18 – Using Human Models of SynGAP1

Event Time

December 4, 2020 at 5:30 pm

Description

Here are our introductory comments:

Intro for Dr. Marcelo Pablo Coba (November 5, 2020)

We are very excited to continue the SRF webinar series. The goals of the series are:

  • getting you closer to the science 
  • making you aware of the research that is been done and the opportunities to participate
  • and empowering your communications with clinicians 

We also want to remind you of our next webinar in the series with Dr. Stephan Sanders on “SYNGAP1 in the Developing Human Cortex” tomorrow Friday, November 6th at 9 AM Pacific. Dr. Sanders has been invaluable in developing frameworks for identifying autism risk genes as well as the mechanisms by which mutations in these genes cause disruption to developmental pathways.

Today’s talk is “How & why to use human cellular models to study Syngap1” by Dr. Marcelo Pablo Coba. Dr. Coba, is an associate professor of psychiatry and behavioral science at the University of Southern California’s (USC) Keck School of Medicine. Dr. Coba’s impressive educational background includes a Doctor of Pharmacy and postdoctoral work in Neuroscience. His education sets him apart and aligns well with SynGAP patients’ needs.

Dr. Coba has a long-standing interest in the role of proteins like SYNGAP1 that are important in the synaptic function, synaptic plasticity, learning and memory. He wants to help us understand the molecular and cellular changes that occur in our Syngapians. Professor Coba proposed a plan to address this essential and complicated question. The proposed work includes ground-breaking new ways to describe the effects of known pathogenic SynGAP1 variants in neuronal cells, and is critical to finding molecular targets for potential medications.

Dr. Coba has a close working relationship with SRF, and has been generous sharing his ideas and listening to parents. For several years Dr. Coba has been working on SynGAP1 Encephalopathy. Now he is asking the Syngap Research Fund to support his work. He is optimistic this will eventually lead to better outcomes in translational research.  

We as a group feel privileged and lucky to have Dr. Coba working with us and we are thrilled to support his work.

Webinar Overview

Dr. Marcelo Coba is a psychiatric researcher at the USC School of Medicine. To begin the webinar, Dr. Coba gives background on research using human models as well as on the synapse and postsynaptic density (PSD). He talks about how SynGAP doesn’t work alone, but it works in the context of the surrounding molecules in PSD. He specifically talks about how the RASGAP region of SynGAP interacts with RAS and GAP regions of other proteins, creating a set of components that can be regulated in the synapse. Mutations in these “synaptic” proteins are associated with a number of neurodevelopmental disorders. Dr. Coba then talks about NGN2 neurons-spines morphology, the process of using immature neurons to develop spines so he can study PSD and SynGAP. Through his research, he has found that different mutations in the SynGAP gene can cause completely opposite phenotypes. He closes by talking about work to be done in the future in order to better characterize what each mutation is doing to the SynGAP protein.

Other Relevant Publications by Dr. Coba

The autism-associated gene SYNGAP1 regulates human cortical neurogenesis

Endogenous Syngap1 alpha splice forms promote cognitive function and seizure protection

THIS IS FOR TRANSCRIPT ONLY:

0:03so our next speaker is dr marcelo coba who is an associate professor of

0:09psychiatry and behavioral sciences at the university of southern california

0:14he studies how genetic variants affect synapse function but uses a systems biology approach so

0:20not just looking at individual genetic variants but thinking about signaling networks um and so he’s going to tell us about

0:27his work today

0:33there we go you can share great

1:02and just make sure you unmute

1:32we can hear you but we lost your slide

1:38yeah let’s go

1:59you can see a slide no yeah yeah yes okay yep we see your powerpoint

2:06i think if you start the slideshow we’ll be able to see it perfect okay thank you thank you for the

2:13invitation and uh i would like to i’m going to discuss

2:18what are we going in terms of using a human models for sync up disease why the reason that we’re using what are

2:26what is good about them and what are the problems about these things as with many on this panel

2:32we were used to work with sync up and other synaptic proteins within the context of of material

2:39rodents synapses but so we’re going to discuss a bit more of

2:45what is this molecular context of syncap within ipsc derived neurons

2:50whoa what is similar and different from rodent synapses and show some what are we what do we got

2:57with with different mutations with mutations in from different patients and seeing gap and what common things

3:03what we found what what are different but then one thing is that what i would need to to do is that will try to

3:09to give this molecular context and why we need to describe a bit

3:15what what do we really know uh in the synapses of these humans mouse

3:21models so the reason on this is that because of some years ago we moved forward with the

3:27lab and trying to get more translation translational approaches for this no source for sync of one but for other

3:33permutations in different components of the synapse and we we recently to do the two

3:39with two main ways one is the classical pharmacology approach and when we improving methods

3:45of stem cell and change editing and trying to recover and improve sync up one

3:52protein levels and gabin gave great examples of how how these things can be achieved but

3:57we’re going to mostly focus within the context of of human neurons so for any of these approaches you are

4:03going to have at least four difficult things to to do is just you would need to get a target in this case

4:08the target obviously is zynga but as i’ve been showing it can be any other other other component we need a compound or

4:17editing approach the delivery system and a model so what we’re going to describe today give you this it can be edited

4:24it’s it can be a top for any of these of these problems but it’s just

4:29well what are we going to do with the model what do we need to know what the model so when we taking this

4:34into account this our view was okay what do we need to know about what do we need to understand

4:41for the these human models and one of the good things about the human models is that well we would like to work with the human shin

4:47with all the regulatory elements and with the and with the genetic background of these particular patients i’m not

4:52going to describe anything on genetic backgrounds but we are heavily interested in the genetic backgrounds from different patients um

4:58the role that plays in different phenotypes the other good thing with human models

5:04is that we can work still with single cell types we can work with glutamatergic neurons certainly in arctic neurons we

5:10are doing the three or three of them also ether genius model of brain organisms what we are doing but i’m not going to show much data and

5:17brain organisms are going to describe only what we are getting with the glutamatergic type of neurons a big problem with this

5:24model is that everybody knows these models are immature neurons they are fetal like neurons and they are poly characterized

5:31so a main question for us was okay everything that we know from sync up all these molecular contexts that we

5:36know that what is it doing in a rodent mature synapse how many of these things can be reproduced

5:42or how what is similar or not similar in this type of neurons

5:47so the idea was not to make catalogs of what is in there but it’s just a problem for us

5:54is i really wanted to understand the model system that we are going to work with

6:00so unfortunately well i i need to give you a little general background and maybe give a

6:05really great talk on some of the molecules that i’m going to describe but i need to show you what what is mostly

6:11what we know in terms of assistant approach from this role and synapses and what do we have in these human

6:17models so you know that this is a postsynaptic site presynaptic postsynaptic density

6:23many of you already know this you have this electrodes region here that is the postsynaptic density

6:28and in this reaction here if you guys you can isolate it and analyze the mass spectrometry what

6:33proteins do you have there you have more than two thousand proteins there’s uh which we define a core component core

6:41more abundant molecules about 200 and you can see there it’s a tense code of protein protein

6:47interactions what we call protein interaction networks when you try to analyze which protein is interacting with which other

6:54you define it as a protein interaction network you have enough data to describe which molecule is associated to

7:00each other so you have different what we described this is a cartoonish kind of thing that is different

7:05and

7:11different layers of scaffold molecules this is the first layer of magox

7:17molecule that you have dlc one two four there gaps that binds to the magog stick up to one to four as well and the shanks

7:23at the bottom layer with shin shang one two three and each one of the others are magogs interacts

7:28with tear gups they got with mangoes and chunks and all of them gets a dense core of protein protein

7:34interactions there and sync up is one of these

7:41molecules this is i think one of these molecules that will have interaction with first with the

7:47different components of of this protein interaction network and i probably don’t need to to show

7:54this as you already know but you have different models that describe that

8:00describe functions like a rascal domain different models that is involving protein interaction that ph

8:05domain bd7 binding but what we described is when we’re

8:10going to describe in terms of protein domains is that these domains and uh of protein modules it can tell you

8:16that what is the function of the protein and we usually would describe the the function of the proteins but the

8:21protein uh domain composition mainly because this protein doesn’t work

8:27alone and these functions are within the context of the surrounding molecules that we are describing the molecular

8:32composition and i’m going to show which what is in this interval and synapses and in the in the human synapses

8:40and more importantly the protein composition change in in a developmental manner obviously

8:45in a cell type manner so when you if you are we are describing that these

8:51synapses from the human mass models are not mature we can think about that the

8:56protein composition is not the same if the protein composition is not the same sync up functions might

9:01not be identical so what we need to do to have we wanted to have a pretty good idea

9:06what sort of molecules are there what sort of molecules thing that can bind to in this in those other sentences

9:14so this is a more detailed view of the kind of protein domain that you have each it doesn’t matter much which one is each

9:20one but you can see all these uh shapes and colors are showing different domains that could find

9:26that combined different proteins and sync up has a collection of some of these uh protein domains as well

9:35mostly what we uh establishing up is by three main only three of these uh

9:42protein domains one is the ras gap domain the pity set ligand which finds the what is that domains and set in turning

9:48kinase domains that are not probably from singapore but we we we have a uh

9:56abundance of data at least some information on what kind of kinases also relate this

10:01thing up and have an effect of of single function

10:06so i’m going to describe mostly some of the molecular composition on

10:12these two the main thrust gap and selenium kinase within the context of the pst so syncap is not the only rust gap

10:20uh we have around 30 rust gaps except 30 gaps at the

10:28at the pst and this is more or less there will be around seven or eight

10:34other rasps specifically rascal at the psd you have the different components of

10:40the gaps family row gaps rise gaps wrap gap that usually they have

10:47as the same as singapore the rust gaps and wrap gap have uh cross reactivity art gap rap gap and the

10:53same from the gaps domains what their molecules is you know are doing is are they

10:59they are helping the shitty base activities so these gaps are helping to hydrolyze gtb to gdp and

11:06inactive uh inactivating traversal pathway and shitty and the gaps doing the opposite she’s

11:11putting back the gtp and getting their activation so mostly what we know about sync up is we have

11:17some idea how is regulating or at least indication that regulates uh

11:23arc and some p-38 pathways but not very clear idea when i say not clear role of

11:28upright relations because erc and p30 have been studying for asia’s at the sinners within the content of

11:34synaptic plasticity and different uh mechanisms of ltp

11:41and it’s not really clear what’s the role of this increase in or up regulation in

11:48eric when you have uh mutations in sync up you have pl you have plenty of rascal activity at

11:55the synapse there are many other rust gaps and it to me is still not clear why even if in the worst

12:02case scenario do you lose 50 percent of sync up activity what is it doing with this if you want

12:09increasing air connectivity you have tons of other factors at the psd you have this this is the

12:17gdp families you can see the ras family includes the rap family because there’s a lot of crosstalk there

12:23they are glued together many of these are present on the synapse there are more than 150 in total in the human

12:29proteome so what it means this is that this increases in

12:34these large numbers of gaps and rascals and gaps

12:39it may indicate you that there might be some other activities that can be

12:45regulated by changes in sync up activity we still don’t know and why we suspect

12:52also these things are because well for many years we were studying protein

12:58interactions at the at the synapse one of these proteins that we are interested obviously synga

13:03but we are doing protein interactions for many components of the psd

13:09um basically what we use for er what we use is as immunoprecipitation gap with a

13:15specific antibody and analyze what are the interactions synthetic syncope can interact with tons

13:21of different molecules so not going to describe many but just focusing one these large groups are

13:28gtb signaling molecules and this other large group here are

13:34protein scaffolds obviously you have a large group of kinases and glutamate receptors

13:40so synga its associate is not just that you have many of these rust gaps

13:46and gaps at the pst singap can associate to many of them so if you this is a bit complicated uh

13:55graph but it’s just to have this convey this idea sync up interact with other reds and gaps and other rust gaps and rust gas

14:03also interact with others is this is a common property of all these molecules so this is just an example of three of

14:08them this is agap2 which is an arc cap and this is carrying which is another abandoned product is a rogue

14:14abundance of the psd you can do immuno precipitations and

14:20protein interaction for each one of them and we have for many other rust gaps as well and this is just an example of these

14:26three and then try to see to to describe protein interaction network for all of them you can see

14:33in this graph with the color code that is showing you different gaps or different gaps that

14:39they are components within the same protein interaction network this is a common property that and even when we found it in other cell types and with

14:45other other molecules that grabs and gets they’re clustered together

14:50and these gaps and gets are not just rust gaps or wrap gaps there are road gaps road gaps

14:56so you have a set of components all together that can be regulating the different activities within the cell and

15:03you you have we have many of these other synonyms most specifically the psd so obviously they are associated with

15:09scaffold receptors with the scaffolds and glutamate receptors and they are probably phosphorylate they are

15:16associated with the number of different protein kinases at the psd and most probably what they are phosphorylated but many of them are

15:24regulated protein activity what we know about this is that obviously is from protein kinase we have tons of them

15:29have a phd we can phosphorylate any possible sequence because you have a sequence of specificity for any any

15:36known uh linear motif at the of the pst you have more than 80

15:42protein kinases at the phd these are at the expectation of the mouse

15:50kinem you have more than 500 kinases and each circle is kinases that are present on the pst

15:55the color codes is that information that well it indicates what happened with that

16:01kindness after the induction of ftp or if or we have data from different patterns of synaptic activity as well if this

16:08kindness can be upregulated or translated so we have a pretty good idea what is happening after synaptic activity with many of

16:14these kinases and you can see for example for syngap even if we have some data of what some phosphorylation sites are

16:21doing and most of the k2 sites it has close to one now it’s even more

16:26it’s a hundred different phosphorylation sites most of them have been found in brain so there is room for regulation and

16:34for many different kinases no not all of them are concave two sides or or probably interact the kinases that we

16:39have a large variety of them so this is just for to have an idea of molecular context so

16:45what will we know about regulation how much room for improvement on finding targets

16:53are there in terms of regulation of camp of sync up activity or what the rust gap domain is doing in terms of the

17:00kinases just for this figure that we published some years ago it’s just to have an idea that all of

17:06these kinases and all of these substrates they have specificities in terms of function so

17:11for example you have different families of protein kinases and they that they phosphorylate different families

17:16of or of proteins with this particular function of the psd

17:23basically if you have the cmcc group of kinetic which are mostly proline directed kinases

17:29they’re phosphorylated mostly cytoskeleton and cell addition proteins and campaigns families and hac kinase which

17:35is protein kinase a protein games b protein knee c phosphorylates mole uh whereas scaffold proteins

17:44kind of other kinases and also rust gap and gap function so this is just for the

17:49end of the talk show to have an idea what’s happening here in the materials roman synapse and what we are seeing in the

17:55in the human synapses another thing to show here is that we

18:01have a good idea as well what is happening with this component with the core components of

18:06the protein interaction network of the phd they are highly phosphorylated when you usually in

18:12in all the assets that we have uh performing phospho uh proteomics of the synapse and also

18:18other systems usually you have when you analyze different functions you get like if

18:24it’s even a distributed 50 of the time you have increases in phosphorylation another 50

18:30percent of the molecule you see decreases in phosphorylation this is the same when you introduce ltp

18:35or with different patterns of synaptic activity but the chorus scaffold machinery the psd it has a high high

18:42up regulation in phosphorylation usually you have increases in phosphorylation and not so much decreases

18:47this is in general terms and this is again to to to compare what we’ve seen in the in the

18:53human neurons so when the concept of singap so we are we

19:00think that you have a list of molecules now that with all the advances in human genetics now they are these are being described even as

19:09polish monogenic disorders we we know that mutations in ldl receptors different dlcs still gaps and

19:15shanks are causing a variety of diseases with similar phenotypes really these are

19:21pretty you have they have a lot of points in common with sync ups and so thing that we always we consider

19:26is belong to is a large group of synaptic proteins that are in you can take any of these one one

19:33of these and there could be people meeting in this time together describing kids with mutations in each one of them

19:40and having this sort of phenotype and trying to address uh these disorders so sync up is one

19:45other of them this is not uh surprising because

19:50in our view of how we approach these signaling mechanisms we understand that well it’s proteins

19:57that are interacting protein interactives proteins that have physical interactions are more likely to

20:04be involved in the same kind of disorders the same kind of phenotypes so this is not unlikely what is this one

20:11when i was doing this slide is it’s just punched me is how inefficient have we had been or

20:18during all this year with all the knowledge that have we been accumulating studying all of these molecules

20:24if you ask what do we have to to to translate our knowledge into any

20:30therapeutics for each any one of these mutations we don’t have a clue we have been

20:36extremely inefficient and we we really it’s not just for synapses for

20:42any of these molecules we can have mutations in any of these molecules and at the moment we are not doing anything

20:47we cannot do we cannot say do this this and that so in terms of that is

20:53one of the things is that many of these things are undruggable when we come with new target but probably is pointing out more to that we

20:59don’t really understand the system after after all these years so to complicate things more

21:08all what i show is what we know you’re not to denote these years of work in material rolling

21:14synopsis these things changes a lot during the development we characterize these protein interactions through

21:20development and it’s very different what you get in a fetal or in an in e14 not fetal bodies

21:28in the cortexes of e14 mice through b7 b14 and adult so protein interactions

21:35are very different and when i was pointing out this probably going to skip this

21:42to make them in time it’s like because we know that this protocols to induce induced neurons

21:49generates immature neurons so if we have see what the the reasoning was well if

21:54these interactions were different in mouse cortexes through e14 to adult

22:01what are we going to find to get really in this type of neurons so these are just pictures how they are

22:07maturing through certain chinese molecules these

22:13neurons and i have a picture here three weeks old that you can see lots of philopody like protrusions and after

22:22eight weeks old you can start have better synapses but everybody that worked with

22:27mars uh models know that these are not very very few and you can you can obviously

22:32you have some some dandruff that you have nice uh many more spines but

22:37at the end of the day yes you don’t have as many spines and as you could find the mouse model and these are really not

22:44much uh mature so the first thing that i uh to me is just pointing out okay we are

22:50going to have these neurons these neurons have psds so we were doing uh looking at uh ancient two neurons

22:59derived neurons at eight weeks old uh these are some pictures that at least

23:05for uh for us it was nice to find psd you have presynaptic here with

23:10with synaptic physical synaptic side with this the psd there and you can see it

23:16better here psd and these neurons and we were doing massive spectrometry analysis

23:22of this but you’re showing some pictures that we have the molecules that we used to play with uh in the mature neurons you

23:28can see this is solar fraction and psd fractions and presynaptic components postsynaptic

23:34components they were as they should be you can see sync up expression with different isoforms

23:41and you have the same pattern in a in a fetal this is the second end of second

23:47trimester uh fetal brain but you have similar patterns so on in in the fetal brain and

23:54i am not showing quantitation there but and now this is a complicated slab we are we we

24:00did analysis of the different time points on during development and comparing different

24:06synapses and what clearly is showing that the protein composition of these ancient two neurons is more

24:13fetal like obviously you have the classical pattern that you have in the fetal brain the different number of molecules that

24:19are highly expressed and then being decreased through development and they say the opposite pattern in the

24:26with at all in adult synapses and these changes are these differences

24:33what mostly we’re not going to go through through a lot of this slide because i want to show some

24:39findings of our on different patients but we have seen a lot of

24:45components involved in aeronautics placing in the in this this synapses in early

24:51in the ancient two derived neurons about five minutes finally okay so i try to go what so we

24:59have different we we there are different cell lines from different uh ipsc neurons from

25:04different um patients i wanted to show just a couple of them

25:09this patient here and the with mutation truncation mutation here and another project with mutation

25:14to engage mutation there so we have some uh as well this is what we expected or

25:21we were suspecting to have with one of these the first model that isn’t hapless efficient model of sync

25:28gap you have this is the patient and this you have to correct the control this is important for each one you you need to do the different uh

25:36isogenic cell line with different corrections if you these things are really really uh

25:44the responses are not tricky you will need to to to do the different corrected cell

25:50lines so we have increases in synaptic activity uh the spike frequency the minus frequency

25:56is higher uh the burst per minute is also higher but this is not what you always get we have this with this mutation we have

26:02the opposite result uh i probably am not going to have time on

26:07what is on the specifics on this but what i mean is that you have different mutations and you can have

26:13even opposite phenotypes and different things with the spines morphology the same with the mutation in the

26:20first patient you we can see that it looks like what we define in mature

26:25spine we can discuss later what is this means in mature spines i’m not sure that we are seeing an immature spine but this

26:31is what we find and it correlates with what is supposed to be an immature spine but this is a

26:36protocol that you are inducing the the the glutamatergic neuron is not

26:42just that their shots developing so we have an increasing spike density uh more is explained late and spy volume

26:50when you look at the different mutation it’s almost not changed and even in the spine lengths is the opposite

26:56we did massive spectrometry for every for this were some really nice experiment we isolated

27:02psd for each one of the patient mutations and for each one of the corrected cell lines so you have tons of replicas where

27:09you have at least four or five replicas for each one of them and different corrected cell lines and we obviously i won’t have time to

27:16describe much what are the specifics of this again in protein phosphorylation we’ve

27:22seen the with the protein quantitation and protein phosphorylation i’m not going to show product quantitation so we’re protein phosphorylation it’s all

27:29again the phenotype shows they’re correlated with with the differences of different phenotypes from different protein

27:35phosphorylation that we’ve seen we have many more phosphorylation sites in one in increasing protein kinase

27:41activity in one of the patients not in the other different types of protein domain

27:47phosphorylating one case and the other we mapped to watch kinases where uh phosphorylating those sites and for

27:55what kinases is to make a story short and it’s important and just to correlate

28:00what we’ve seen in the to not correlate really to what we’ve seen in the

28:06mature rodent synapses we have an increased activity in protein kinases that phosphorylate

28:11cytoskeleton protein which is the cmc cmgc group of kinases probably directed kinases

28:18and this is probably correlated with the increase in cytoskeletal protein

28:24activity if you want proteins cytoskeleton remodeling that are these

28:29immature synapses showing like increases showing uh increasing feel

28:36you have more like philo podia like um spines that are actually been remodeling

28:44we found many more than 40 sites that showed the same regulation in all the controls they will have

28:50number of replicas in each corrected subline that we have exactly the same sign going in the same direction which

28:55are interesting point to follow up some of them going the opposite reduction until it’s nice sites to look for

29:01sites that we study in the context of materials synapses are white umpire receptor auxiliary units calcium

29:07potassium channels solar gaps protein kinases so

29:12this is an interesting thing to show separately i won’t have time to show all the data but we mapped with the

29:18protein interaction data in the in these in these models as well which which sites were upregulated and

29:24regulated with each mutation within the sync up protein interactors and

29:30i’m not going to show anything related to to brain organisms but we we got

29:37sync up expression in brain organoids also the interactors there and also we derive inhibitory neurons

29:43from uh from these patients as well this is sync up expression

29:49immunoprecipitation from these are specifically somatostatin neurons so what we are doing doing this combination of inhibitory

29:55and excitatory neurons the addition showing one mutant what what what both mutant groups what weltec and

30:00also we have others other cells there so probably is not

30:06much uh time but we will really need to

30:11it’s just to convey the idea i want you that the mutations even it’s some of them and

30:19i agree with what have been said before we have this disruption of thing up uh activity what

30:25we do what do we know is to what extent uh i’m not sure that all the happiness

30:30efficient models is like 50 decrease on sync up

30:36levels we obviously we are going to have many more phenotypes and also

30:43patients phenotype to what the mutations are really doing so if we are considered that most of

30:49them are just a lot some function and highly sufficient model we are going to find lots of different phenotypes

30:55at least in our models that are not so well correlated so a way i feel that we need to have a

31:00better way to characterize really properly what each mutation is doing to the single protein

31:08and we’re working on that and then most of these catalogue of things that we were seeing

31:13is just to be clear what do we need to look for one

31:18obviously when we are trying to to improve the levels of syngap is just to look for synga so in that regard we need a

31:25uh the model for to determine properly how much syngap do we have and also if

31:31we are looking for phenotype which ones are we looking at in which model and that’s more related also to if we

31:40that there’s room to find to to find other targets as well so those are

31:47collaborate those fundings and but mostly i would like to have sierra the singapore research fund and the singapore family because the work that

31:53they’re doing i i always say it’s great and in my case i was working with sig

32:00with other molecules at the psd all the time and this just helped me to reconnect with synga

32:07from my early years of postdoc really thank you great

32:12thank you so much marcel that was fantastic and a whirlwind tour um i there are there’s some discussion

32:19in the chat and i would encourage you to look at some of that and and continue

32:26some of that discussion in the chat box so that we can move on to our next

32:32speakers i think we’re going to have a

32:39tag

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