John Lisman (1944-2017)

Chair of Biology Piali Sengupta wrote:

It is with great sadness that I am writing to let you know that John Lisman passed away last night. He passed away peacefully surrounded by his family. John was an influential and creative scientist and a very good friend to all of us in Biology and Neuroscience. We are glad that we had the opportunity to honor him and hear from him at the Volen Retreat last week. He will be much missed.

John’s talk at the Volen Retreat earlier this month, delivered by video conference, is available here: The critical role of CaMKII in memory storage: 6 key physiological and behavioral tests

The family has asked that in lieu of flowers people consider contributing to the John Lisman Memorial Scholarship

Update: The Memorial Service, taking place at 2 p.m. Thu Oct 26, will be live streamed. Brandeis community members can watch the live streaming in real time in Gerstenzang 121 as well as the Shapiro Science Center level 1 library. There will be a reception at the Brandeis Faculty Club at 3:30 open to the community.

We also wanted to share some tweets from past students and colleagues:

We also received this longer tribute from Michael Kahana:

I was greatly saddened to hear the news that John Lisman passed away this weekend. I spoke with him just a few weeks ago and was greatly looking forward to his upcoming visit to Penn. Although he told me of his illness, I was hoping to have a little more time with my good colleague and friend. Upon learning of his passing, I wanted to write down a few memories to share with friends and colleagues who knew John well.

I vividly recall when I first met John, at an evening gathering at his home that I attended just prior to joining the faculty at Brandeis (this may have been a precursor to the famous Boston Hippocampus meetings that John helped organize). The meeting was teaming with energy, and John welcomed me warmly, introducing me to other scientists in the room. John had recently become very interested in human memory, and as a newly minted PhD working on memory, John took me under his wings, teaching me about neurophysiology and quizzing me enthusiastically about the psychology of memory, a field that John was keen to master as quickly as possible.

John was a polymath, bursting with creative energy, and capable of seeing connections between diverse fields. Over the subsequent decade at Brandeis, John had an enormous influence on my career and research direction, introducing me to theta and gamma rhythms, and teaching me about countless topics in neurophysiology. On a typical day in the Volen Center, John would rush into my lab eager to share a new discovery or ask me a question about a study of memory that he had just learned about. He had this incredibly-infectious scientific curiosity, and he was always abundantly generous with his time, both with me and my students.

I particularly remember the early days when John was developing the LIJ (Lisman-Idiart-Jensen) model, and trying to learn as much as he could about the Sternberg task and other related phenomena in the field of human memory. Although I frequently challenged John on this front, he kept at it, continuing to refine the model together with Ole Jensen until they were able to answer many of the most serious objections. I just saw that the original paper was cited more than 1,200 times, and several of the follow up papers are well into the many hundreds of citations. This is arguably the most creative neurophysiological model of a cognitive function, and the best example of an attempt to link detailed physiological measurements to behavioral measures of human memory.

We have all lost a great friend, colleague, and mentor, and the field of neuroscience has lost one of its shining stars. I want to share my deepest sympathies with all of you who knew and loved John.

May his family be comforted among the mourners of Zion and Jerusalem.
Mike Kahana

Thomas Reese shared his thoughts:

John, your intellect and spirit lighted more than 30 summers my life at the MBL in Woods Hole.  You were a reference point for neurobiology there, holding court at your favorite table at the Kidd, at the far end of the dock.  A cherished invitation to lunch at exactly 12:00, with interesting synapse people passing though, or to hear a deluge of you new ideas about how a synapse is, or should be, put together.  Occasionally an invitation to dinner outside, behind your house with talk of many things…..joined by the delightful Natashia and other interesting people….discussing well into the night.

If Woods Hole is a little scientific Athens, you were our Socrates, lurking on Milfield. questioning in your disarming, open open way…bringing out the truth.  You were our Dogenes. searching Gardner Road for a man with the honest truth.

John, ,… will seem empty there without you…you
will be very much missed..Tom Reese.     NIH

CaMKII: some basics to remember

The theme of Thursday’s Volen Center for Complex Systems annual retreat will be Breakthroughs in understanding the role of CaMKII in synaptic function and memory and honors the pioneering work of John Lisman. To help bring non-experts up to speed, we asked Neuroscience Ph.D. students Stephen D. Alkins and Johanna G. Flyer-Adams from the Griffith lab at Brandeis for a quick primer on CaMKII.

What’s a protein kinase? 

Protein kinases are enzymes that act by adding phosphate groups to other proteins – a process called phosphorylation. Phosphorylation of a protein usually initiates a cascade of downstream effects such as changes in the protein’s 3D shape,  changes in its interactions with other proteins, changes in its activity and changes in its localization. In causing these types of changes, kinases facilitate some of the most essential cellular and molecular processes required for survival and proper functionality.

Aren’t there lots of protein kinases? What makes CaMKII special? 

Among the roughly 500+ genes in the human genome encoding protein kinases, a kinase known as calcium (Ca2+)/calmodulin-dependent protein kinase II (CaMKII) phosphorylates serine or threonine residues in a broad array of target proteins.  Though found in many different tissues (skeletal muscle, cardiac muscle, spleen, etc.), there is a lot of CaMKII in the brain– about 1% of total forebrain protein and 2% of total hippocampal protein (in rats). Previous research, including pivotal contributions from the Lisman Lab at Brandeis University working in mammalian brain, has identified CaMKII as a cellular and molecular correlate of learning and memory through its multiple roles governing normal neuronal structure, synaptic strength, plasticity, and homeostasis. The Griffith Lab has been instrumental in demonstrating that these roles of the kinase are conserved in invertebrates.

Why do we think CaMKII might play a role in memory?

a) Location!

As previously mentioned, CaMKII accounts for up to 2% of all proteins in memory-important brain regions like the hippocampus. It’s also highly abundant at neuronal synapses, where neurons communicate with each other.

b) Function!

Memory is thought to require a process called long term potentiation (LTP) where two neurons, in response to environmental changes, will change the strength of the synaptic connections by which they communicate with each other—these changes will last even after the environmental input has disappeared. We know that CaMKII is required for LTP. We also know that the increases in neuronal calcium levels that accompany neuronal activation and cause LTP also allow CaMKII to phosphorylate itself. This autophosphorylation of CaMKII changes its kinase activity so that CaMKII can stay active well past the window of neuronal activation, essentially ‘storing’ the memory of previous neuronal activity—much like LTP!

c) Structure!

Ultimately, the issue with ‘molecular memory’ is that all proteins degrade over time, causing one to ask how we can remember things for so long when the original proteins that stored that memory no longer exist. CaMKII is such an exciting candidate for molecular memory because it is mostly found as a dodecameric holoenzyme—this means that CaMKII likes to exist as a big assembly of twelve identical CaMKII subunits. However, each CaMKII subunit retains its kinase activity even when all twelve are assembled. What’s interesting is that the autophosphorylation and activation of one CaMKII subunit (which happens when neurons are activated and intracellular calcium levels rise) actually makes it easier for the other CaMKII subunits in the twelve-unit holoenzyme to become autophosphorylated and activated. This means that maybe when an activated subunit is old and get degraded, another new CaMKII subunit could take its place among the twelve-unit holoenzyme—and become activated just like the old subunit, allowing for the ‘molecular memory’ to last beyond when proteins degrade!

CaMKII phosphorylation and activationCaMKII in more detail…

Calcium binds to the small protein calmodulin and forms (Ca2+/CaM), which acts as a ‘second messenger’ that increases in concentration when neurons are activated. CaMKII relies on calcium/calmodulin (Ca2+/CaM) binding to activate an individual domain containing a regulatory segment.  In conditions of low calcium, elements within the CaMKII regulatory segment will have less affinity for (Ca2+/CaM) binding, keeping CaMKII in an autoinhibited state.  In conditions of high calcium, (Ca2+/CaM) binding initiates phosphorylation at three threonine residue sites, including Thr286 which prevents rebinding of the regulatory segment, thus keeping CaMKII constitutively active even when calcium levels fall.  In this activated state CaMKII can autophosphorylate inactivated intra-kinase domains, and will undergo subunit exchange with neighboring inactivated CaMKII holoenzymes. Furthermore, mutation of CaMKII residues or binding sites in target proteins, such as postsynaptic glutamate (AMPA) receptors, disrupts establishment of long-term potentiation (LTP) in neurons.  Together, CaMKII’s role as molecular switch that bidirectionally, and autonomously regulates activity in neurons has earned it the illustrious title of a “memory molecule.”

What amino-acid manipulations might I hear about?

a) T286A:

Changing a threonine in a phosphorylation site to an alanine prevents phosphorylation at that site. Blocking Thr286 phosphorylation with a T286A mutation prevents CaMKII generation of autonomous activity that disrupts neuronal activity and results in learning deficits.

b) T286D:

Changing a threonine to an aspartate puts a negative charge at the site, often making it act like it’s always phosphorylated. In the case of CaMKII, a T286D mutation renders the kinase constitutively active, which can interrupt normal LTP induction and normal memory storage and acquisition.

To learn more:

8th Annual Pepose Award Lecture moved to Monday, March 13

Professor Frank Werblin, Professor Emeritus of Neuroscience at the University of California, Berkeley will receive the eighth annual Jay Pepose ’75 Award in Vision Sciences from Brandeis University on Monday, March 13 (date change due to impending snowstorm). The event will be held at 4 PM (room to be announced). At that time, Werblin will deliver a public lecture titled, “The Evolution of Retinal Science over the Last 50 Years.”

During his research, Professor Werblin identified a number of cellular correlates underlying visual information processing in the retina. He has authored many articles in peer-reviewed journals, and has contributed articles on retinal circuitry to the Handbook of Brain Microcircuits (Oxford University Press) and retinal processing in the Encyclopedia of the Eye (Elsevier). Werblin founded Visionize in 2013, a company dedicated to helping patients suffering from vision diseases that cannot be corrected with glasses or surgery.

The Pepose Award is funded by a $1 million endowment established in 2009 through a gift from Jay Pepose ’75, MA’75, P’08, P’17, and Susan K. Feigenbaum ’74, P’08, P’17, his wife. Pepose is the founder and medical director of the Pepose Vision Institute in St. Louis and a professor of clinical ophthalmology at Washington University. He founded and serves as board president of the Lifelong Vision Foundation, whose mission is to preserve lifelong vision for people in the St. Louis community, nationally and internationally through research, community programs and education programs. While a student at Brandeis, he worked closely with John Lisman, the Zalman Abraham Kekst Chair in Neuroscience and professor of biology at Brandeis.

Odor Recognition & Brute-Force Conversions

Frontiers in Computational Neuroscience will be publishing an interesting paper written by Honi Sanders and John Lisman (with co-authors Brian E. Kolterman, Roman Shusterman, Dmitry Rinberg, Alexei Koulakov) titled, “A network that performs brute-force conversion of a temporal sequence to a spatial pattern: relevance to odor recognition“. Honi Sanders has written a preview of this paper.

by Honi Sanders

Lisman_ProvisionalPDF_BLThere are many occasions in which the brain needs to process information that is provided in a sequence. These sequences may be externally generated or internally generated. For example, in the case of understanding speech, where words that come later may affect the meaning of words that come earlier, the brain must somehow store the sentence it is receiving long enough to process the sentence as a whole. On the other hand, sequences of information also are passed from one brain area to another.  In these cases too the brain must store the sequence it is receiving long enough to process the message as a whole.

One such sequence is generated by the olfactory bulb, which is the second stage of processing of the sense of smell.  While individual cells in the olfactory bulb will fire bursts in response to many odors, the order in which they fire is specific to an individual odor. How such a sequence can be recognized as a specific odor remains unclear.  In Sanders et al, we present experimental evidence that the sequence is discrete and therefore contains a relatively small number of sequential elements; each element is represented in a given cycle of the gamma frequency oscillations that occur during a sniff. This raises the possibility of a “brute force” solution for converting the sequence into a spatial pattern of the sort that could be recognized by standard “attractor” neural networks.  We present computer simulations of model networks that have modules; each model can produce a persistent snapshot of what occurs during a given gamma cycle. In this way, the unique properties of the sequence can be determined at the end of sniff by the spatial pattern of cell firing in all modules.

The authors thank Brandeis University High Performance Computing Cluster for cluster time. This work was supported by the NSF Collaborative Research in Computational Neuroscience, NSF IGERT, and the Howard Hughes Medical Institute.

More science

We’ve all been busy this spring writing grants and teaching courses and doing research and graduating(!), so lots of publications snuck by that we didn’t comment on. Here’s a few I think that might be interesting to our readers.

  • From Chris Miller‘s lab, bacterial antiporters do act as “virtual proton efflux pumps”:
  • nsrv2Are ninja stars responsible for controlling actin disassembly? Seems like the Goode lab might think so.
    • Chaudhry F, Breitsprecher D, Little K, Sharov G, Sokolova O, Goode BL. Srv2/cyclase-associated protein forms hexameric shurikens that directly catalyze actin filament severing by cofilin. Mol Biol Cell. 2013;24(1):31-41.
  • What do you get from statistical mechanics of self-propelled particles? The Hagan and Baskaran groups team up to find out.
  • From John Lisman and Ole Jensen (PhD ’98), thoughts about what the theta and gamma rhythms in the brain encode
  • From Mike Marr‘s lab, studeies using genome-wide nascent sequencing to understand how transcrption bursting is controlled in eukaryotic cells
  • From the Lau and Sengupta labs, RNAi pathways contribute to long term plasticity in worms that have gone through the Dauer stage
    • Hall SE, Chirn GW, Lau NC, Sengupta P. RNAi pathways contribute to developmental history-dependent phenotypic plasticity in C. elegans. RNA. 2013;19(3):306-19.
  • Can nanofibers selectively disrupt cancer cell types? Early results from Bing Xu‘s group.
    • Kuang Y, Xu B. Disruption of the Dynamics of Microtubules and Selective Inhibition of Glioblastoma Cells by Nanofibers of Small Hydrophobic Molecules. Angew Chem Int Ed Engl. 2013.

A biologically plausible transform for visual recognition

People can recognize objects despite changes in their visual appearance that stem from changes in viewpoint. Looking at a television set, we can follow the action displayed on it even if we don’t look straight at it, if we sit closer than usual, or if we are lying sideways on a couch. The object identity is thus invariant to simple transformations of its visual appearance in the 2-D plane such as translation, scaling and rotation. There is experimental evidence for such invariant representations in the brain, and many competing theories of varying biological plausibility that try to explain how those representations arise. A recent paper detailing a biologcally plausible algorithmic model of this phenomenon is the result of a collaboration between Brandeis Neuroscience graduate student Pavel Sountsov, postdoctoral fellow David Santucci and Professor of Biology John Lisman.

Many theories of invariant recognition rely on the computation of spatial frequency of visual stimuli using the Fourier transform. This, however, is problematic from a biological realism standpoint, as the Fourier transform requires the global analysis of the entire visual field. The novelty of the model proposed in the paper is the use of a local filter to compute spatial frequency. This filter consists of a detector of pairs of parallel edges. It can be implemented in the brain by multiplicatively combining the activities of pairs of edge detectors that detect edges of similar orientations, but in different locations in the visual field. By varying the separation of the receptive fields of those detectors (thus varying the separation of the detected edges), different spatial frequencies can be detected. The model shows how this type of detector can be used to build up invariant representations of visual stimuli. It also makes predictions about how the activity of neurons in higher visual areas should depend on the spatial frequency content of visual stimuli.

Sountsov P, Santucci DM, Lisman JE. A Biologically Plausible Transform for Visual Recognition that is Invariant to Translation, Scale, and Rotation. Frontiers in computational neuroscience. 2011;5:53.

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