discrete experimental findings into integrative computer-based platforms for disease prediction. We develop data-driven models and use them to uncover the promiscuous way in which disease disrupts normal dynamics of ion channels, neurons and networks. We also use these models to test strategies for normalizing neural excitability in real-time. By developing these models, we further seek to delineate clinically relevant physiological parameters and operational regimes to aid disease prediction and design of effective therapeutic strategies.
These investigations use transgenic mouse models and diverse approaches:
- Patch-clamp electrophysiology coupled with dynamic clamp
- Mathematical modeling
- Systems and computational biology
- Protein quantification using cell-specific assays
We are grateful for the generous support from:
- Dr. Scott Chandler Lab (Integrative Biology and Physiology)
- Dr. Martina Wiedau-Pazos Lab (Neurology)
- Dr. Xia Yang Lab (Integrative Biology and Physiology)
- Dr. Igor Spigelman Lab (Section of Oral Biology, Dentistry)
- Dr. Riccardo Olcese Lab (Anesthesiology and Perioperative Medicine, Physiology)
- Dr. Michael Levine Lab (Department of Psychiatry and Biobehavioral Sciences)
Sharmila Venugopal, Ph.D.
Assistant Adjunct Professor
Department of Integrative Biology and Physiology
Soju Seki, Ph.D.
Jakob von Morgenland
Applied Developmental Psychology Minor
Biomedical Research Minor
Jessica Lizeth Torres
Physiological Sciences Major
♥ Corresponding author ♦ Postdoc Undergraduate ♠ Book chapter
W Liu, S Venugopal, S Majid, IS Ahn, G Diamante, J Hong, X Yang♥, SH Chandler♥, “Single-cell RNA-seq Analysis of the Brainstem of Mutant SOD1 mice Reveals Perturbed Cell Types and Pathways of Amyotrophic Lateral Sclerosis”, Neurobiology of Disease, 2020 April; 104877. PMID: 32360664.
J von Morgenland, S Venugopal♥, “Hill’s Model for Muscle Physiology and Biomechanics”, In: Encyclopedia of Computational Neuroscience, Section on Models of Motor Neurons and Neuromuscular Systems, D Jaeger, R Jung (Eds), Springer, New York, NY, April 2020 [link] ♠.
A Denizot, H Berry, S Venugopal♥, “Intracellular Ca2+ Dynamics in Astrocytes: Modeling the Underlying Spatiotemporal Diversity”, In: Encyclopedia of Computational Neuroscience, Section on Astrocyte Models, D Jaeger, R Jung (Eds), Springer, New York, NY, March 2020 [link] ♠.
S Venugopal, “What makes neurons good listeners?”, Society for Industrial and Applied Mathematics (Invited article for SIAM News), Lina Sorg (Eds.), 1-5, Dec 2019. [link].
S Seki♦, T Yamamoto, K Quinn, I Spigelman, A Pantazis, R Olcese, M Wiedau-Pazos, SH Chandler♥, S Venugopal♥, “Circuit-specific early impairment of proprioceptive sensory neurons in the SOD1G93A mouse model for ALS”, Journal of Neuroscience, 2019 Oct 30; 39(44):8798-8815. PMID: 31530644.
S Venugopal♥, S Seki♦, DH Terman, A Pantazis, R Olcese, M Wiedau-Pazos, SH Chandler, “Resurgent Na+ current offers noise modulation in bursting neurons”, PLOS Computational Biology, 2019 Jun 21;15(6):e1007154. PMID: 31226124.
S Venugopal♥, R Srinivasan♥, BS Khakh♥, “GECIquant: semi-automated detection and quantification of astrocyte intracellular Ca2+ signals monitored with GCaMP6f”, In: Computational Glioscience, Springer International Publishing, M De Pitta, H Berry (Eds), DOI: 10.1007/978-3-030-00817-8_17, 1st edition (2018)♠.
R Srinivasan, BS Huang, S Venugopal, AD Johnston, H Chai, H Zeng, P Golshani & BS Khakh, “Physiological Ca2+ signaling in astrocytes from IP3R2-/- mice in brain slices and during startle responses in vivo”, Nature Neuroscience, May 2015, 18(5):708-17. PMID: 25894291.
S Venugopal, CF Hsiao, T Sonoda, M Wiedau-Pazos & SH Chandler, “Homeostatic dysregulation in membrane properties of masticatory motoneurons compared to oculomotor neurons in a mouse model for Amyotrophic Lateral Sclerosis”, Journal of Neuroscience, January 2015, 35(2): 707-720. PMID: 25589764.
S Venugopal♥, “Connectionist Models of CPG Networks”, In: Encyclopedia of Computational Neuroscience, Section on Brain-Scale Networks, D Jaeger, R Jung (Eds), Springer-Verlag New York, ISBN: 978-1-4614-6676-5, 1st edition (2015)♠.
S Venugopal♥, “Conductance-based models of nonlinear dynamics in vertebrate motoneurons”, In: Encyclopedia of Computational Neuroscience, Section on Models of Motor Neurons and Neuromuscular Systems, D Jaeger, R Jung (Eds), Springer-Verlag New York, Print, eBook ISBN: 978-1-4614-6676-5, 1st edition (2015) [link] ♠.
S Venugopal♥, “Models of motoneurons and neuromuscular systems: Overview”, In: Encyclopedia of Computational Neuroscience, D Jaeger, R Jung (Eds), Springer-Verlag New York, Print, eBook ISBN: 978-1-4614-6676-5, 1st edition (2015)♠.
S Venugopal, TM Hamm & R Jung, “Differential contributions of somatic and dendritic calcium-dependent potassium currents to the control of motoneuron excitability following spinal cord injury”, Cognitive Neurodynamics, February 2012, 6(3):283-293. PMID: 23730358.
S Venugopal, TM Hamm, SM Crook & R Jung, “Modulation of inhibitory strength and kinetics facilitates regulation of persistent inward currents and motoneuron excitability following spinal cord injury”, Journal of Neurophysiology, July 2011, 106:2167-2179. PMID: 21775715.
S Venugopal, S Crook, M Srivatsan & R Jung, “Principles of Computational Neuroscience”, In: Biohybrid Systems – Nerves, Interfaces and Machines, John Wiley & Sons, ISBN: 3527409491, 9783527409495 (2011)♠.
S Venugopal, JA Boulant, Z Chen & JB Travers, “Intrinsic membrane properties of pre-oromotor neurons in the intermediate zone of the medullary reticular formation”, Neuroscience, June 2010, 168(1):31-47. PMID: 20338224.
J Nasse, DH Terman, S Venugopal, G Hermann, R Rogers & JB Travers, “Local circuit input to the medullary reticular formation from the rostral nucleus of the solitary tract”, American Journal of Physiology, November 2008, 295(5): R1391-408. PMID: 18716034.
S Venugopal, JB Travers, DH Terman, “A computational model for motor pattern switching of taste-induced ingestion and rejection oromotor behaviors”, Journal of Computational Neuroscience, April 2007, 22(2):223-38. PMID: 17072755.
S Venugopal, CR Castro-Pareja, O Dandekar, “An FPGA-based 3D image processor with median and convolution filters for real-time applications” International Society for Optical Engineering – SPIE Annual Conference on Medical Imaging, 2005, vol. 5671, pp. 174-182.
CR Castro-Pareja, JM Jagadeesh, S Venugopal, R Shekar, “FPGA-based 3D median filtering using word-parallel systolic arrays” IEEE International Symposium on Circuits and Systems, 2004, vol. 5, Issue 23-26 May, pp. 996-1023.
- MATLAB and XPPAUT code for proprioceptive sensory neuron with bursting dynamics (Deposited in ModelDB: http://modeldb.yale.edu/258235) MesVModel (PMID: 31226124).
- MATLAB and XPPAUT code for spinal cord injury motor neuron model (Deposited in ModelDB: http://modeldb.yale.edu/258234) SCIMN (PMID: 21775715; 23730358)
- MATLAB code for a motor pool model for isometric muscle force generation (PMID 25589764).
- MATLAB code for simulating the Hill Muscle Model.
- Astrocyte Ca2+ signal detection and analysis from real-time video recordings: GECIquant Image Analysis ImageJ Script and Manual (PMID: 25894291)
- Quantification of synaptic terminals on neurons in immunofluorescently labeled images: IMMUNOquant Image Analysis ImageJ Script.
More coming soon….