1 00:00:05,610 --> 00:00:07,210 - I am Dr. Nelson Scottgale, 2 00:00:07,210 --> 00:00:10,160 a member of the Darwin Festival Committee 3 00:00:10,160 --> 00:00:12,240 of Salem State University. 4 00:00:12,240 --> 00:00:13,720 Welcome to the third day 5 00:00:13,720 --> 00:00:16,650 of the 42nd annual Darwin Festival 6 00:00:16,650 --> 00:00:17,963 at Salem State University. 7 00:00:19,150 --> 00:00:21,820 So, what I'd like to do now is 8 00:00:23,929 --> 00:00:27,610 turn it over to Dr. Ronald Mac Taylor, 9 00:00:27,610 --> 00:00:31,700 who can give us an introduction to Dr. Manyanga. 10 00:00:31,700 --> 00:00:32,940 Thank you very much for being here 11 00:00:32,940 --> 00:00:37,013 and we look forward to our talk today. 12 00:00:39,250 --> 00:00:40,610 - Hello everyone. 13 00:00:40,610 --> 00:00:42,410 My name is Dr. Ronald Mac Taylor. 14 00:00:42,410 --> 00:00:45,640 I am the chair of the Department of Chemistry and Physics. 15 00:00:45,640 --> 00:00:48,970 And today we get to hear from one of my colleagues, 16 00:00:48,970 --> 00:00:51,260 Dr. Fidelis Manyanga. 17 00:00:51,260 --> 00:00:56,220 Dr. Manyanga came by way of his undergraduate training 18 00:00:56,220 --> 00:01:00,260 in Zimbabwe to Portland State University 19 00:01:00,260 --> 00:01:03,040 where he earned his PhD. 20 00:01:03,040 --> 00:01:06,650 And we were fortunate that 21 00:01:07,510 --> 00:01:12,040 at the time that we were looking to add another biochemist 22 00:01:12,040 --> 00:01:14,070 to our faculty, 23 00:01:14,070 --> 00:01:17,310 Dr. Manyanga happened to be looking for a position 24 00:01:17,310 --> 00:01:20,020 and we were delighted 25 00:01:20,020 --> 00:01:24,120 when we saw his materials and met with him 26 00:01:24,120 --> 00:01:29,120 and it was fabulous to bring him to Salem State. 27 00:01:29,720 --> 00:01:34,720 I remember well, in his first year teaching here 28 00:01:34,960 --> 00:01:39,960 the 2014/2015 academic year. 29 00:01:40,220 --> 00:01:45,220 In December of 2014, Dr. Manyanga came and spoke to me 30 00:01:45,570 --> 00:01:49,037 and said, "Where is the snow," 31 00:01:49,037 --> 00:01:51,867 "these New England winter are supposed to be legendary" 32 00:01:51,867 --> 00:01:54,227 "but there's no snow, what's going on," 33 00:01:54,227 --> 00:01:56,117 "I thought there would be snow." 34 00:01:57,210 --> 00:02:02,210 For those of you who don't remember in January, 2015 35 00:02:02,730 --> 00:02:07,730 we had a blizzard followed by three other snowstorms 36 00:02:08,440 --> 00:02:12,800 that all gave us over a foot of snow each. 37 00:02:12,800 --> 00:02:16,110 By the end of this three and a half week period 38 00:02:16,110 --> 00:02:18,563 we had 80 inches of snow. 39 00:02:19,900 --> 00:02:23,883 Dr. Manyanga no longer questions New England winters. 40 00:02:24,720 --> 00:02:28,630 Dr. Manyanga is gonna give us a delightful talk today. 41 00:02:28,630 --> 00:02:30,370 We're looking forward to hearing 42 00:02:30,370 --> 00:02:33,580 about his work with human serum albumin 43 00:02:33,580 --> 00:02:38,580 and I like to welcome him to the Zoom stage. 44 00:02:39,560 --> 00:02:41,823 Hello Fidelis, welcome. 45 00:02:46,550 --> 00:02:48,003 - Can I unmute now and start? 46 00:02:57,704 --> 00:02:58,537 All right. 47 00:02:58,537 --> 00:03:00,690 I'm just fixing one or two things here 48 00:03:00,690 --> 00:03:02,063 and now I get started. 49 00:03:04,480 --> 00:03:05,603 Get my pointer. 50 00:03:08,990 --> 00:03:10,543 Hello, can you hear me? 51 00:03:15,111 --> 00:03:15,944 Hello! 52 00:03:19,682 --> 00:03:20,970 - [Dr. Ronald Mac Taylor] You're good. 53 00:03:20,970 --> 00:03:22,223 - You can hear me, right? 54 00:03:25,740 --> 00:03:26,573 I start? 55 00:03:27,415 --> 00:03:28,390 - [Dr. Ronald Mac Taylor] Please. 56 00:03:28,390 --> 00:03:29,730 - All right. 57 00:03:29,730 --> 00:03:32,310 So, my name is Fidelis Manyanga. 58 00:03:32,310 --> 00:03:35,100 And as Ronald said already 59 00:03:35,100 --> 00:03:39,339 I'm an Associate Professor of Biochemistry and Chemistry 60 00:03:39,339 --> 00:03:42,200 in the Chemistry and Physics Department. 61 00:03:42,200 --> 00:03:44,580 Today I'm going to talk to you about 62 00:03:45,460 --> 00:03:47,280 one of the research that I do 63 00:03:47,280 --> 00:03:51,257 on the "Human serum albumin and plasma." 64 00:03:52,430 --> 00:03:55,720 And I'm gonna show you some of the interesting 65 00:03:55,720 --> 00:04:00,643 or some thought-provoking things that we can do with it. 66 00:04:03,220 --> 00:04:04,660 A few housekeeping here, 67 00:04:04,660 --> 00:04:08,900 my focus here is going to be on blood plasma, 68 00:04:08,900 --> 00:04:10,540 and HSA. 69 00:04:10,540 --> 00:04:12,530 There's so many things that go around 70 00:04:12,530 --> 00:04:16,260 but that's what my focus is going to be. 71 00:04:16,260 --> 00:04:21,260 And also today, I'm going to focus mostly on one technique 72 00:04:21,450 --> 00:04:23,500 called differential scanning calorimetry. 73 00:04:24,560 --> 00:04:26,710 And we leave other techniques to another day 74 00:04:26,710 --> 00:04:28,060 I will just mentioned them. 75 00:04:29,450 --> 00:04:33,350 And because some of these information involve 76 00:04:33,350 --> 00:04:34,800 are real patients, 77 00:04:34,800 --> 00:04:39,053 some data will be redacted deliberately. 78 00:04:40,380 --> 00:04:41,213 Okay. 79 00:04:43,760 --> 00:04:44,827 Since I have the mic, 80 00:04:46,150 --> 00:04:48,390 I'm just going to go ahead and tell you 81 00:04:48,390 --> 00:04:49,683 what to expect today. 82 00:04:50,640 --> 00:04:52,590 Talk a little bit about my career path. 83 00:04:53,570 --> 00:04:56,630 And I will talk about plasma 84 00:04:58,078 --> 00:04:58,911 and HSA. 85 00:05:00,036 --> 00:05:02,580 And then I will try by all means 86 00:05:02,580 --> 00:05:04,760 to give you thermodynamics 101 on 87 00:05:08,990 --> 00:05:13,990 how physical chemistry links to all this. 88 00:05:15,200 --> 00:05:16,970 And then I'll give you a brief 89 00:05:16,970 --> 00:05:21,380 very brief account on differential scanning calorimetry 90 00:05:21,380 --> 00:05:24,763 which I will call DSC from now on. 91 00:05:26,020 --> 00:05:29,403 Then now just jump into the results and, 92 00:05:31,370 --> 00:05:32,203 Let's do this. 93 00:05:32,203 --> 00:05:35,043 So a little bit of my background of, 94 00:05:36,780 --> 00:05:39,990 two majors a BS in chemistry and biochemistry. 95 00:05:39,990 --> 00:05:42,950 And for some of you who know the British system 96 00:05:43,990 --> 00:05:46,503 we all do this in three years. 97 00:05:48,100 --> 00:05:52,610 The only catch is we don't do liberal arts stuff. 98 00:05:52,610 --> 00:05:54,720 If you're go in a chemistry degree 99 00:05:54,720 --> 00:05:56,890 all you have to take is math, chemistry and physics 100 00:05:56,890 --> 00:06:00,363 so that's why it's doable in three years. 101 00:06:01,200 --> 00:06:05,280 So I worked a little bit in an analytical lab 102 00:06:06,350 --> 00:06:10,080 and then I applied for PhD in chemistry 103 00:06:10,080 --> 00:06:11,970 at Portland State University. 104 00:06:11,970 --> 00:06:14,230 I was able to do Biophysics 105 00:06:14,230 --> 00:06:18,363 and I met an extraordinary professor who was my mentor. 106 00:06:19,500 --> 00:06:22,140 After that, I worked for Portland Biosciences 107 00:06:22,140 --> 00:06:23,830 which is a startup, 108 00:06:23,830 --> 00:06:26,487 dealing with DNA and RNA. 109 00:06:26,487 --> 00:06:28,910 And then it got acquired 110 00:06:29,773 --> 00:06:31,873 and we moved on to Louisville Biosciences. 111 00:06:32,860 --> 00:06:37,860 And after some movement right there 112 00:06:39,400 --> 00:06:43,983 I decided to be a professor at Salem State University. 113 00:06:47,090 --> 00:06:52,090 So, I work in three broad areas of research. 114 00:06:53,250 --> 00:06:54,770 The first one is I 115 00:06:56,100 --> 00:06:59,050 work with compounds called Isatins 116 00:06:59,050 --> 00:07:03,573 and we have found out that we can find active ingredients 117 00:07:05,220 --> 00:07:10,220 that we can now potentially put in medications to be sent. 118 00:07:12,720 --> 00:07:16,510 I've had the privilege of meeting a remarkable people 119 00:07:16,510 --> 00:07:18,287 to collaborate on this project 120 00:07:18,287 --> 00:07:20,260 and Dr. Mustafa Yatin here 121 00:07:21,190 --> 00:07:25,960 is at Salem State University is a colleague in that project. 122 00:07:25,960 --> 00:07:30,960 And Jesse here now and PhD at Boston University. 123 00:07:31,690 --> 00:07:34,540 He was one of the guys who started this project with me 124 00:07:34,540 --> 00:07:35,833 when I just got in. 125 00:07:37,100 --> 00:07:41,670 And I have to mention Isra Malik, she expanded this project 126 00:07:42,890 --> 00:07:46,960 and hopefully we will be working on a publication 127 00:07:46,960 --> 00:07:48,840 of some our work. 128 00:07:48,840 --> 00:07:52,587 It needs a little bit of panel beating before we get there. 129 00:07:53,487 --> 00:07:58,030 And I also investigate thermodynamics of DNA and RNA. 130 00:07:58,987 --> 00:08:00,253 And this one, 131 00:08:01,440 --> 00:08:06,440 I don't necessarily have that many tools at Salem State 132 00:08:06,680 --> 00:08:09,010 so I do this one my own. 133 00:08:09,010 --> 00:08:14,010 And I also investigate human serum albumin 134 00:08:14,020 --> 00:08:17,410 a major fatty acid, binding protein and plasma. 135 00:08:17,410 --> 00:08:20,513 And that was lucky, 136 00:08:21,392 --> 00:08:24,623 (indistinct) last one here Angelina Calnan 137 00:08:25,520 --> 00:08:27,320 to work with me on that project. 138 00:08:27,320 --> 00:08:31,180 And we were able to present this work 139 00:08:31,180 --> 00:08:32,760 at the American Chemical Society 140 00:08:32,760 --> 00:08:34,890 and that was wonderful. 141 00:08:34,890 --> 00:08:38,160 And I think it also helped them move on with their career 142 00:08:38,160 --> 00:08:39,683 she works for Cell Signaling. 143 00:08:41,670 --> 00:08:42,503 Okay. 144 00:08:42,503 --> 00:08:45,510 So, let's dig in into what I'm going to talk about today 145 00:08:45,510 --> 00:08:47,320 which is a human serum albumin 146 00:08:50,660 --> 00:08:52,310 and plasma. 147 00:08:52,310 --> 00:08:54,583 So, we start here, 148 00:08:56,310 --> 00:09:00,083 you take your blood here, your blood sample. 149 00:09:01,841 --> 00:09:02,691 Put it in a tube, 150 00:09:03,540 --> 00:09:06,403 this has got a centrifuge it's in it. 151 00:09:08,580 --> 00:09:09,543 If you do that, 152 00:09:10,900 --> 00:09:13,070 you're going to get the following. 153 00:09:13,070 --> 00:09:15,460 At the bottom, we're going to have 154 00:09:17,790 --> 00:09:19,190 erythrocytes which are dense, 155 00:09:19,190 --> 00:09:21,113 is a dense stuff. 156 00:09:22,087 --> 00:09:22,920 And on the top, 157 00:09:22,920 --> 00:09:26,113 you're gonna have, it's a plasma. 158 00:09:27,240 --> 00:09:31,360 And this constitute over 55% of the whole blood. 159 00:09:31,360 --> 00:09:34,070 And today, 160 00:09:34,070 --> 00:09:36,420 we will be talking mostly about 161 00:09:36,420 --> 00:09:38,097 what's on top here, our plasma. 162 00:09:39,755 --> 00:09:41,705 We're gonaa break it down. 163 00:09:41,705 --> 00:09:42,955 For example, this plasma, 164 00:09:44,070 --> 00:09:45,120 if you look down here 165 00:09:46,640 --> 00:09:50,697 over 90% by weight of water is water 166 00:09:50,697 --> 00:09:53,003 and 60% is albumin. 167 00:09:55,833 --> 00:09:56,666 Albumin here. 168 00:09:57,975 --> 00:10:01,810 And we also have about 36% are all globulins. 169 00:10:05,090 --> 00:10:07,020 We also have some fibrinogen 170 00:10:07,020 --> 00:10:08,543 which is, fibrinogen. 171 00:10:14,005 --> 00:10:17,943 Fibrinogen is responsible for clotting factors in blood. 172 00:10:19,090 --> 00:10:20,673 So, if you notice down here, 173 00:10:21,820 --> 00:10:25,150 if we remove the clotting factors from this plasma 174 00:10:25,150 --> 00:10:26,683 we get what we call serum. 175 00:10:27,870 --> 00:10:31,150 So, since we're dealing with albumin from humans here, 176 00:10:31,150 --> 00:10:35,300 I'll be calling it, human serum albumin. 177 00:10:39,651 --> 00:10:43,550 And the abbreviation is HSA moving forward. 178 00:10:43,550 --> 00:10:46,980 Sometimes they start from the top right here. 179 00:10:46,980 --> 00:10:49,833 So let's go and dig a little bit into that. 180 00:10:51,430 --> 00:10:52,263 So, 181 00:10:53,150 --> 00:10:55,330 I don't make this stuff. 182 00:10:55,330 --> 00:10:58,890 We don't have the capability of going to patients 183 00:10:58,890 --> 00:10:59,913 and getting this. 184 00:11:00,958 --> 00:11:03,330 We have a company called the Sigma-Aldrich 185 00:11:03,330 --> 00:11:05,259 they do all this stuff for us 186 00:11:05,259 --> 00:11:07,510 we just buy it. 187 00:11:07,510 --> 00:11:09,177 We just buy them and sell albumin 188 00:11:09,177 --> 00:11:12,280 it has been screened of all the diseases 189 00:11:12,280 --> 00:11:14,853 and takes a lot of work out for us. 190 00:11:16,710 --> 00:11:17,543 Okay. 191 00:11:17,543 --> 00:11:22,390 So, let's talk a little bit more about human serum albumin. 192 00:11:22,390 --> 00:11:23,223 Let me say this, 193 00:11:23,223 --> 00:11:27,170 it is a most abundant protein in plasma. 194 00:11:27,170 --> 00:11:30,493 About 60% by mass of the total plasma. 195 00:11:31,400 --> 00:11:36,400 It's responsible mostly to transport fatty acids, 196 00:11:37,410 --> 00:11:39,063 fatty acid transporter. 197 00:11:40,730 --> 00:11:43,773 And I can say, number two role is to regulate 198 00:11:43,773 --> 00:11:48,610 the oncotic pressure in the body 199 00:11:48,610 --> 00:11:51,730 because you can look at circulatory system. 200 00:11:51,730 --> 00:11:55,360 Once blood is pumped from your heart, 201 00:11:55,360 --> 00:11:57,020 there has to be something responsible 202 00:11:57,020 --> 00:12:00,120 to distribute it evenly to all parts of your body. 203 00:12:00,120 --> 00:12:02,773 And we generally call that public pressure. 204 00:12:04,230 --> 00:12:09,230 This human serum albumin is also a powerful antioxidant 205 00:12:09,260 --> 00:12:13,920 because it is one reduced sulfhydryl groups 206 00:12:15,750 --> 00:12:20,670 that can get linked to a lot of thing 207 00:12:20,670 --> 00:12:25,173 that remove dangerous reactive oxygen species in our body. 208 00:12:26,040 --> 00:12:31,040 So, one of my major talks today is right here in red. 209 00:12:31,650 --> 00:12:34,380 We're gonna see how we can study 210 00:12:34,380 --> 00:12:37,223 these human serum albumin and how it links to blood. 211 00:12:38,550 --> 00:12:39,383 Okay. 212 00:12:39,383 --> 00:12:41,470 A little bit more science here. 213 00:12:41,470 --> 00:12:43,860 The primary structure. 214 00:12:43,860 --> 00:12:47,140 This is a nice stuff for the students 215 00:12:48,490 --> 00:12:50,950 who are required to be in this class to get credits. 216 00:12:50,950 --> 00:12:53,730 The primary structure of a protein. 217 00:12:53,730 --> 00:12:55,263 This one is 585 amino acids. 218 00:12:58,670 --> 00:13:01,800 The molecular weight is about 66.5 kilodaltons, 219 00:13:01,800 --> 00:13:04,153 you'll see it in gel electrophoresis 220 00:13:06,800 --> 00:13:08,450 As you can see on the right here, 221 00:13:10,450 --> 00:13:14,923 they are three structurally conserved domains. 222 00:13:16,240 --> 00:13:19,663 Like right here, domain one, 223 00:13:20,810 --> 00:13:23,070 domain two, 224 00:13:23,070 --> 00:13:24,620 domain three. 225 00:13:24,620 --> 00:13:29,050 And what's on top is the amino acid sequences, right there. 226 00:13:29,050 --> 00:13:31,913 And these domains are sub-divided into A and B. 227 00:13:33,000 --> 00:13:36,583 This is just to let us know where things are binding. 228 00:13:37,590 --> 00:13:41,640 And the secondary structure is mostly 229 00:13:41,640 --> 00:13:45,003 some alpha-helix helical coils. 230 00:13:46,000 --> 00:13:47,930 And a sudlow here, 231 00:13:47,930 --> 00:13:50,840 on reference number six of the (indistinct) 232 00:13:50,840 --> 00:13:52,373 did a lot of work, 233 00:13:53,690 --> 00:13:57,150 and by now we know where certain drugs binds 234 00:13:57,150 --> 00:14:00,683 and mostly it's site one and two, 235 00:14:02,660 --> 00:14:07,000 the Ibuprofen, (indistinct), they all bind to site man 236 00:14:07,000 --> 00:14:12,000 and then, also a few things to note about HSA right here. 237 00:14:14,100 --> 00:14:16,903 We have about, 238 00:14:20,006 --> 00:14:22,070 59 lysin residues 239 00:14:23,190 --> 00:14:25,460 and about 35 sistin residues, 240 00:14:25,460 --> 00:14:27,960 17 fibrigens. 241 00:14:27,960 --> 00:14:30,373 This is going to be important as I proceed. 242 00:14:31,420 --> 00:14:34,638 And the last thing to note about this is 243 00:14:34,638 --> 00:14:36,738 I have, we have a lot of ionized residues, 244 00:14:37,830 --> 00:14:42,450 which is responsible for its globular functions 245 00:14:43,500 --> 00:14:45,930 because it's tertiary restructure for this 246 00:14:45,930 --> 00:14:47,440 is a globular protein 247 00:14:47,440 --> 00:14:50,313 that allows it to bind and transport 248 00:14:50,313 --> 00:14:54,267 the diverse amount of molecules and metabolites in the body. 249 00:14:55,507 --> 00:14:56,660 All right. 250 00:14:56,660 --> 00:14:58,753 Why do we really care about this studies? 251 00:14:59,633 --> 00:15:02,128 Okay, this is a fact, 252 00:15:02,128 --> 00:15:05,850 abundant literature shows that over 90% 253 00:15:05,850 --> 00:15:08,580 of new drug candidates fail. 254 00:15:08,580 --> 00:15:12,090 Even this time for suppose they were looking 255 00:15:12,090 --> 00:15:14,433 for this Covid vaccine. 256 00:15:15,300 --> 00:15:20,140 They probably started with over 2000 candidates 257 00:15:20,140 --> 00:15:21,780 until they come up with one. 258 00:15:21,780 --> 00:15:23,633 It doesn't mean that just that one 259 00:15:23,633 --> 00:15:26,370 we also have other candidates in the pipeline. 260 00:15:26,370 --> 00:15:31,370 So, a lot of the failure rates, drug making is huge. 261 00:15:34,690 --> 00:15:38,160 And just because plasma and HSA bind 262 00:15:38,160 --> 00:15:40,545 both reversibly and covalently 263 00:15:40,545 --> 00:15:43,117 to a broad spectrum of substances 264 00:15:45,020 --> 00:15:47,580 it will affect its bioavailability. 265 00:15:47,580 --> 00:15:49,830 How much the medication is gonna go 266 00:15:49,830 --> 00:15:52,380 into systemic circulation. 267 00:15:52,380 --> 00:15:55,544 I'm gonna use this word lightly here 268 00:15:55,544 --> 00:15:58,950 ligands is any external thing that binds. 269 00:15:58,950 --> 00:16:02,623 So a drug, can be a ligand 270 00:16:03,900 --> 00:16:07,427 but not all ligands are drugs. 271 00:16:07,427 --> 00:16:10,120 Another word we use here 272 00:16:10,120 --> 00:16:10,953 is xenobiotic. 273 00:16:14,000 --> 00:16:15,940 I don't want you to be scared of these words. 274 00:16:15,940 --> 00:16:20,940 Xenobiotic is just an external thing that bind to something. 275 00:16:22,470 --> 00:16:26,300 So, I can use these words interchangeably 276 00:16:26,300 --> 00:16:27,973 sometimes a ligand, 277 00:16:28,940 --> 00:16:32,571 if it's confuses you just think it as a drug. 278 00:16:32,571 --> 00:16:34,713 Xenobiotics is a blanket term. 279 00:16:36,080 --> 00:16:40,080 In the world of biochemistry, 280 00:16:40,080 --> 00:16:45,080 we also use the word adduct or adducts it's the same stuff. 281 00:16:45,840 --> 00:16:48,340 So, assessment of plasma and HSA binding 282 00:16:48,340 --> 00:16:50,284 for new drug candidates, 283 00:16:50,284 --> 00:16:54,610 is one of the goals for doing the stylists, okay? 284 00:16:54,610 --> 00:16:56,220 So, that's what you see right there, 285 00:16:56,220 --> 00:16:59,550 overarching goals to investigate and exploit the ligand 286 00:16:59,550 --> 00:17:02,167 or xenobiotic binding properties of HSA. 287 00:17:03,560 --> 00:17:05,650 And the next question obviously will be, 288 00:17:05,650 --> 00:17:09,640 can we potentially use this technology 289 00:17:09,640 --> 00:17:13,810 for (indistinct) markers 290 00:17:13,810 --> 00:17:18,153 or other things that may maybe important. 291 00:17:19,350 --> 00:17:23,360 A few housekeeping here in the interest of time. 292 00:17:23,360 --> 00:17:24,700 I'm not going to move too much 293 00:17:24,700 --> 00:17:26,777 into materials and methods here, 294 00:17:26,777 --> 00:17:28,090 but we just wanna make sure 295 00:17:28,090 --> 00:17:30,690 if you decide to work on this stuff 296 00:17:30,690 --> 00:17:34,480 all materials and reagents must be molecular biology grade 297 00:17:34,480 --> 00:17:36,150 or higher. 298 00:17:36,150 --> 00:17:40,270 And we buy most of it from Sigma-Aldrich 299 00:17:40,270 --> 00:17:43,557 and Thermo Fisher Scientific and then (mumbles). 300 00:17:45,460 --> 00:17:48,916 Usually as the lyophilized powder. 301 00:17:48,916 --> 00:17:51,650 Lyophilized it's a fancy term, 302 00:17:51,650 --> 00:17:53,663 but just means freeze dried, 303 00:17:57,090 --> 00:17:58,173 dried powder. 304 00:17:59,530 --> 00:18:01,123 And then we re-constituted it in a buffer 305 00:18:01,123 --> 00:18:04,340 we checked the total ionic strength of the buffer, 306 00:18:04,340 --> 00:18:05,890 we checked the protein concentration 307 00:18:05,890 --> 00:18:08,883 by using BCA Protein Assay Kit. 308 00:18:09,760 --> 00:18:13,960 We did gel electrophoresis at every time to check 309 00:18:15,210 --> 00:18:18,670 if our (indistinct) is still intact or not 310 00:18:18,670 --> 00:18:21,600 And gel electrophoresis material 311 00:18:21,600 --> 00:18:23,940 mostly from Sigma-Aldtrich. 312 00:18:23,940 --> 00:18:26,480 And when I arrived at Salem State, 313 00:18:26,480 --> 00:18:28,280 I also got a generous gift 314 00:18:29,370 --> 00:18:32,010 for some gel electrophoresis material 315 00:18:32,010 --> 00:18:33,453 from professors Tracy Ware. 316 00:18:34,920 --> 00:18:36,493 And as you can notice here, 317 00:18:37,970 --> 00:18:39,910 if I'm looking at a human serum albumin here 318 00:18:39,910 --> 00:18:41,370 on the ladder here, 319 00:18:41,370 --> 00:18:44,090 we always check the molecular weight right here 320 00:18:44,090 --> 00:18:46,843 before we run it for whatever we doing. 321 00:18:48,720 --> 00:18:51,030 So, I'm gonna talk a little bit 322 00:18:51,030 --> 00:18:53,030 about differential scanning calorimeter. 323 00:18:54,530 --> 00:18:58,120 So, this is just a machine with a computer connected to it. 324 00:18:58,120 --> 00:19:03,120 And right here, let me see if I can drop my pointers here. 325 00:19:05,680 --> 00:19:09,340 Right here is where we put a sample 326 00:19:09,340 --> 00:19:11,320 the biological sample. 327 00:19:11,320 --> 00:19:13,973 And that sample has to be measured with the buffer. 328 00:19:15,130 --> 00:19:19,813 And that's picture view here, sample cell. 329 00:19:20,958 --> 00:19:23,910 The reference cell is always a buffer 330 00:19:23,910 --> 00:19:26,900 and we're gonna hit this 331 00:19:26,900 --> 00:19:28,130 and we're going to check 332 00:19:29,200 --> 00:19:33,700 the temperature difference between the buffer and the cell. 333 00:19:33,700 --> 00:19:35,313 And we collect that information. 334 00:19:35,313 --> 00:19:37,590 Because if we eat the sample 335 00:19:37,590 --> 00:19:40,120 a biological sample and a buffer with nothing, 336 00:19:40,120 --> 00:19:42,510 we're going to get some differences 337 00:19:42,510 --> 00:19:45,430 in the way they absorb heat. 338 00:19:45,430 --> 00:19:48,350 So, this is the model for the DSC. 339 00:19:48,350 --> 00:19:50,860 So calorimetry generally measures 340 00:19:50,860 --> 00:19:54,539 the difference in heat flow from the biological material 341 00:19:54,539 --> 00:19:55,920 to the buffer. 342 00:19:55,920 --> 00:19:59,340 And we notice these slight differences 343 00:19:59,340 --> 00:20:01,950 and we take a computer program to figure out 344 00:20:01,950 --> 00:20:04,253 if we'll get good information out of there. 345 00:20:05,264 --> 00:20:06,097 Okay. 346 00:20:06,097 --> 00:20:08,350 So, a computer program is connected right there 347 00:20:08,350 --> 00:20:10,960 and we get our information. 348 00:20:10,960 --> 00:20:12,900 In the interest of time 349 00:20:12,900 --> 00:20:15,497 I need to jump in to data analysis. 350 00:20:15,497 --> 00:20:20,430 'Cause I need to make sure I get enough time to do that. 351 00:20:20,430 --> 00:20:24,350 So this is the raw data we get from the DSC. 352 00:20:24,350 --> 00:20:28,519 As you notice here (sniffing) on the Y-axis here, 353 00:20:28,519 --> 00:20:30,019 is the heat rate in microwatts 354 00:20:31,270 --> 00:20:34,580 and down there on the X-axis is the temperature. 355 00:20:34,580 --> 00:20:35,573 As you notice, 356 00:20:37,380 --> 00:20:42,380 we scan, we just hit the sample, this buffer from zero, 357 00:20:42,761 --> 00:20:45,140 to about 100 degrees and then we cool it. 358 00:20:45,140 --> 00:20:47,940 This is heating, this is cooling. 359 00:20:47,940 --> 00:20:49,830 Will heat it again and cool it 360 00:20:49,830 --> 00:20:50,910 because there's some background noise 361 00:20:50,910 --> 00:20:53,850 that comes with physics. 362 00:20:53,850 --> 00:20:58,210 So, we ran about three to five buffer scans 363 00:20:58,210 --> 00:21:01,347 just to make sure if we can see some reversibility or not. 364 00:21:01,347 --> 00:21:04,000 And we're going to use this information later 365 00:21:04,000 --> 00:21:06,320 to check out with sample is gonna be half. 366 00:21:06,320 --> 00:21:07,820 So that's a buffer. 367 00:21:07,820 --> 00:21:09,083 It's the raw data. 368 00:21:10,570 --> 00:21:15,570 This is a typical HSA sample ran in the same way, 369 00:21:15,960 --> 00:21:19,610 heating range that I already explained this as temperature. 370 00:21:19,610 --> 00:21:22,800 You'll start noticing here is that heat here. 371 00:21:22,800 --> 00:21:25,333 You can start seeing stuff getting like that. 372 00:21:26,760 --> 00:21:30,380 It means there's some differential absorption 373 00:21:30,380 --> 00:21:35,300 of heat right there, between the buffer and the sample. 374 00:21:35,300 --> 00:21:39,800 So this is an example of how we expect seeing them 375 00:21:39,800 --> 00:21:42,730 you don't have to see them all the time, 376 00:21:42,730 --> 00:21:47,220 but you can notice some differences in some absorption here. 377 00:21:47,220 --> 00:21:52,220 And as you notice, if I drop this line down here, 378 00:21:52,320 --> 00:21:55,600 I can see some temperatures at which 379 00:21:55,600 --> 00:21:59,160 at manifestation is happening. 380 00:21:59,160 --> 00:22:01,630 And that's what I'm interested in. 381 00:22:01,630 --> 00:22:05,030 And what we'll do is we'll take this biological material 382 00:22:06,400 --> 00:22:08,033 we convey it to the buffer. 383 00:22:09,130 --> 00:22:13,570 And then we take the molar heat capacity. 384 00:22:13,570 --> 00:22:15,540 We check the molar masses and everything 385 00:22:15,540 --> 00:22:20,220 and we convert everything to something that we call this 386 00:22:21,480 --> 00:22:22,993 the excess heat capacity. 387 00:22:24,710 --> 00:22:28,430 And from there we get away information. 388 00:22:28,430 --> 00:22:30,480 This is an example of raw data. 389 00:22:30,480 --> 00:22:31,963 This goes on like, 390 00:22:34,401 --> 00:22:37,700 thousands and thousands data points going down here. 391 00:22:37,700 --> 00:22:39,310 But I brought that up for, 392 00:22:39,310 --> 00:22:44,310 temperature, power microwatt, time and my baseline 393 00:22:45,500 --> 00:22:47,660 we converted it to molar heat capacity 394 00:22:47,660 --> 00:22:50,340 by taking care of the volume, the molar mass 395 00:22:50,340 --> 00:22:53,180 'cause each biological sample is different 396 00:22:53,180 --> 00:22:55,750 and the machine will give you 397 00:22:55,750 --> 00:23:00,430 that conversion for excess heat capacity, 398 00:23:00,430 --> 00:23:02,563 just the differential heat capacity. 399 00:23:03,483 --> 00:23:06,060 So, we're just adjusting for protein concentration, 400 00:23:06,060 --> 00:23:08,890 cell volume, molecular weight, pressure, 401 00:23:08,890 --> 00:23:11,040 partial specific volume, all that 402 00:23:11,040 --> 00:23:13,530 and we get specific heat capacity. 403 00:23:13,530 --> 00:23:17,010 And this is, basically where we get it. 404 00:23:17,010 --> 00:23:18,910 This process we call it normalization. 405 00:23:20,960 --> 00:23:24,927 So, we have already seen this buffer scan 406 00:23:27,610 --> 00:23:30,600 with nothing happened straight line, back and forth. 407 00:23:30,600 --> 00:23:33,180 This is nice happening right here. 408 00:23:33,180 --> 00:23:34,387 This is a sample scan 409 00:23:37,323 --> 00:23:39,540 (indistinct) exactly the same way 410 00:23:39,540 --> 00:23:42,060 or something happened right there. 411 00:23:42,060 --> 00:23:45,610 And through the process of normalization 412 00:23:45,610 --> 00:23:49,290 you can see here, we're going to get 413 00:23:49,290 --> 00:23:54,030 what we call the heat capacity, specific heat capacity 414 00:23:54,030 --> 00:23:57,470 yeah, against temperature after we adjust everything. 415 00:23:57,470 --> 00:24:02,370 And we'll see a very nice curve happening 416 00:24:02,370 --> 00:24:06,793 and on that curve we'll get this thing called enthalpy. 417 00:24:07,720 --> 00:24:09,060 I'm going to talk briefly about 418 00:24:09,060 --> 00:24:11,370 this is about melting temperature. 419 00:24:11,370 --> 00:24:15,400 And this is essentially just going down here 420 00:24:15,400 --> 00:24:17,630 to about 63.5 right here, 421 00:24:17,630 --> 00:24:21,120 and the area under this curve 422 00:24:22,620 --> 00:24:25,753 give us some thermodynamic information here. 423 00:24:26,702 --> 00:24:29,640 Well, I'll talk briefly about it really soon. 424 00:24:29,640 --> 00:24:32,420 So that's it, that's the melting temperature. 425 00:24:32,420 --> 00:24:35,590 We need that information so much. 426 00:24:35,590 --> 00:24:38,867 And it's melted at a certain that area here. 427 00:24:38,867 --> 00:24:41,760 And we call this normalized graphs. 428 00:24:41,760 --> 00:24:44,040 And for the lack of a better term, 429 00:24:44,040 --> 00:24:46,990 I'm gonna call them, thermograms, 430 00:24:46,990 --> 00:24:48,950 meaning we're getting them from 431 00:24:48,950 --> 00:24:52,040 heat induced melting transition. 432 00:24:52,040 --> 00:24:53,770 So I will call them thermograms. 433 00:24:53,770 --> 00:24:56,733 And these are the title of my talk, 434 00:24:57,767 --> 00:25:01,750 "Thermodynamic signatures, Biochemical signatures," 435 00:25:01,750 --> 00:25:04,860 because if we can do it over and over again 436 00:25:04,860 --> 00:25:06,860 and we get the same data 437 00:25:06,860 --> 00:25:10,040 it means it's something that we can use 438 00:25:10,040 --> 00:25:11,743 for diagnostic purposes. 439 00:25:12,600 --> 00:25:14,550 And we're going to convert everything 440 00:25:14,550 --> 00:25:16,160 into something called free energy mass. 441 00:25:16,160 --> 00:25:18,243 So let me talk briefly about free energy. 442 00:25:20,250 --> 00:25:23,540 Yeah, it's free energy here. 443 00:25:23,540 --> 00:25:24,463 As you notice, 444 00:25:26,755 --> 00:25:27,994 go here. 445 00:25:27,994 --> 00:25:29,740 I'm going to give you a Crash Course 446 00:25:30,600 --> 00:25:33,580 for about a minute on thermodynamics. 447 00:25:33,580 --> 00:25:36,153 So, there's something called a Gibbs free energy. 448 00:25:38,580 --> 00:25:40,220 Lambda and professor (indistinct) 449 00:25:40,220 --> 00:25:43,150 at Yale University a long time ago. 450 00:25:43,150 --> 00:25:45,900 It is a combination of something we call delta H, 451 00:25:45,900 --> 00:25:48,120 which is called the enthalpy. 452 00:25:49,460 --> 00:25:53,913 This is essentially the forces in a chemical bond. 453 00:25:55,030 --> 00:25:56,630 There's more to it. 454 00:25:56,630 --> 00:25:58,400 This is temperature dependent on it 455 00:25:58,400 --> 00:26:02,950 and this is delta S is change in enthalpy, 456 00:26:02,950 --> 00:26:04,773 which is a measure of disorder. 457 00:26:05,950 --> 00:26:08,650 So, if you notice these two things, 458 00:26:08,650 --> 00:26:12,450 these are forces putting together chemical bond 459 00:26:12,450 --> 00:26:14,250 these are forces that don't wanna bring them apart. 460 00:26:14,250 --> 00:26:18,480 So there's gonna be, some of forces have a competition here. 461 00:26:18,480 --> 00:26:19,780 There're competing forces. 462 00:26:21,140 --> 00:26:25,200 And, there are three scenarios. 463 00:26:25,200 --> 00:26:30,200 Scenario number one, if the overall delta G is negative, 464 00:26:30,690 --> 00:26:32,550 the reaction proceed forward. 465 00:26:32,550 --> 00:26:34,880 That's what we need to know for this papers. 466 00:26:34,880 --> 00:26:37,963 If the overall delta G is equal to zero, 467 00:26:39,350 --> 00:26:41,630 it means the system is at equilibrium. 468 00:26:41,630 --> 00:26:44,490 We can essentially calculate what we call 469 00:26:44,490 --> 00:26:46,740 the equilibrium constant. 470 00:26:46,740 --> 00:26:50,553 And this is we can also calculate the binding constant, 471 00:26:51,529 --> 00:26:53,973 which is the numbers we need for medications. 472 00:26:54,930 --> 00:26:56,530 And if the process is positive, 473 00:26:56,530 --> 00:26:58,580 it means the proceeds is reversed. 474 00:26:58,580 --> 00:27:00,780 We are not getting anything going backwards, 475 00:27:02,360 --> 00:27:05,080 so, that's thermodynamics 101. 476 00:27:05,080 --> 00:27:08,060 Because I'll be showing you a delta G all the time 477 00:27:08,060 --> 00:27:10,923 and Let's see it right there. 478 00:27:12,820 --> 00:27:13,950 And this is the information that we're going to get 479 00:27:13,950 --> 00:27:17,180 from our DSC as technical as it is 480 00:27:17,180 --> 00:27:19,360 I'm trying to break it down a little bit. 481 00:27:19,360 --> 00:27:21,070 So heat capacity, I already told you, 482 00:27:21,070 --> 00:27:26,010 it's the amount of heat needed to raise the temperature 483 00:27:26,010 --> 00:27:28,133 of one gram of material by one degree. 484 00:27:29,790 --> 00:27:33,863 Now, these are the curves I showed you before, 485 00:27:35,290 --> 00:27:37,710 Y-axis specific or excess heat capacity, 486 00:27:37,710 --> 00:27:39,780 and this is temperature. 487 00:27:39,780 --> 00:27:43,910 And after normalization we'll get a curve like that. 488 00:27:43,910 --> 00:27:46,950 In that curve, on top here 489 00:27:48,590 --> 00:27:49,940 the peak height which you can see 490 00:27:49,940 --> 00:27:51,740 the melting temperature right there. 491 00:27:52,930 --> 00:27:55,900 And that melting temperature here is essentially 492 00:27:57,450 --> 00:28:02,450 a ratio of the enthalpy forces divided by entropic forces. 493 00:28:03,530 --> 00:28:07,010 We put the word cal to mean it's from calorimeter 494 00:28:07,010 --> 00:28:09,860 just being fancy here. 495 00:28:09,860 --> 00:28:11,904 The cal is colorimeter. 496 00:28:11,904 --> 00:28:13,010 Tm is the middle temperature, 497 00:28:13,010 --> 00:28:15,010 it gives us information about stability. 498 00:28:16,310 --> 00:28:18,900 Delta H is essentially 499 00:28:20,960 --> 00:28:23,280 this area under this curve, 500 00:28:23,280 --> 00:28:24,380 area under this curve. 501 00:28:24,380 --> 00:28:26,510 So, delta H over here, 502 00:28:26,510 --> 00:28:29,523 is actually an integral of specifically heat capacity 503 00:28:29,523 --> 00:28:31,437 and (mumbles) under this curve. 504 00:28:31,437 --> 00:28:36,437 And delta S is also an integral for that. 505 00:28:36,830 --> 00:28:39,980 Once we get these values, we have to calculate this. 506 00:28:39,980 --> 00:28:43,810 You have to physically calculate, you need to calculate. 507 00:28:46,140 --> 00:28:48,230 And the good part about this 508 00:28:48,230 --> 00:28:50,847 is you can change to the temperature you want 509 00:28:50,847 --> 00:28:55,847 for biological process you want, okay? 510 00:28:56,040 --> 00:28:59,137 So, that's our stuff right there. 511 00:28:59,137 --> 00:29:01,853 And I'm going to dig straight into the results. 512 00:29:02,956 --> 00:29:04,120 I'm going to dig straight into the results 513 00:29:04,120 --> 00:29:07,631 in the next 10 to 15 minutes as such. 514 00:29:07,631 --> 00:29:08,870 So, I am going to show you the results 515 00:29:08,870 --> 00:29:10,500 and there'll be as follows. 516 00:29:10,500 --> 00:29:15,500 First, I'll show you how HSA and plasma curves appear. 517 00:29:18,280 --> 00:29:21,100 Next, I'll show you how they get changed 518 00:29:21,100 --> 00:29:23,873 when you bind them to some medications. 519 00:29:27,000 --> 00:29:30,192 After that, I'll tell you how HSA binds to biotin. 520 00:29:30,192 --> 00:29:34,040 And I'll show you how this technology can be used 521 00:29:34,040 --> 00:29:36,400 to detect some diseases. 522 00:29:36,400 --> 00:29:39,103 And with time and maybe a game changer. 523 00:29:41,900 --> 00:29:42,733 Okay. 524 00:29:42,733 --> 00:29:44,100 I've already shown this. 525 00:29:44,100 --> 00:29:48,650 So, my topic is, "Biochemical signatures." 526 00:29:48,650 --> 00:29:52,410 It's essentially graphical signatures 527 00:29:52,410 --> 00:29:54,740 that get repeated over and over again. 528 00:29:54,740 --> 00:29:56,530 Then we can use as biomarkers. 529 00:29:56,530 --> 00:30:01,530 So what you see here is HSA signature, human serum albumin. 530 00:30:02,562 --> 00:30:04,483 Human serum albumin, subsidiarity to DSC, 531 00:30:05,930 --> 00:30:07,640 you normalize the curve. 532 00:30:07,640 --> 00:30:10,500 You're going to get this small peak 533 00:30:10,500 --> 00:30:12,080 with a melting temperature 534 00:30:13,683 --> 00:30:16,943 right there around 63.5. 535 00:30:19,680 --> 00:30:24,650 If it's no more HSA, you are not going to get that curve, 536 00:30:25,610 --> 00:30:27,530 you are not going to get that curve. 537 00:30:27,530 --> 00:30:31,620 So that's the first holy grail 538 00:30:31,620 --> 00:30:32,650 of HSA right there 539 00:30:32,650 --> 00:30:33,543 as you have seen. 540 00:30:34,840 --> 00:30:37,812 Just as a matter of comparison 541 00:30:37,812 --> 00:30:40,160 what you see here is three graphs. 542 00:30:40,160 --> 00:30:44,107 On the left, is the signature or pattern HSA. 543 00:30:46,700 --> 00:30:50,780 I also took some protein that I talked about 544 00:30:50,780 --> 00:30:52,230 that it's called transferrin. 545 00:30:56,330 --> 00:30:59,930 And we ran it normal transferrin. 546 00:30:59,930 --> 00:31:02,000 It has its own signature. 547 00:31:02,000 --> 00:31:03,570 As you can notice, 548 00:31:03,570 --> 00:31:08,570 there's a little peak at around 60 degrees right there. 549 00:31:08,930 --> 00:31:13,530 And another major pick at around 75 degrees. 550 00:31:13,530 --> 00:31:17,120 And it doesn't matter if you take transferrin, 551 00:31:17,120 --> 00:31:20,860 ran it on DSC, it doesn't matter what type of DSC you get, 552 00:31:20,860 --> 00:31:22,620 you're going to get that signature. 553 00:31:22,620 --> 00:31:26,310 You will also see some certain structures here 554 00:31:26,310 --> 00:31:29,940 which is some proteins, but we don't know yet. 555 00:31:29,940 --> 00:31:31,990 It could be some contamination in there 556 00:31:31,990 --> 00:31:36,753 but it could be some binding that we are yet to figure out. 557 00:31:37,760 --> 00:31:39,360 This is another comparison. 558 00:31:39,360 --> 00:31:42,540 The whole plasma, which is a combination 559 00:31:42,540 --> 00:31:45,680 of all these proteins and many more. 560 00:31:45,680 --> 00:31:47,040 If you notice, this thermogram here 561 00:31:48,290 --> 00:31:53,163 you can see that there is abundant HSA here. 562 00:31:54,230 --> 00:31:56,980 But you'll start seeing other things that are in there. 563 00:31:58,210 --> 00:32:00,600 I'm going to show you in the next slide 564 00:32:00,600 --> 00:32:05,403 the most abundant proteins that we can get in plasma. 565 00:32:06,570 --> 00:32:09,160 And this is some published data 566 00:32:09,160 --> 00:32:11,700 that you can go to this website here. 567 00:32:12,753 --> 00:32:16,863 This is just a summation of how plasma, normal plasma 568 00:32:16,863 --> 00:32:19,060 when we say normal, 569 00:32:19,060 --> 00:32:24,060 we mean they've been screened of certain diseases. 570 00:32:24,440 --> 00:32:25,790 They've been screened on certain disease. 571 00:32:25,790 --> 00:32:29,030 You can notice here, the dark stuff here 572 00:32:29,030 --> 00:32:33,400 it means these experiments were done over and over again. 573 00:32:33,400 --> 00:32:35,180 And these are just averages. 574 00:32:35,180 --> 00:32:39,040 And this is a real patient about 15 individuals, 575 00:32:39,040 --> 00:32:42,740 nine males, six females ages 22 to 50. 576 00:32:42,740 --> 00:32:45,510 And it's so consistent there is a peak right there, 577 00:32:45,510 --> 00:32:46,915 and there's a peak right there, 578 00:32:46,915 --> 00:32:47,748 there's a peak right there 579 00:32:47,748 --> 00:32:50,710 and there's other stuff right there, that's plasma. 580 00:32:50,710 --> 00:32:55,710 And we were also, this paper was also able to go in here 581 00:32:55,820 --> 00:32:57,470 and figure out what is here. 582 00:32:57,470 --> 00:32:59,520 This is (mumbles) fibrinogen right there. 583 00:33:00,921 --> 00:33:03,030 This is a HSA right there. 584 00:33:03,030 --> 00:33:04,430 Since this is all plasma 585 00:33:04,430 --> 00:33:07,758 there's also other proteins like haptoglobin right there. 586 00:33:07,758 --> 00:33:12,210 IGG, LGG and LGA are also right there. 587 00:33:12,210 --> 00:33:16,420 And some transferrin right at the end 588 00:33:16,420 --> 00:33:20,180 but we can ran the transferrin alone, 589 00:33:20,180 --> 00:33:22,040 we can ran the fibrinogen alone 590 00:33:22,040 --> 00:33:24,260 I think I've already shown you that. 591 00:33:24,260 --> 00:33:27,980 Now, this is what I'm talking about. 592 00:33:27,980 --> 00:33:31,330 These are patterns that are repeated over and over again 593 00:33:31,330 --> 00:33:34,610 and I decided to call them biochemical signatures 594 00:33:34,610 --> 00:33:36,390 for specific diseases. 595 00:33:36,390 --> 00:33:38,735 Plasma and HSA in medication. 596 00:33:38,735 --> 00:33:43,735 So, we are able to go in and take some common medications 597 00:33:44,630 --> 00:33:46,600 and other unknown medication. 598 00:33:46,600 --> 00:33:48,740 Doesn't see if we can see patterns here. 599 00:33:48,740 --> 00:33:51,592 So, we did that for that 600 00:33:51,592 --> 00:33:54,440 and many more 601 00:33:54,440 --> 00:33:57,070 but I'm just going to show you for one or two right here 602 00:33:57,070 --> 00:33:58,270 in the interest of time. 603 00:33:59,670 --> 00:34:04,670 So, what you see here is where we will bind 604 00:34:05,550 --> 00:34:08,503 I'm gonna start with graph A here. 605 00:34:09,500 --> 00:34:11,570 This is whole plasma, 606 00:34:11,570 --> 00:34:14,600 we now know how whole plasma looks like. 607 00:34:14,600 --> 00:34:17,203 In black here is plasma alone. 608 00:34:18,460 --> 00:34:20,180 That's the curve. 609 00:34:20,180 --> 00:34:25,180 And in red here, this is when we bind plasm to naproxen. 610 00:34:25,850 --> 00:34:27,280 You can see a shift 611 00:34:29,272 --> 00:34:31,422 a significant shift in the thermogram here. 612 00:34:32,970 --> 00:34:37,849 And we can use that to identify these medications. 613 00:34:37,849 --> 00:34:41,016 And on the B right here you can notice 614 00:34:42,750 --> 00:34:46,639 HSA alone which is this nice peak reproducible 615 00:34:46,639 --> 00:34:50,018 and then bind it to naproxen here. 616 00:34:50,018 --> 00:34:53,870 We can see a significant shift in melting temperature 617 00:34:53,870 --> 00:34:56,077 and this is repeated over and over again. 618 00:34:56,077 --> 00:34:59,533 It means that you can use it to identify these things. 619 00:34:59,533 --> 00:35:01,624 So we're gonna shift the thermograms 620 00:35:01,624 --> 00:35:02,970 this is a biochemical signature 621 00:35:02,970 --> 00:35:05,310 so that's what I'm talking about here. 622 00:35:05,310 --> 00:35:08,530 Shifts in thermograms and we can measure them. 623 00:35:08,530 --> 00:35:10,443 You can read more about this in the publication 624 00:35:10,443 --> 00:35:12,083 that is posted online. 625 00:35:13,610 --> 00:35:16,120 I was able to work with Angelina Calnan, 626 00:35:16,120 --> 00:35:17,460 I talked to you about her earlier. 627 00:35:17,460 --> 00:35:20,093 And she did couple of work on, 628 00:35:21,110 --> 00:35:22,670 as you can now see here 629 00:35:22,670 --> 00:35:25,428 this is just an example of her work here. 630 00:35:25,428 --> 00:35:28,710 She was able to observe that 631 00:35:28,710 --> 00:35:32,550 and a significant shift 632 00:35:33,640 --> 00:35:35,460 to put this medication. 633 00:35:35,460 --> 00:35:36,680 We are yet to publish this 634 00:35:36,680 --> 00:35:38,580 but she was able to present this work 635 00:35:38,580 --> 00:35:42,200 at American Chemical Society in 2018 636 00:35:42,200 --> 00:35:44,580 and that was a phenomenon. 637 00:35:44,580 --> 00:35:47,810 If any students wanted to work on this, 638 00:35:47,810 --> 00:35:50,833 find me and follow, we can get you involved. 639 00:35:51,840 --> 00:35:55,340 Okay, this is another work under construction 640 00:35:58,520 --> 00:36:03,520 where we have started binding some unknown medications 641 00:36:04,850 --> 00:36:08,710 to these plasma in black normal 642 00:36:08,710 --> 00:36:11,360 and we can see some significant shift here, 643 00:36:11,360 --> 00:36:15,040 this is one-to-one titration with a certain medication. 644 00:36:15,040 --> 00:36:16,983 We will be working more on that. 645 00:36:18,268 --> 00:36:20,370 I am going to rush a little bit here. 646 00:36:20,370 --> 00:36:22,963 We did some experiments with the biotin. 647 00:36:23,840 --> 00:36:27,680 As you noticed, biotin is a form of vitamin B. 648 00:36:27,680 --> 00:36:30,367 We had sort of a forms of vitamin B over there. 649 00:36:30,367 --> 00:36:31,417 But most importantly, 650 00:36:32,540 --> 00:36:35,730 if you have taken a little bit of advanced bio 651 00:36:35,730 --> 00:36:39,130 it is a core factor responsible for carbon dioxide transfer 652 00:36:39,130 --> 00:36:43,110 in processes like gluconeogenesis 653 00:36:44,320 --> 00:36:46,706 process called oxidative decarboxylation. 654 00:36:46,706 --> 00:36:51,190 So, what we did here is we decided to investigate attachment 655 00:36:51,190 --> 00:36:54,700 of biotin to HSA and plasma. 656 00:36:54,700 --> 00:36:57,760 And this process is called a biotylation 657 00:36:57,760 --> 00:36:59,810 when you attach biotin to that 658 00:36:59,810 --> 00:37:02,307 we get a fancy term, biotinylation. 659 00:37:02,307 --> 00:37:06,500 And I'm going to quickly go over what was done. 660 00:37:06,500 --> 00:37:11,250 So we, got two commercially available preparations of HSA 661 00:37:14,393 --> 00:37:19,330 one was 99% pure and other was a 96%, 662 00:37:19,330 --> 00:37:20,920 but 3% difference is mostly 663 00:37:20,920 --> 00:37:23,960 due to globulins and fatty acids. 664 00:37:23,960 --> 00:37:28,960 We wanted to see DSC can be able to descend these changes. 665 00:37:31,010 --> 00:37:34,100 And this is a multiple binding sites 666 00:37:34,100 --> 00:37:35,667 and we were able to, 667 00:37:37,740 --> 00:37:39,830 to do some work on this 668 00:37:39,830 --> 00:37:41,210 and I'll briefly go over this. 669 00:37:41,210 --> 00:37:46,210 And, when you see the red arrow it means 670 00:37:46,680 --> 00:37:49,400 we're talking about the most pure form of HSA 671 00:37:49,400 --> 00:37:53,870 and green in the 96%, 672 00:37:53,870 --> 00:37:56,562 I guess no 96% is all the fatty acids 673 00:37:56,562 --> 00:37:57,862 and globulins right there. 674 00:37:58,700 --> 00:38:02,120 And they're few housekeeking rules 675 00:38:02,120 --> 00:38:05,870 on binding, on HSA. 676 00:38:05,870 --> 00:38:08,043 We have what we call single site binding. 677 00:38:09,270 --> 00:38:13,430 We can go on and pick a site where 678 00:38:15,910 --> 00:38:18,630 we know we just blogged one site here. 679 00:38:18,630 --> 00:38:20,510 And then under neutral condition we found that 680 00:38:20,510 --> 00:38:23,720 there's only one reduced sulfhydryl group 681 00:38:23,720 --> 00:38:25,163 on cysteine number 34. 682 00:38:26,190 --> 00:38:31,190 And we buy this link from Thermo-Fisher Scientific. 683 00:38:31,360 --> 00:38:34,920 It comes with instructions on how to block one site. 684 00:38:34,920 --> 00:38:38,430 Similarly, we can do multiple site binding. 685 00:38:38,430 --> 00:38:39,633 And in this one, 686 00:38:40,990 --> 00:38:43,920 this link here, the EZ of sulfo-NHS link 687 00:38:43,920 --> 00:38:45,260 from Thermo-Fisher, 688 00:38:45,260 --> 00:38:49,013 it targets about 59 lysine residues. 689 00:38:50,760 --> 00:38:54,010 And literature shows that about 20 or so are exposed. 690 00:38:54,010 --> 00:38:56,344 So, we just gonna unblock them. 691 00:38:56,344 --> 00:38:58,390 So these are different kits to block. 692 00:38:58,390 --> 00:39:03,390 So we have a way to investigate single site binding 693 00:39:03,570 --> 00:39:06,120 verses multiple binding. 694 00:39:06,120 --> 00:39:09,963 And here's the data that we'll got initially here. 695 00:39:11,068 --> 00:39:14,787 As you notice here, 696 00:39:14,787 --> 00:39:16,480 HSA 99 is the most pure one, 697 00:39:16,480 --> 00:39:20,150 which is nice curve reproducible 698 00:39:20,150 --> 00:39:22,340 with little error biases for many experiments 699 00:39:22,340 --> 00:39:23,370 that were done 700 00:39:23,370 --> 00:39:27,300 and they all came to be within a certain margin of error. 701 00:39:27,300 --> 00:39:30,450 I wanna skip this one in dotted lines a little bit 702 00:39:30,450 --> 00:39:32,090 then I'm gonna talk about this one. 703 00:39:32,090 --> 00:39:35,682 In which we have so many other things the dirty one, 704 00:39:35,682 --> 00:39:38,897 the HSA 96 with a lot of blends and (indistinct). 705 00:39:38,897 --> 00:39:40,580 We can see there is the melting is a little bit further 706 00:39:44,090 --> 00:39:47,580 and we've got two clear signs right there. 707 00:39:47,580 --> 00:39:52,580 And we call this a bi-phasic melting transition. 708 00:39:52,600 --> 00:39:56,440 The dashed thermogram is when we decided 709 00:39:56,440 --> 00:40:01,270 after a year or so to remelt this. 710 00:40:01,270 --> 00:40:05,693 And we figured it out in condense into the stable form 711 00:40:06,630 --> 00:40:07,630 that was surprising. 712 00:40:08,520 --> 00:40:13,040 So that's what we're talking about right here, okay? 713 00:40:13,040 --> 00:40:15,963 And then I talk about bi-phasic melting transition. 714 00:40:17,637 --> 00:40:18,950 All right. 715 00:40:18,950 --> 00:40:21,530 Next, we did single site binding, 716 00:40:21,530 --> 00:40:23,833 as you noticed this is a normal HSA. 717 00:40:24,790 --> 00:40:26,980 When I bind it to biotin 718 00:40:26,980 --> 00:40:29,100 you can see a shift in thermogram. 719 00:40:29,100 --> 00:40:32,807 Not only that there's a drop in this peak 720 00:40:36,710 --> 00:40:39,670 and we calculated all the binding constant, 721 00:40:39,670 --> 00:40:43,373 the thermodynamics and it's all listed in the paper. 722 00:40:44,480 --> 00:40:47,240 We have different ones here meaning we were titrating 723 00:40:47,240 --> 00:40:51,459 like one to one Biotin one to say, 20 ratio molar, 724 00:40:51,459 --> 00:40:52,877 ratio (indistinct). 725 00:40:54,470 --> 00:40:59,420 So, we don't see like significant changes when we titrate, 726 00:40:59,420 --> 00:41:02,560 but we can see a significant shift right there. 727 00:41:02,560 --> 00:41:05,943 And if we do that with HSA 96, 728 00:41:08,090 --> 00:41:09,980 which is the one on the globulins, 729 00:41:09,980 --> 00:41:13,980 we have a clear one right here. 730 00:41:13,980 --> 00:41:16,030 And once you attach biotin you can notice 731 00:41:16,940 --> 00:41:19,350 this peak and this peak kind of disappear 732 00:41:19,350 --> 00:41:21,740 and we wanna get to one single peak here. 733 00:41:21,740 --> 00:41:26,083 And this is, we suspect is a, you do some aggregation. 734 00:41:26,970 --> 00:41:29,240 That'll get to talk about later. 735 00:41:29,240 --> 00:41:31,570 So that's a major differences here. 736 00:41:31,570 --> 00:41:36,200 It's consolidation of peaks in a less stable form. 737 00:41:36,200 --> 00:41:38,740 And then that's it right there. 738 00:41:38,740 --> 00:41:41,700 And we went on to do multiple site binding. 739 00:41:41,700 --> 00:41:43,240 And in the interest of time 740 00:41:43,240 --> 00:41:45,880 I'm just gonna show you the pure one. 741 00:41:45,880 --> 00:41:48,890 This is normal HSA binding curve 742 00:41:50,460 --> 00:41:54,690 and these are titrations from lower molarity so 743 00:41:56,060 --> 00:41:58,781 these are titrations from lower molarity here, 744 00:41:58,781 --> 00:41:59,614 we see a shift. 745 00:42:00,680 --> 00:42:04,690 We increase the molality to see a different shift 746 00:42:04,690 --> 00:42:07,700 and it keeps on going until saturation. 747 00:42:07,700 --> 00:42:12,540 So, we actually see significant differences 748 00:42:12,540 --> 00:42:17,340 in the attachment of biotin and the saturation curves. 749 00:42:17,340 --> 00:42:19,400 But home message here is 750 00:42:19,400 --> 00:42:22,830 we see significant temperature shifts 751 00:42:22,830 --> 00:42:26,343 for the most stable form or the most pure form of HSA. 752 00:42:31,250 --> 00:42:33,000 In the next three minutes or so, 753 00:42:33,000 --> 00:42:37,140 I am gonna talk about how HSA binding 754 00:42:37,140 --> 00:42:39,570 affects other diseases. 755 00:42:39,570 --> 00:42:43,130 So, before I proceed the tickle message here 756 00:42:43,130 --> 00:42:45,407 will be the binding of many drugs 757 00:42:45,407 --> 00:42:48,293 the HSA patients can be changed in disease states. 758 00:42:49,340 --> 00:42:54,140 So, just because this is work in progress 759 00:42:54,140 --> 00:42:56,560 I'm going to give you some work done 760 00:42:56,560 --> 00:43:00,210 by my colleagues at University of Louisville here. 761 00:43:02,030 --> 00:43:04,350 And the topic is about, "Biochemical signatures." 762 00:43:04,350 --> 00:43:06,510 So these are real patients here. 763 00:43:06,510 --> 00:43:08,463 So they took patients. 764 00:43:09,680 --> 00:43:14,450 I'm gonna start with the left here. 765 00:43:14,450 --> 00:43:19,307 This is a curve normalized for plasma 766 00:43:22,910 --> 00:43:27,910 with so many rounds done on real patients here. 767 00:43:28,530 --> 00:43:33,090 This is normal, meaning there've been screened 768 00:43:33,090 --> 00:43:35,430 of rheumatoid arthritis. 769 00:43:35,430 --> 00:43:37,430 And then in red here 770 00:43:37,430 --> 00:43:40,200 these are patients who are screened rheumatoid arthritis 771 00:43:40,200 --> 00:43:43,610 and you can see significant difference here. 772 00:43:43,610 --> 00:43:45,620 Lyme disease, same thing. 773 00:43:45,620 --> 00:43:50,150 This is a normal patients not suffering 774 00:43:50,150 --> 00:43:51,850 from that disease screened. 775 00:43:51,850 --> 00:43:53,700 You can see a significant shift here. 776 00:43:55,110 --> 00:43:55,943 When rhyme, 777 00:43:57,953 --> 00:44:02,570 this on HSA patients we know have this disease. 778 00:44:02,570 --> 00:44:05,960 Lupus, same thing is auto-immune diseases right here. 779 00:44:05,960 --> 00:44:09,500 We can see some, some shifts here in thermograms. 780 00:44:09,500 --> 00:44:12,120 And on the right here is this thing put together 781 00:44:12,120 --> 00:44:13,823 in this publication here, 782 00:44:14,700 --> 00:44:16,870 we can read more about this. 783 00:44:16,870 --> 00:44:18,400 So, you can see here 784 00:44:19,910 --> 00:44:22,843 that this is the normal plasma right here, 785 00:44:24,160 --> 00:44:27,030 and you can see some significant shifts 786 00:44:28,060 --> 00:44:29,943 in specific diseases. 787 00:44:31,050 --> 00:44:34,487 Blue here, rheumatoid arthretis, the lyme right there, 788 00:44:34,487 --> 00:44:35,790 the lupus. 789 00:44:35,790 --> 00:44:38,940 We have done this over and over again. 790 00:44:38,940 --> 00:44:43,310 And right now I'm working with some melanoma 791 00:44:44,420 --> 00:44:48,830 some tabs of ovarian cancer patient and diabetic. 792 00:44:48,830 --> 00:44:52,720 We get these patients already screened maybe 793 00:44:52,720 --> 00:44:57,163 from Mayo clinic and all these other places. 794 00:44:58,820 --> 00:45:03,530 And it will be interesting that we can 795 00:45:03,530 --> 00:45:06,453 get some reliable biomarkers right there. 796 00:45:08,927 --> 00:45:10,570 I would like to thank these awesome people 797 00:45:10,570 --> 00:45:12,920 for letting me speak. 798 00:45:12,920 --> 00:45:15,270 First my sponsor Chemistry and Physics Department 799 00:45:15,270 --> 00:45:18,780 and Charles Albert Read Trust. 800 00:45:18,780 --> 00:45:23,660 And my colleagues (indistinct) 801 00:45:23,660 --> 00:45:26,337 Biology and Department for the Invitation 802 00:45:27,540 --> 00:45:29,860 Past students I just mentioned a few 803 00:45:30,980 --> 00:45:35,980 but you're also welcome all collaborators and consultants 804 00:45:38,470 --> 00:45:42,283 mostly at Beverly right there. 805 00:45:44,660 --> 00:45:46,397 And professor Albert S. Benight 806 00:45:46,397 --> 00:45:48,863 and some colleagues at University of Louisville. 807 00:45:51,250 --> 00:45:53,300 What is the take home message here? 808 00:45:53,300 --> 00:45:54,133 Last minute. 809 00:45:54,133 --> 00:45:54,966 Number one, 810 00:45:54,966 --> 00:45:58,760 I told you that plasma and HSA produced thermograms 811 00:45:59,773 --> 00:46:03,890 and thermograms can be used to show us some disease states. 812 00:46:03,890 --> 00:46:04,723 Number two, 813 00:46:07,945 --> 00:46:09,820 surely this technology has potential 814 00:46:09,820 --> 00:46:13,060 to screen new disease candidates, drug candidates 815 00:46:13,060 --> 00:46:15,203 the new and old ones. 816 00:46:17,040 --> 00:46:19,170 My goal in the long term is number three. 817 00:46:19,170 --> 00:46:22,397 I need to create a drug binding library with (indistinct). 818 00:46:23,880 --> 00:46:27,690 We can actually predict other drugs that are gonna be made 819 00:46:27,690 --> 00:46:29,470 whether their behaviors then I mentioned 820 00:46:29,470 --> 00:46:31,560 some of the drugs that we already know. 821 00:46:31,560 --> 00:46:34,250 That's the goal, number three. 822 00:46:34,250 --> 00:46:35,860 I already showed you too 823 00:46:35,860 --> 00:46:39,040 that we can use this technology to distinguish 824 00:46:39,040 --> 00:46:43,050 between disease states and non-disease states. 825 00:46:43,050 --> 00:46:45,233 And number five. 826 00:46:46,570 --> 00:46:50,143 I told you about biochemical signatures today. 827 00:46:51,000 --> 00:46:51,833 Thank you. 828 00:46:55,732 --> 00:46:57,899 (mumbles) 829 00:46:59,720 --> 00:47:04,290 - Thank you very much Dr. Manyanga for your wonderful talk. 830 00:47:04,290 --> 00:47:05,973 Thank you for informing us. 831 00:47:07,010 --> 00:47:12,010 And at this time myself, I'm Dr. Gordon from biology 832 00:47:14,130 --> 00:47:18,380 and my colleague, Dr. Nelson Scottgale 833 00:47:18,380 --> 00:47:21,370 will help to answer, to provide some of the questions 834 00:47:21,370 --> 00:47:24,200 that our audience have submitted. 835 00:47:24,200 --> 00:47:27,810 So, I'm gonna go directly to the first question 836 00:47:27,810 --> 00:47:32,020 which is, what is indicated by the shift 837 00:47:32,020 --> 00:47:36,850 in the signature noted from the thermograms with HSA alone 838 00:47:36,850 --> 00:47:39,770 versus the increase in temperature signature 839 00:47:39,770 --> 00:47:43,033 when SHA was combined with naproxen? 840 00:47:44,950 --> 00:47:45,783 - Okay. 841 00:47:45,783 --> 00:47:48,020 Let me see if I can go to that slide. 842 00:47:48,020 --> 00:47:49,786 Can I go to the slide? 843 00:47:49,786 --> 00:47:52,620 Let me see if I can do that. 844 00:47:55,166 --> 00:47:57,012 (mumbles) 845 00:47:57,012 --> 00:47:57,845 Okay. 846 00:47:59,459 --> 00:48:01,900 I'm going to the slide of naproxen 847 00:48:01,900 --> 00:48:04,900 and I'm gonna ask you to repeat that a little bit. 848 00:48:04,900 --> 00:48:08,050 I did not get the last part,(mumbles) 849 00:48:08,050 --> 00:48:12,410 - Okay, the question was, 850 00:48:12,410 --> 00:48:16,140 what is indicated by the shift in the signature noted 851 00:48:16,140 --> 00:48:19,490 from the thermograms with HSA alone 852 00:48:19,490 --> 00:48:22,600 versus the increase in temperature signature 853 00:48:22,600 --> 00:48:26,910 when HSA was combined with naproxen? 854 00:48:26,910 --> 00:48:28,680 - Okay so, what we are doing here, 855 00:48:28,680 --> 00:48:31,380 we're just trying to see 856 00:48:33,640 --> 00:48:37,780 how the stability of medications 857 00:48:38,700 --> 00:48:42,700 first the stability of medications affect HSA. 858 00:48:42,700 --> 00:48:46,500 Because this information with this information 859 00:48:46,500 --> 00:48:51,500 if I know there's a significant binding for naproxen to HSA, 860 00:48:54,570 --> 00:48:59,040 it will change the way I'm going to administer 861 00:48:59,040 --> 00:49:02,640 this naproxen because remember these drugs, 862 00:49:02,640 --> 00:49:07,640 they have to go get in the systemic circulation. 863 00:49:07,970 --> 00:49:11,750 So, if I'm predicting this drug is going 864 00:49:11,750 --> 00:49:13,963 to be bound a lot by HSA 865 00:49:14,947 --> 00:49:18,210 it means it's gonna affect its bioavailability. 866 00:49:18,210 --> 00:49:22,310 I have to either a more defined 867 00:49:22,310 --> 00:49:25,630 the way I'm going to administer it or change the molarities, 868 00:49:25,630 --> 00:49:28,000 knowing very well that I have to factor in 869 00:49:28,000 --> 00:49:31,290 that there's some strong binding. 870 00:49:31,290 --> 00:49:36,100 So, a change in that shift it just tells me 871 00:49:36,100 --> 00:49:39,303 that there is some significant binding. 872 00:49:44,130 --> 00:49:45,170 - Thank you Dr. Manyanga 873 00:49:45,170 --> 00:49:50,170 and I believe Dr. Nelson Scottsdale has the next question. 874 00:49:52,750 --> 00:49:53,583 - [Dr. Nelson Scottsgale] Thank you for 875 00:49:53,583 --> 00:49:56,140 a very interesting talk, Dr. Manyanga. 876 00:49:56,140 --> 00:49:59,630 The next question has to do with 877 00:49:59,630 --> 00:50:01,760 or came in at about the time when you were talking 878 00:50:01,760 --> 00:50:04,060 about biotin and it says, 879 00:50:04,060 --> 00:50:05,960 can this be done with any vitamin 880 00:50:07,830 --> 00:50:12,830 - The binding, if the vitamin binds to HSA, 881 00:50:15,150 --> 00:50:19,300 yes, I believe there's a lot of literature on binding 882 00:50:19,300 --> 00:50:23,660 of vitamin X to HSA or plasma. 883 00:50:23,660 --> 00:50:26,520 They should be a lot of literature that is 884 00:50:26,520 --> 00:50:27,993 if there is binding. 885 00:50:28,870 --> 00:50:33,870 But if HSA is responsible for transporting 886 00:50:34,260 --> 00:50:36,840 those vitamins, yes. 887 00:50:36,840 --> 00:50:41,840 You can even find the thermodynamics on the curves. 888 00:50:43,720 --> 00:50:46,440 But I think a quick Google say 889 00:50:46,440 --> 00:50:50,193 to somebody should have figured it out already by now. 890 00:50:53,000 --> 00:50:53,833 Okay. 891 00:50:56,400 --> 00:50:59,083 - Well, I'm going to ask the next question. 892 00:51:00,190 --> 00:51:04,870 Is there any theory about the mechanism causing the shifts 893 00:51:04,870 --> 00:51:06,853 with any of these diseases? 894 00:51:07,980 --> 00:51:10,030 - Yes, that's the problem. 895 00:51:10,030 --> 00:51:12,270 That's the problem we have. 896 00:51:12,270 --> 00:51:16,733 So, if you notice the plasma protein is over 3000 897 00:51:17,670 --> 00:51:18,763 in visual protein. 898 00:51:19,890 --> 00:51:23,010 So right now, when the DSC 899 00:51:23,010 --> 00:51:25,420 because today I focus on DSC alone, 900 00:51:25,420 --> 00:51:30,360 but with the DSC that's actually like the fist point, 901 00:51:30,360 --> 00:51:33,123 just to see HSA some binding here, 902 00:51:34,000 --> 00:51:38,310 but we are not really sure on what exactly 903 00:51:38,310 --> 00:51:40,230 is causing them binding , 904 00:51:40,230 --> 00:51:42,740 where exactly is gonna cause. 905 00:51:42,740 --> 00:51:47,207 Some people have done isothermal titration, calorimeters 906 00:51:48,110 --> 00:51:52,790 they can, you can see in real time as this happens. 907 00:51:52,790 --> 00:51:57,790 Some people have done what we'll call plasma depletion, 908 00:51:57,970 --> 00:52:00,690 you can do depletion studies, 909 00:52:00,690 --> 00:52:02,510 which means you can take plasma 910 00:52:04,080 --> 00:52:06,970 and try to remove everything else. 911 00:52:06,970 --> 00:52:09,380 And then just ran one by one, 912 00:52:09,380 --> 00:52:14,380 just to see if there's if those changes reproducible. 913 00:52:16,720 --> 00:52:21,720 But yes, the DSC alone is just like a first point, 914 00:52:22,400 --> 00:52:25,090 but we don't know exactly 915 00:52:25,090 --> 00:52:28,970 how these interactions are happening. 916 00:52:28,970 --> 00:52:32,820 And that's one of the reasons why it's very hard 917 00:52:32,820 --> 00:52:37,010 at this point to get FDA approval 918 00:52:37,010 --> 00:52:39,880 for diagnosis with this one because 919 00:52:40,770 --> 00:52:42,640 we cannot pinpoint like the real 920 00:52:42,640 --> 00:52:44,593 the exact mechanism right now. 921 00:52:45,760 --> 00:52:49,480 Some people have done NMO 922 00:52:51,770 --> 00:52:53,220 to a reasonable degree 923 00:52:53,220 --> 00:52:57,430 but honestly we just really don't know 924 00:52:57,430 --> 00:52:59,663 the mechanism right its work in progress. 925 00:53:02,800 --> 00:53:07,240 - The next question is it relates in some ways, 926 00:53:07,240 --> 00:53:10,933 do you know why the different diseases cause the shifts? 927 00:53:12,820 --> 00:53:16,940 - Well, like I said, this is just kind of a biomarker level 928 00:53:16,940 --> 00:53:21,525 we just accidentally saw there is a shift on this disease 929 00:53:21,525 --> 00:53:22,870 there is a shift on this disease. 930 00:53:22,870 --> 00:53:24,167 But the problem is 931 00:53:24,167 --> 00:53:29,167 the major problem we outlined in the previous paper was 932 00:53:32,510 --> 00:53:35,780 we don't know like, yes you have been suppose I'm looking 933 00:53:35,780 --> 00:53:39,902 at someone with a rheumatoid arthritis, 934 00:53:39,902 --> 00:53:44,350 they have been screened, not to have rheumatoid arthritis 935 00:53:44,350 --> 00:53:46,123 and this normal plasma here. 936 00:53:47,060 --> 00:53:48,640 That person has been screened 937 00:53:48,640 --> 00:53:50,250 and did not to have rheumatoid arthritis 938 00:53:50,250 --> 00:53:52,805 may be suffering from other disease 939 00:53:52,805 --> 00:53:54,743 (laughs) that we don't know. 940 00:53:56,130 --> 00:54:01,130 So, so far we are just able to say, 941 00:54:02,783 --> 00:54:04,437 "(indistinct) if this rheumatoid arthritis", 942 00:54:04,437 --> 00:54:05,887 "we are seeing this pattern". 943 00:54:06,797 --> 00:54:09,700 "If there's early, we are seeing this pattern". 944 00:54:09,700 --> 00:54:11,080 And the utility of this, 945 00:54:11,080 --> 00:54:13,383 I know there's one company trying 946 00:54:13,383 --> 00:54:15,610 to take this to another level. 947 00:54:15,610 --> 00:54:17,003 The utility of this is, 948 00:54:18,406 --> 00:54:22,003 this would be the dream, maybe 10 to 20 years from now. 949 00:54:23,170 --> 00:54:27,333 There could be a way people can find these patterns 950 00:54:29,860 --> 00:54:33,300 in so many diseases that we can do 951 00:54:33,300 --> 00:54:34,813 what we call multiplexing. 952 00:54:35,840 --> 00:54:40,310 Like on my wish list, suppose I go to my doctor's office 953 00:54:40,310 --> 00:54:41,960 for my annual exam, 954 00:54:41,960 --> 00:54:43,483 and there's a tool like this. 955 00:54:44,660 --> 00:54:49,660 If I can ran a blood sample for one hour 956 00:54:51,000 --> 00:54:54,520 that tells me different shifts. 957 00:54:54,520 --> 00:54:56,140 Yes, I'll be worried in a such 958 00:54:56,140 --> 00:54:57,730 I am worrying about my health right away 959 00:54:57,730 --> 00:55:01,280 and then I start going from one doctor to another 960 00:55:01,280 --> 00:55:05,290 I just know there's something wrong my plasma is not normal. 961 00:55:05,290 --> 00:55:08,870 So we can detect multiple things long-term 962 00:55:08,870 --> 00:55:11,430 but so far we are not there. 963 00:55:11,430 --> 00:55:14,560 But I just hope 964 00:55:14,560 --> 00:55:18,900 we should be able to combine these shifts 965 00:55:18,900 --> 00:55:22,940 in disease states and do multiplexing. 966 00:55:22,940 --> 00:55:27,590 And honestly, if I'm suffering from this disease 967 00:55:27,590 --> 00:55:29,680 and I get another shift on that 968 00:55:29,680 --> 00:55:32,090 it would tell me to make a better decision 969 00:55:32,090 --> 00:55:35,740 to get go and get screened on those things 970 00:55:35,740 --> 00:55:37,810 and it should be cheaper. 971 00:55:37,810 --> 00:55:40,873 And I can catch my diseases earlier by just a shift. 972 00:55:42,060 --> 00:55:43,240 And honestly, I'll be worried 973 00:55:43,240 --> 00:55:47,380 if my it's my thermogram shift is there 974 00:55:47,380 --> 00:55:49,593 I'll start taking care right away. 975 00:55:50,610 --> 00:55:51,443 Okay. 976 00:55:52,950 --> 00:55:54,950 I don't know if I answered the question. 977 00:55:58,890 --> 00:56:00,149 - Thank you. 978 00:56:00,149 --> 00:56:02,600 - All right. - All right. 979 00:56:02,600 --> 00:56:04,360 - Yes, I think you did answer the question. 980 00:56:04,360 --> 00:56:06,180 Thank you, Dr. Manyanga. 981 00:56:06,180 --> 00:56:09,760 And we'll now go to the next question 982 00:56:09,760 --> 00:56:11,410 from my colleague, Jason Brown, 983 00:56:11,410 --> 00:56:15,410 which is is a long-term goal to use the technology, 984 00:56:15,410 --> 00:56:18,780 to try to identify drugs that revert 985 00:56:19,670 --> 00:56:22,530 the altered biochemical signatures 986 00:56:22,530 --> 00:56:24,207 in different disease states 987 00:56:24,207 --> 00:56:26,133 from a normal signature? 988 00:56:27,140 --> 00:56:28,382 - Yes. 989 00:56:28,382 --> 00:56:29,843 (mumbles) like, 990 00:56:31,080 --> 00:56:33,060 there should be like a screening tool, 991 00:56:33,060 --> 00:56:36,470 like I said, over 90% of medications fail out there 992 00:56:39,166 --> 00:56:41,806 because when are designing a medication, 993 00:56:41,806 --> 00:56:44,940 one of the major things you need to worry about 994 00:56:44,940 --> 00:56:47,700 is the pharmacokinetics of it. 995 00:56:47,700 --> 00:56:49,230 So, 996 00:56:49,230 --> 00:56:52,740 if I am 997 00:56:52,740 --> 00:56:55,590 designing a drug and I see the binding here 998 00:56:55,590 --> 00:56:58,430 is extraordinary, it's going to affect 999 00:56:59,900 --> 00:57:01,830 like the volume of distribution 1000 00:57:01,830 --> 00:57:04,960 it's gonna affect how much it's gonna be available. 1001 00:57:04,960 --> 00:57:08,923 I don't want to take, say a medication by mouth. 1002 00:57:10,000 --> 00:57:12,980 That's gonna be say 30% available 1003 00:57:12,980 --> 00:57:14,650 in the systematic speculation 1004 00:57:14,650 --> 00:57:16,550 because it has been bound already. 1005 00:57:16,550 --> 00:57:19,000 So, I need that information 1006 00:57:19,000 --> 00:57:23,230 to see if I can administer my medication orally 1007 00:57:23,230 --> 00:57:25,450 by injection and all that, 1008 00:57:25,450 --> 00:57:27,650 because I need to know that binding. 1009 00:57:27,650 --> 00:57:31,423 How it's going to be affected by the bound 1010 00:57:33,560 --> 00:57:37,110 and the free medications long time. 1011 00:57:37,110 --> 00:57:41,943 That's number one goal, drug screening. 1012 00:57:42,860 --> 00:57:46,430 Number two goal is 1013 00:57:46,430 --> 00:57:50,820 to investigate medication interactions. 1014 00:57:50,820 --> 00:57:54,040 I can start investigating medication indirection. 1015 00:57:54,040 --> 00:57:54,963 For example, 1016 00:57:55,810 --> 00:57:59,930 if you go say to the cytochrome P four feet right there 1017 00:57:59,930 --> 00:58:04,370 you'll see some medications that either 1018 00:58:08,112 --> 00:58:11,940 up-regulate or down-regulate if they are together, 1019 00:58:11,940 --> 00:58:13,893 what you can do right there is, 1020 00:58:15,210 --> 00:58:17,830 you can use this technology to see 1021 00:58:19,040 --> 00:58:23,530 if the binding constants are close enough 1022 00:58:23,530 --> 00:58:25,530 for two medications that you want to administer 1023 00:58:25,530 --> 00:58:27,180 at the same time 1024 00:58:27,180 --> 00:58:30,350 or that kind of information. 1025 00:58:30,350 --> 00:58:32,530 And the third goal 1026 00:58:33,490 --> 00:58:35,853 is new medications. 1027 00:58:37,670 --> 00:58:38,633 For example, 1028 00:58:39,520 --> 00:58:42,423 I do have the data but I cannot show it right now. 1029 00:58:45,040 --> 00:58:50,040 We just went over and look at five HIV medications 1030 00:58:50,260 --> 00:58:51,690 on the counter. 1031 00:58:51,690 --> 00:58:53,740 We just take them and start running them. 1032 00:58:55,250 --> 00:58:57,920 We are interested to see if there any patterns 1033 00:58:57,920 --> 00:58:59,360 that are repeating. 1034 00:58:59,360 --> 00:59:02,690 They could, we don't know for sure 1035 00:59:02,690 --> 00:59:04,260 but they could be working 1036 00:59:06,000 --> 00:59:09,960 on the same binding site or some signature. 1037 00:59:09,960 --> 00:59:14,960 So we can predict other things or new drugs that are coming 1038 00:59:15,590 --> 00:59:17,933 before we start manufacturing them. 1039 00:59:18,930 --> 00:59:22,540 Like for example, I'm populating a library 1040 00:59:23,560 --> 00:59:27,350 of binding concepts for the medication that we know. 1041 00:59:27,350 --> 00:59:28,990 And once in a while, 1042 00:59:28,990 --> 00:59:32,300 we're gonna throw some medications that we don't know yet 1043 00:59:33,140 --> 00:59:36,530 to see if there's any patterns 1044 00:59:36,530 --> 00:59:38,480 just pattern recognition at this point. 1045 00:59:41,090 --> 00:59:42,940 - Thank you very much, Dr. Manyanga. 1046 00:59:42,940 --> 00:59:44,390 I think we'll have to leave it there 1047 00:59:44,390 --> 00:59:47,683 and I'll turn it over to Dr. Ronald Mac Taylor. 1048 00:59:49,770 --> 00:59:52,660 - Thank you Dr. Manyanga for this talk today. 1049 00:59:52,660 --> 00:59:55,090 On behalf of your colleagues 1050 00:59:55,090 --> 00:59:57,520 in the Department of Chemistry and Physics, 1051 00:59:57,520 --> 01:00:00,600 Darwin Fest and the Biology Department 1052 01:00:00,600 --> 01:00:04,683 we just wanna thank you for this excellent talk, 1053 01:00:05,770 --> 01:00:08,610 learning a little more about biochemistry all the time 1054 01:00:08,610 --> 01:00:10,930 which is a fun and exciting, 1055 01:00:10,930 --> 01:00:12,513 so, thank you very much. 1056 01:00:14,900 --> 01:00:17,920 - And this concludes our presentation and the webinar. 1057 01:00:17,920 --> 01:00:18,753 Thank you very much 1058 01:00:18,753 --> 01:00:22,070 and we look forward to seeing you at the talk 1059 01:00:22,070 --> 01:00:23,880 at 2:00 this afternoon. 1060 01:00:23,880 --> 01:00:24,890 Thank you so much. 1061 01:00:24,890 --> 01:00:26,514 We appreciate it. (mumbles) 1062 01:00:26,514 --> 01:00:27,347 Bye bye everybody. 1063 01:00:27,347 --> 01:00:28,180 - Bye bye.