- I am Dr. Nelson Scottgale,
a member of the Darwin Festival Committee
of Salem State University.
Welcome to the third day
of the 42nd annual Darwin Festival
at Salem State University.
So, what I'd like to do now is
turn it over to Dr. Ronald Mac Taylor,
who can give us an introduction to Dr. Manyanga.
Thank you very much for being here
and we look forward to our talk today.
- Hello everyone.
My name is Dr. Ronald Mac Taylor.
I am the chair of the Department of Chemistry and Physics.
And today we get to hear from one of my colleagues,
Dr. Fidelis Manyanga.
Dr. Manyanga came by way of his undergraduate training
in Zimbabwe to Portland State University
where he earned his PhD.
And we were fortunate that
at the time that we were looking to add another biochemist
to our faculty,
Dr. Manyanga happened to be looking for a position
and we were delighted
when we saw his materials and met with him
and it was fabulous to bring him to Salem State.
I remember well, in his first year teaching here
the 2014/2015 academic year.
In December of 2014, Dr. Manyanga came and spoke to me
and said, "Where is the snow,"
"these New England winter are supposed to be legendary"
"but there's no snow, what's going on,"
"I thought there would be snow."
For those of you who don't remember in January, 2015
we had a blizzard followed by three other snowstorms
that all gave us over a foot of snow each.
By the end of this three and a half week period
we had 80 inches of snow.
Dr. Manyanga no longer questions New England winters.
Dr. Manyanga is gonna give us a delightful talk today.
We're looking forward to hearing
about his work with human serum albumin
and I like to welcome him to the Zoom stage.
Hello Fidelis, welcome.
- Can I unmute now and start?
All right.
I'm just fixing one or two things here
and now I get started.
Get my pointer.
Hello, can you hear me?
Hello!
- [Dr. Ronald Mac Taylor] You're good.
- You can hear me, right?
I start?
- [Dr. Ronald Mac Taylor] Please.
- All right.
So, my name is Fidelis Manyanga.
And as Ronald said already
I'm an Associate Professor of Biochemistry and Chemistry
in the Chemistry and Physics Department.
Today I'm going to talk to you about
one of the research that I do
on the "Human serum albumin and plasma."
And I'm gonna show you some of the interesting
or some thought-provoking things that we can do with it.
A few housekeeping here,
my focus here is going to be on blood plasma,
and HSA.
There's so many things that go around
but that's what my focus is going to be.
And also today, I'm going to focus mostly on one technique
called differential scanning calorimetry.
And we leave other techniques to another day
I will just mentioned them.
And because some of these information involve
are real patients,
some data will be redacted deliberately.
Okay.
Since I have the mic,
I'm just going to go ahead and tell you
what to expect today.
Talk a little bit about my career path.
And I will talk about plasma
and HSA.
And then I will try by all means
to give you thermodynamics 101 on
how physical chemistry links to all this.
And then I'll give you a brief
very brief account on differential scanning calorimetry
which I will call DSC from now on.
Then now just jump into the results and,
Let's do this.
So a little bit of my background of,
two majors a BS in chemistry and biochemistry.
And for some of you who know the British system
we all do this in three years.
The only catch is we don't do liberal arts stuff.
If you're go in a chemistry degree
all you have to take is math, chemistry and physics
so that's why it's doable in three years.
So I worked a little bit in an analytical lab
and then I applied for PhD in chemistry
at Portland State University.
I was able to do Biophysics
and I met an extraordinary professor who was my mentor.
After that, I worked for Portland Biosciences
which is a startup,
dealing with DNA and RNA.
And then it got acquired
and we moved on to Louisville Biosciences.
And after some movement right there
I decided to be a professor at Salem State University.
So, I work in three broad areas of research.
The first one is I
work with compounds called Isatins
and we have found out that we can find active ingredients
that we can now potentially put in medications to be sent.
I've had the privilege of meeting a remarkable people
to collaborate on this project
and Dr. Mustafa Yatin here
is at Salem State University is a colleague in that project.
And Jesse here now and PhD at Boston University.
He was one of the guys who started this project with me
when I just got in.
And I have to mention Isra Malik, she expanded this project
and hopefully we will be working on a publication
of some our work.
It needs a little bit of panel beating before we get there.
And I also investigate thermodynamics of DNA and RNA.
And this one,
I don't necessarily have that many tools at Salem State
so I do this one my own.
And I also investigate human serum albumin
a major fatty acid, binding protein and plasma.
And that was lucky,
(indistinct) last one here Angelina Calnan
to work with me on that project.
And we were able to present this work
at the American Chemical Society
and that was wonderful.
And I think it also helped them move on with their career
she works for Cell Signaling.
Okay.
So, let's dig in into what I'm going to talk about today
which is a human serum albumin
and plasma.
So, we start here,
you take your blood here, your blood sample.
Put it in a tube,
this has got a centrifuge it's in it.
If you do that,
you're going to get the following.
At the bottom, we're going to have
erythrocytes which are dense,
is a dense stuff.
And on the top,
you're gonna have, it's a plasma.
And this constitute over 55% of the whole blood.
And today,
we will be talking mostly about
what's on top here, our plasma.
We're gonaa break it down.
For example, this plasma,
if you look down here
over 90% by weight of water is water
and 60% is albumin.
Albumin here.
And we also have about 36% are all globulins.
We also have some fibrinogen
which is, fibrinogen.
Fibrinogen is responsible for clotting factors in blood.
So, if you notice down here,
if we remove the clotting factors from this plasma
we get what we call serum.
So, since we're dealing with albumin from humans here,
I'll be calling it, human serum albumin.
And the abbreviation is HSA moving forward.
Sometimes they start from the top right here.
So let's go and dig a little bit into that.
So,
I don't make this stuff.
We don't have the capability of going to patients
and getting this.
We have a company called the Sigma-Aldrich
they do all this stuff for us
we just buy it.
We just buy them and sell albumin
it has been screened of all the diseases
and takes a lot of work out for us.
Okay.
So, let's talk a little bit more about human serum albumin.
Let me say this,
it is a most abundant protein in plasma.
About 60% by mass of the total plasma.
It's responsible mostly to transport fatty acids,
fatty acid transporter.
And I can say, number two role is to regulate
the oncotic pressure in the body
because you can look at circulatory system.
Once blood is pumped from your heart,
there has to be something responsible
to distribute it evenly to all parts of your body.
And we generally call that public pressure.
This human serum albumin is also a powerful antioxidant
because it is one reduced sulfhydryl groups
that can get linked to a lot of thing
that remove dangerous reactive oxygen species in our body.
So, one of my major talks today is right here in red.
We're gonna see how we can study
these human serum albumin and how it links to blood.
Okay.
A little bit more science here.
The primary structure.
This is a nice stuff for the students
who are required to be in this class to get credits.
The primary structure of a protein.
This one is 585 amino acids.
The molecular weight is about 66.5 kilodaltons,
you'll see it in gel electrophoresis
As you can see on the right here,
they are three structurally conserved domains.
Like right here, domain one,
domain two,
domain three.
And what's on top is the amino acid sequences, right there.
And these domains are sub-divided into A and B.
This is just to let us know where things are binding.
And the secondary structure is mostly
some alpha-helix helical coils.
And a sudlow here,
on reference number six of the (indistinct)
did a lot of work,
and by now we know where certain drugs binds
and mostly it's site one and two,
the Ibuprofen, (indistinct), they all bind to site man
and then, also a few things to note about HSA right here.
We have about,
59 lysin residues
and about 35 sistin residues,
17 fibrigens.
This is going to be important as I proceed.
And the last thing to note about this is
I have, we have a lot of ionized residues,
which is responsible for its globular functions
because it's tertiary restructure for this
is a globular protein
that allows it to bind and transport
the diverse amount of molecules and metabolites in the body.
All right.
Why do we really care about this studies?
Okay, this is a fact,
abundant literature shows that over 90%
of new drug candidates fail.
Even this time for suppose they were looking
for this Covid vaccine.
They probably started with over 2000 candidates
until they come up with one.
It doesn't mean that just that one
we also have other candidates in the pipeline.
So, a lot of the failure rates, drug making is huge.
And just because plasma and HSA bind
both reversibly and covalently
to a broad spectrum of substances
it will affect its bioavailability.
How much the medication is gonna go
into systemic circulation.
I'm gonna use this word lightly here
ligands is any external thing that binds.
So a drug, can be a ligand
but not all ligands are drugs.
Another word we use here
is xenobiotic.
I don't want you to be scared of these words.
Xenobiotic is just an external thing that bind to something.
So, I can use these words interchangeably
sometimes a ligand,
if it's confuses you just think it as a drug.
Xenobiotics is a blanket term.
In the world of biochemistry,
we also use the word adduct or adducts it's the same stuff.
So, assessment of plasma and HSA binding
for new drug candidates,
is one of the goals for doing the stylists, okay?
So, that's what you see right there,
overarching goals to investigate and exploit the ligand
or xenobiotic binding properties of HSA.
And the next question obviously will be,
can we potentially use this technology
for (indistinct) markers
or other things that may maybe important.
A few housekeeping here in the interest of time.
I'm not going to move too much
into materials and methods here,
but we just wanna make sure
if you decide to work on this stuff
all materials and reagents must be molecular biology grade
or higher.
And we buy most of it from Sigma-Aldrich
and Thermo Fisher Scientific and then (mumbles).
Usually as the lyophilized powder.
Lyophilized it's a fancy term,
but just means freeze dried,
dried powder.
And then we re-constituted it in a buffer
we checked the total ionic strength of the buffer,
we checked the protein concentration
by using BCA Protein Assay Kit.
We did gel electrophoresis at every time to check
if our (indistinct) is still intact or not
And gel electrophoresis material
mostly from Sigma-Aldtrich.
And when I arrived at Salem State,
I also got a generous gift
for some gel electrophoresis material
from professors Tracy Ware.
And as you can notice here,
if I'm looking at a human serum albumin here
on the ladder here,
we always check the molecular weight right here
before we run it for whatever we doing.
So, I'm gonna talk a little bit
about differential scanning calorimeter.
So, this is just a machine with a computer connected to it.
And right here, let me see if I can drop my pointers here.
Right here is where we put a sample
the biological sample.
And that sample has to be measured with the buffer.
And that's picture view here, sample cell.
The reference cell is always a buffer
and we're gonna hit this
and we're going to check
the temperature difference between the buffer and the cell.
And we collect that information.
Because if we eat the sample
a biological sample and a buffer with nothing,
we're going to get some differences
in the way they absorb heat.
So, this is the model for the DSC.
So calorimetry generally measures
the difference in heat flow from the biological material
to the buffer.
And we notice these slight differences
and we take a computer program to figure out
if we'll get good information out of there.
Okay.
So, a computer program is connected right there
and we get our information.
In the interest of time
I need to jump in to data analysis.
'Cause I need to make sure I get enough time to do that.
So this is the raw data we get from the DSC.
As you notice here (sniffing) on the Y-axis here,
is the heat rate in microwatts
and down there on the X-axis is the temperature.
As you notice,
we scan, we just hit the sample, this buffer from zero,
to about 100 degrees and then we cool it.
This is heating, this is cooling.
Will heat it again and cool it
because there's some background noise
that comes with physics.
So, we ran about three to five buffer scans
just to make sure if we can see some reversibility or not.
And we're going to use this information later
to check out with sample is gonna be half.
So that's a buffer.
It's the raw data.
This is a typical HSA sample ran in the same way,
heating range that I already explained this as temperature.
You'll start noticing here is that heat here.
You can start seeing stuff getting like that.
It means there's some differential absorption
of heat right there, between the buffer and the sample.
So this is an example of how we expect seeing them
you don't have to see them all the time,
but you can notice some differences in some absorption here.
And as you notice, if I drop this line down here,
I can see some temperatures at which
at manifestation is happening.
And that's what I'm interested in.
And what we'll do is we'll take this biological material
we convey it to the buffer.
And then we take the molar heat capacity.
We check the molar masses and everything
and we convert everything to something that we call this
the excess heat capacity.
And from there we get away information.
This is an example of raw data.
This goes on like,
thousands and thousands data points going down here.
But I brought that up for,
temperature, power microwatt, time and my baseline
we converted it to molar heat capacity
by taking care of the volume, the molar mass
'cause each biological sample is different
and the machine will give you
that conversion for excess heat capacity,
just the differential heat capacity.
So, we're just adjusting for protein concentration,
cell volume, molecular weight, pressure,
partial specific volume, all that
and we get specific heat capacity.
And this is, basically where we get it.
This process we call it normalization.
So, we have already seen this buffer scan
with nothing happened straight line, back and forth.
This is nice happening right here.
This is a sample scan
(indistinct) exactly the same way
or something happened right there.
And through the process of normalization
you can see here, we're going to get
what we call the heat capacity, specific heat capacity
yeah, against temperature after we adjust everything.
And we'll see a very nice curve happening
and on that curve we'll get this thing called enthalpy.
I'm going to talk briefly about
this is about melting temperature.
And this is essentially just going down here
to about 63.5 right here,
and the area under this curve
give us some thermodynamic information here.
Well, I'll talk briefly about it really soon.
So that's it, that's the melting temperature.
We need that information so much.
And it's melted at a certain that area here.
And we call this normalized graphs.
And for the lack of a better term,
I'm gonna call them, thermograms,
meaning we're getting them from
heat induced melting transition.
So I will call them thermograms.
And these are the title of my talk,
"Thermodynamic signatures, Biochemical signatures,"
because if we can do it over and over again
and we get the same data
it means it's something that we can use
for diagnostic purposes.
And we're going to convert everything
into something called free energy mass.
So let me talk briefly about free energy.
Yeah, it's free energy here.
As you notice,
go here.
I'm going to give you a Crash Course
for about a minute on thermodynamics.
So, there's something called a Gibbs free energy.
Lambda and professor (indistinct)
at Yale University a long time ago.
It is a combination of something we call delta H,
which is called the enthalpy.
This is essentially the forces in a chemical bond.
There's more to it.
This is temperature dependent on it
and this is delta S is change in enthalpy,
which is a measure of disorder.
So, if you notice these two things,
these are forces putting together chemical bond
these are forces that don't wanna bring them apart.
So there's gonna be, some of forces have a competition here.
There're competing forces.
And, there are three scenarios.
Scenario number one, if the overall delta G is negative,
the reaction proceed forward.
That's what we need to know for this papers.
If the overall delta G is equal to zero,
it means the system is at equilibrium.
We can essentially calculate what we call
the equilibrium constant.
And this is we can also calculate the binding constant,
which is the numbers we need for medications.
And if the process is positive,
it means the proceeds is reversed.
We are not getting anything going backwards,
so, that's thermodynamics 101.
Because I'll be showing you a delta G all the time
and Let's see it right there.
And this is the information that we're going to get
from our DSC as technical as it is
I'm trying to break it down a little bit.
So heat capacity, I already told you,
it's the amount of heat needed to raise the temperature
of one gram of material by one degree.
Now, these are the curves I showed you before,
Y-axis specific or excess heat capacity,
and this is temperature.
And after normalization we'll get a curve like that.
In that curve, on top here
the peak height which you can see
the melting temperature right there.
And that melting temperature here is essentially
a ratio of the enthalpy forces divided by entropic forces.
We put the word cal to mean it's from calorimeter
just being fancy here.
The cal is colorimeter.
Tm is the middle temperature,
it gives us information about stability.
Delta H is essentially
this area under this curve,
area under this curve.
So, delta H over here,
is actually an integral of specifically heat capacity
and (mumbles) under this curve.
And delta S is also an integral for that.
Once we get these values, we have to calculate this.
You have to physically calculate, you need to calculate.
And the good part about this
is you can change to the temperature you want
for biological process you want, okay?
So, that's our stuff right there.
And I'm going to dig straight into the results.
I'm going to dig straight into the results
in the next 10 to 15 minutes as such.
So, I am going to show you the results
and there'll be as follows.
First, I'll show you how HSA and plasma curves appear.
Next, I'll show you how they get changed
when you bind them to some medications.
After that, I'll tell you how HSA binds to biotin.
And I'll show you how this technology can be used
to detect some diseases.
And with time and maybe a game changer.
Okay.
I've already shown this.
So, my topic is, "Biochemical signatures."
It's essentially graphical signatures
that get repeated over and over again.
Then we can use as biomarkers.
So what you see here is HSA signature, human serum albumin.
Human serum albumin, subsidiarity to DSC,
you normalize the curve.
You're going to get this small peak
with a melting temperature
right there around 63.5.
If it's no more HSA, you are not going to get that curve,
you are not going to get that curve.
So that's the first holy grail
of HSA right there
as you have seen.
Just as a matter of comparison
what you see here is three graphs.
On the left, is the signature or pattern HSA.
I also took some protein that I talked about
that it's called transferrin.
And we ran it normal transferrin.
It has its own signature.
As you can notice,
there's a little peak at around 60 degrees right there.
And another major pick at around 75 degrees.
And it doesn't matter if you take transferrin,
ran it on DSC, it doesn't matter what type of DSC you get,
you're going to get that signature.
You will also see some certain structures here
which is some proteins, but we don't know yet.
It could be some contamination in there
but it could be some binding that we are yet to figure out.
This is another comparison.
The whole plasma, which is a combination
of all these proteins and many more.
If you notice, this thermogram here
you can see that there is abundant HSA here.
But you'll start seeing other things that are in there.
I'm going to show you in the next slide
the most abundant proteins that we can get in plasma.
And this is some published data
that you can go to this website here.
This is just a summation of how plasma, normal plasma
when we say normal,
we mean they've been screened of certain diseases.
They've been screened on certain disease.
You can notice here, the dark stuff here
it means these experiments were done over and over again.
And these are just averages.
And this is a real patient about 15 individuals,
nine males, six females ages 22 to 50.
And it's so consistent there is a peak right there,
and there's a peak right there,
there's a peak right there
and there's other stuff right there, that's plasma.
And we were also, this paper was also able to go in here
and figure out what is here.
This is (mumbles) fibrinogen right there.
This is a HSA right there.
Since this is all plasma
there's also other proteins like haptoglobin right there.
IGG, LGG and LGA are also right there.
And some transferrin right at the end
but we can ran the transferrin alone,
we can ran the fibrinogen alone
I think I've already shown you that.
Now, this is what I'm talking about.
These are patterns that are repeated over and over again
and I decided to call them biochemical signatures
for specific diseases.
Plasma and HSA in medication.
So, we are able to go in and take some common medications
and other unknown medication.
Doesn't see if we can see patterns here.
So, we did that for that
and many more
but I'm just going to show you for one or two right here
in the interest of time.
So, what you see here is where we will bind
I'm gonna start with graph A here.
This is whole plasma,
we now know how whole plasma looks like.
In black here is plasma alone.
That's the curve.
And in red here, this is when we bind plasm to naproxen.
You can see a shift
a significant shift in the thermogram here.
And we can use that to identify these medications.
And on the B right here you can notice
HSA alone which is this nice peak reproducible
and then bind it to naproxen here.
We can see a significant shift in melting temperature
and this is repeated over and over again.
It means that you can use it to identify these things.
So we're gonna shift the thermograms
this is a biochemical signature
so that's what I'm talking about here.
Shifts in thermograms and we can measure them.
You can read more about this in the publication
that is posted online.
I was able to work with Angelina Calnan,
I talked to you about her earlier.
And she did couple of work on,
as you can now see here
this is just an example of her work here.
She was able to observe that
and a significant shift
to put this medication.
We are yet to publish this
but she was able to present this work
at American Chemical Society in 2018
and that was a phenomenon.
If any students wanted to work on this,
find me and follow, we can get you involved.
Okay, this is another work under construction
where we have started binding some unknown medications
to these plasma in black normal
and we can see some significant shift here,
this is one-to-one titration with a certain medication.
We will be working more on that.
I am going to rush a little bit here.
We did some experiments with the biotin.
As you noticed, biotin is a form of vitamin B.
We had sort of a forms of vitamin B over there.
But most importantly,
if you have taken a little bit of advanced bio
it is a core factor responsible for carbon dioxide transfer
in processes like gluconeogenesis
process called oxidative decarboxylation.
So, what we did here is we decided to investigate attachment
of biotin to HSA and plasma.
And this process is called a biotylation
when you attach biotin to that
we get a fancy term, biotinylation.
And I'm going to quickly go over what was done.
So we, got two commercially available preparations of HSA
one was 99% pure and other was a 96%,
but 3% difference is mostly
due to globulins and fatty acids.
We wanted to see DSC can be able to descend these changes.
And this is a multiple binding sites
and we were able to,
to do some work on this
and I'll briefly go over this.
And, when you see the red arrow it means
we're talking about the most pure form of HSA
and green in the 96%,
I guess no 96% is all the fatty acids
and globulins right there.
And they're few housekeeking rules
on binding, on HSA.
We have what we call single site binding.
We can go on and pick a site where
we know we just blogged one site here.
And then under neutral condition we found that
there's only one reduced sulfhydryl group
on cysteine number 34.
And we buy this link from Thermo-Fisher Scientific.
It comes with instructions on how to block one site.
Similarly, we can do multiple site binding.
And in this one,
this link here, the EZ of sulfo-NHS link
from Thermo-Fisher,
it targets about 59 lysine residues.
And literature shows that about 20 or so are exposed.
So, we just gonna unblock them.
So these are different kits to block.
So we have a way to investigate single site binding
verses multiple binding.
And here's the data that we'll got initially here.
As you notice here,
HSA 99 is the most pure one,
which is nice curve reproducible
with little error biases for many experiments
that were done
and they all came to be within a certain margin of error.
I wanna skip this one in dotted lines a little bit
then I'm gonna talk about this one.
In which we have so many other things the dirty one,
the HSA 96 with a lot of blends and (indistinct).
We can see there is the melting is a little bit further
and we've got two clear signs right there.
And we call this a bi-phasic melting transition.
The dashed thermogram is when we decided
after a year or so to remelt this.
And we figured it out in condense into the stable form
that was surprising.
So that's what we're talking about right here, okay?
And then I talk about bi-phasic melting transition.
All right.
Next, we did single site binding,
as you noticed this is a normal HSA.
When I bind it to biotin
you can see a shift in thermogram.
Not only that there's a drop in this peak
and we calculated all the binding constant,
the thermodynamics and it's all listed in the paper.
We have different ones here meaning we were titrating
like one to one Biotin one to say, 20 ratio molar,
ratio (indistinct).
So, we don't see like significant changes when we titrate,
but we can see a significant shift right there.
And if we do that with HSA 96,
which is the one on the globulins,
we have a clear one right here.
And once you attach biotin you can notice
this peak and this peak kind of disappear
and we wanna get to one single peak here.
And this is, we suspect is a, you do some aggregation.
That'll get to talk about later.
So that's a major differences here.
It's consolidation of peaks in a less stable form.
And then that's it right there.
And we went on to do multiple site binding.
And in the interest of time
I'm just gonna show you the pure one.
This is normal HSA binding curve
and these are titrations from lower molarity so
these are titrations from lower molarity here,
we see a shift.
We increase the molality to see a different shift
and it keeps on going until saturation.
So, we actually see significant differences
in the attachment of biotin and the saturation curves.
But home message here is
we see significant temperature shifts
for the most stable form or the most pure form of HSA.
In the next three minutes or so,
I am gonna talk about how HSA binding
affects other diseases.
So, before I proceed the tickle message here
will be the binding of many drugs
the HSA patients can be changed in disease states.
So, just because this is work in progress
I'm going to give you some work done
by my colleagues at University of Louisville here.
And the topic is about, "Biochemical signatures."
So these are real patients here.
So they took patients.
I'm gonna start with the left here.
This is a curve normalized for plasma
with so many rounds done on real patients here.
This is normal, meaning there've been screened
of rheumatoid arthritis.
And then in red here
these are patients who are screened rheumatoid arthritis
and you can see significant difference here.
Lyme disease, same thing.
This is a normal patients not suffering
from that disease screened.
You can see a significant shift here.
When rhyme,
this on HSA patients we know have this disease.
Lupus, same thing is auto-immune diseases right here.
We can see some, some shifts here in thermograms.
And on the right here is this thing put together
in this publication here,
we can read more about this.
So, you can see here
that this is the normal plasma right here,
and you can see some significant shifts
in specific diseases.
Blue here, rheumatoid arthretis, the lyme right there,
the lupus.
We have done this over and over again.
And right now I'm working with some melanoma
some tabs of ovarian cancer patient and diabetic.
We get these patients already screened maybe
from Mayo clinic and all these other places.
And it will be interesting that we can
get some reliable biomarkers right there.
I would like to thank these awesome people
for letting me speak.
First my sponsor Chemistry and Physics Department
and Charles Albert Read Trust.
And my colleagues (indistinct)
Biology and Department for the Invitation
Past students I just mentioned a few
but you're also welcome all collaborators and consultants
mostly at Beverly right there.
And professor Albert S. Benight
and some colleagues at University of Louisville.
What is the take home message here?
Last minute.
Number one,
I told you that plasma and HSA produced thermograms
and thermograms can be used to show us some disease states.
Number two,
surely this technology has potential
to screen new disease candidates, drug candidates
the new and old ones.
My goal in the long term is number three.
I need to create a drug binding library with (indistinct).
We can actually predict other drugs that are gonna be made
whether their behaviors then I mentioned
some of the drugs that we already know.
That's the goal, number three.
I already showed you too
that we can use this technology to distinguish
between disease states and non-disease states.
And number five.
I told you about biochemical signatures today.
Thank you.
(mumbles)
- Thank you very much Dr. Manyanga for your wonderful talk.
Thank you for informing us.
And at this time myself, I'm Dr. Gordon from biology
and my colleague, Dr. Nelson Scottgale
will help to answer, to provide some of the questions
that our audience have submitted.
So, I'm gonna go directly to the first question
which is, what is indicated by the shift
in the signature noted from the thermograms with HSA alone
versus the increase in temperature signature
when SHA was combined with naproxen?
- Okay.
Let me see if I can go to that slide.
Can I go to the slide?
Let me see if I can do that.
(mumbles)
Okay.
I'm going to the slide of naproxen
and I'm gonna ask you to repeat that a little bit.
I did not get the last part,(mumbles)
- Okay, the question was,
what is indicated by the shift in the signature noted
from the thermograms with HSA alone
versus the increase in temperature signature
when HSA was combined with naproxen?
- Okay so, what we are doing here,
we're just trying to see
how the stability of medications
first the stability of medications affect HSA.
Because this information with this information
if I know there's a significant binding for naproxen to HSA,
it will change the way I'm going to administer
this naproxen because remember these drugs,
they have to go get in the systemic circulation.
So, if I'm predicting this drug is going
to be bound a lot by HSA
it means it's gonna affect its bioavailability.
I have to either a more defined
the way I'm going to administer it or change the molarities,
knowing very well that I have to factor in
that there's some strong binding.
So, a change in that shift it just tells me
that there is some significant binding.
- Thank you Dr. Manyanga
and I believe Dr. Nelson Scottsdale has the next question.
- [Dr. Nelson Scottsgale] Thank you for
a very interesting talk, Dr. Manyanga.
The next question has to do with
or came in at about the time when you were talking
about biotin and it says,
can this be done with any vitamin
- The binding, if the vitamin binds to HSA,
yes, I believe there's a lot of literature on binding
of vitamin X to HSA or plasma.
They should be a lot of literature that is
if there is binding.
But if HSA is responsible for transporting
those vitamins, yes.
You can even find the thermodynamics on the curves.
But I think a quick Google say
to somebody should have figured it out already by now.
Okay.
- Well, I'm going to ask the next question.
Is there any theory about the mechanism causing the shifts
with any of these diseases?
- Yes, that's the problem.
That's the problem we have.
So, if you notice the plasma protein is over 3000
in visual protein.
So right now, when the DSC
because today I focus on DSC alone,
but with the DSC that's actually like the fist point,
just to see HSA some binding here,
but we are not really sure on what exactly
is causing them binding ,
where exactly is gonna cause.
Some people have done isothermal titration, calorimeters
they can, you can see in real time as this happens.
Some people have done what we'll call plasma depletion,
you can do depletion studies,
which means you can take plasma
and try to remove everything else.
And then just ran one by one,
just to see if there's if those changes reproducible.
But yes, the DSC alone is just like a first point,
but we don't know exactly
how these interactions are happening.
And that's one of the reasons why it's very hard
at this point to get FDA approval
for diagnosis with this one because
we cannot pinpoint like the real
the exact mechanism right now.
Some people have done NMO
to a reasonable degree
but honestly we just really don't know
the mechanism right its work in progress.
- The next question is it relates in some ways,
do you know why the different diseases cause the shifts?
- Well, like I said, this is just kind of a biomarker level
we just accidentally saw there is a shift on this disease
there is a shift on this disease.
But the problem is
the major problem we outlined in the previous paper was
we don't know like, yes you have been suppose I'm looking
at someone with a rheumatoid arthritis,
they have been screened, not to have rheumatoid arthritis
and this normal plasma here.
That person has been screened
and did not to have rheumatoid arthritis
may be suffering from other disease
(laughs) that we don't know.
So, so far we are just able to say,
"(indistinct) if this rheumatoid arthritis",
"we are seeing this pattern".
"If there's early, we are seeing this pattern".
And the utility of this,
I know there's one company trying
to take this to another level.
The utility of this is,
this would be the dream, maybe 10 to 20 years from now.
There could be a way people can find these patterns
in so many diseases that we can do
what we call multiplexing.
Like on my wish list, suppose I go to my doctor's office
for my annual exam,
and there's a tool like this.
If I can ran a blood sample for one hour
that tells me different shifts.
Yes, I'll be worried in a such
I am worrying about my health right away
and then I start going from one doctor to another
I just know there's something wrong my plasma is not normal.
So we can detect multiple things long-term
but so far we are not there.
But I just hope
we should be able to combine these shifts
in disease states and do multiplexing.
And honestly, if I'm suffering from this disease
and I get another shift on that
it would tell me to make a better decision
to get go and get screened on those things
and it should be cheaper.
And I can catch my diseases earlier by just a shift.
And honestly, I'll be worried
if my it's my thermogram shift is there
I'll start taking care right away.
Okay.
I don't know if I answered the question.
- Thank you.
- All right. - All right.
- Yes, I think you did answer the question.
Thank you, Dr. Manyanga.
And we'll now go to the next question
from my colleague, Jason Brown,
which is is a long-term goal to use the technology,
to try to identify drugs that revert
the altered biochemical signatures
in different disease states
from a normal signature?
- Yes.
(mumbles) like,
there should be like a screening tool,
like I said, over 90% of medications fail out there
because when are designing a medication,
one of the major things you need to worry about
is the pharmacokinetics of it.
So,
if I am
designing a drug and I see the binding here
is extraordinary, it's going to affect
like the volume of distribution
it's gonna affect how much it's gonna be available.
I don't want to take, say a medication by mouth.
That's gonna be say 30% available
in the systematic speculation
because it has been bound already.
So, I need that information
to see if I can administer my medication orally
by injection and all that,
because I need to know that binding.
How it's going to be affected by the bound
and the free medications long time.
That's number one goal, drug screening.
Number two goal is
to investigate medication interactions.
I can start investigating medication indirection.
For example,
if you go say to the cytochrome P four feet right there
you'll see some medications that either
up-regulate or down-regulate if they are together,
what you can do right there is,
you can use this technology to see
if the binding constants are close enough
for two medications that you want to administer
at the same time
or that kind of information.
And the third goal
is new medications.
For example,
I do have the data but I cannot show it right now.
We just went over and look at five HIV medications
on the counter.
We just take them and start running them.
We are interested to see if there any patterns
that are repeating.
They could, we don't know for sure
but they could be working
on the same binding site or some signature.
So we can predict other things or new drugs that are coming
before we start manufacturing them.
Like for example, I'm populating a library
of binding concepts for the medication that we know.
And once in a while,
we're gonna throw some medications that we don't know yet
to see if there's any patterns
just pattern recognition at this point.
- Thank you very much, Dr. Manyanga.
I think we'll have to leave it there
and I'll turn it over to Dr. Ronald Mac Taylor.
- Thank you Dr. Manyanga for this talk today.
On behalf of your colleagues
in the Department of Chemistry and Physics,
Darwin Fest and the Biology Department
we just wanna thank you for this excellent talk,
learning a little more about biochemistry all the time
which is a fun and exciting,
so, thank you very much.
- And this concludes our presentation and the webinar.
Thank you very much
and we look forward to seeing you at the talk
at 2:00 this afternoon.
Thank you so much.
We appreciate it. (mumbles)
Bye bye everybody.
- Bye bye.