1 00:00:09,240 --> 00:00:10,110 Sara Dong: Hi everyone. 2 00:00:10,110 --> 00:00:14,670 Welcome to Febrile, a cultured podcast about all things infectious disease. 3 00:00:14,820 --> 00:00:20,310 We use consult questions to dive into ID clinical reasoning, diagnostics and antimicrobial management. 4 00:00:20,400 --> 00:00:23,150 I'm Sara, your host, and a Med-Peds ID fellow. 5 00:00:23,205 --> 00:00:31,485 I am very excited for our Febrile Digest today, which we have entitled Hide and Seq: an ID Fellow Primer on Molecular Diagnostics. 6 00:00:31,784 --> 00:00:49,945 But really, I suspect that this will be helpful for anyone who is interacting with these tests because molecular diagnostics have been part of our ID space, but there are new developments every day, and some of us may not have the best handle on what these tests actually are, and more importantly, the strengths and limitations of these tests. 7 00:00:50,265 --> 00:00:56,250 Our guest co-host is here to guide us through helping you create a foundation for the terminology and methodology. 8 00:00:56,280 --> 00:00:57,330 You may remember Dr. 9 00:00:57,330 --> 00:01:02,400 Pratik "Tik" Patel from our PJP episode number 53 with Josh Wolf. 10 00:01:02,610 --> 00:01:05,190 Definitely go back and check that out if you have not already. 11 00:01:05,259 --> 00:01:09,860 Tik is a second year pediatric ID fellow at Emory University and Children's Healthcare of Atlanta. 12 00:01:10,170 --> 00:01:24,840 He also completed a pediatric hematology oncology fellowship at the same institution, and he wishes to leverage his training in both fields to advance the ID care of immunocompromised children with a focus on those undergoing treatment of cancer and hematopoietic stem cell transplant. 13 00:01:25,080 --> 00:01:31,970 He also has a research interest and introduction and implementation of novel diagnostics for improved stewardship and clinical care. 14 00:01:31,970 --> 00:01:32,610 Pratik Patel: Hey, everyone. 15 00:01:33,255 --> 00:01:36,855 Sara Dong: Today we actually have two special guests, which is wonderful. 16 00:01:37,305 --> 00:01:38,115 I'll start with Dr. 17 00:01:38,115 --> 00:01:43,434 Robin Patel, who is joining us from Mayo Clinic where she is the Elizabeth P and Robert E. 18 00:01:43,435 --> 00:01:58,785 Allen, Professor of Individualized Medicine and Director of the ID Research Laboratory, co-director of the Clinical Bacteriology Laboratory, Vice Chair of Education in the Department of Laboratory Medicine and Pathology, and the former chair of the division of Clinical Microbiology. 19 00:01:59,150 --> 00:01:59,450 Dr. 20 00:01:59,450 --> 00:02:10,490 Patel's focused her research on improvement of next generation diagnostic techniques, understanding the inherent biology of periprosthetic infection, and understanding antibiotic resistance through a clinical lens. 21 00:02:10,729 --> 00:02:19,430 She has published over 500 publications and is the director of the Laboratory Center of the Antibacterial Resistance Leadership Group of the NIH. 22 00:02:19,610 --> 00:02:20,360 Welcome, Robin. 23 00:02:20,360 --> 00:02:20,900 Hi. 24 00:02:20,900 --> 00:02:21,710 Robin Patel: Happy to be here. 25 00:02:21,710 --> 00:02:22,869 Thanks for having me. 26 00:02:23,470 --> 00:02:23,820 Sara Dong: Dr. 27 00:02:23,820 --> 00:02:29,955 Kevin Messacar is a associate professor of pediatrics at the University of Colorado School of Medicine. 28 00:02:30,015 --> 00:02:34,485 He is an attending pediatric hospitalist and ID consultant at Children's Hospital Colorado. 29 00:02:34,635 --> 00:02:43,165 His research seeks to improve diagnostic tests and surveillance for central nervous system infections, with a focus on enter viruses and other emerging infectious diseases. 30 00:02:43,650 --> 00:02:53,640 He is also interested in the process of selecting, implementing, and evaluating newly rapid diagnostic technologies using concepts of diagnostic and antimicrobial stewardship. 31 00:02:53,730 --> 00:03:06,629 He has received the Colorado Department of Public Health and Environment Astute Physician Award for work on acute flaccid myelitis and enterovirus D 68, as well as the Young Investigator Award from the Pediatric ID Society or PIDS. 32 00:03:06,780 --> 00:03:19,515 He is currently the principal investigator of a multi-center pandemic preparedness clinical research study, PREMISE, pandemic response repository- microbial and immunologic surveillance and epidemiology, thanks for joining us. 33 00:03:19,695 --> 00:03:20,265 Kevin Messacar: Happy to be here. 34 00:03:20,265 --> 00:03:21,015 Thanks for having me. 35 00:03:21,975 --> 00:03:24,075 Pratik Patel: So let's get started with a primer on the basics. 36 00:03:24,075 --> 00:03:35,705 So in molecular testing, it's always makes sense to nail down what exactly is DNA and what exactly is RNA, and how does the difference between them relate to diagnostics and infections? 37 00:03:36,225 --> 00:03:37,275 Robin Patel: Yeah, great question. 38 00:03:37,335 --> 00:03:52,125 Uh, DNA or Deoxyribo nucleic acid is what we've all learned about as being the hereditary material in humans, but also in bacteria, fungi, parasites, and DNA viruses. 39 00:03:52,695 --> 00:04:03,945 And it makes a great target for detecting organisms because the DNA of each microbial species is fairly unique. 40 00:04:05,100 --> 00:04:07,920 RNA is ribo nucleic acid. 41 00:04:08,130 --> 00:04:14,380 So that is what is transcribed from DNA in DNA containing organisms. 42 00:04:15,330 --> 00:04:24,840 But we also have some organisms, particularly RV viruses that have an RNA genome and no DNA. 43 00:04:25,380 --> 00:04:30,445 And in those organisms we can use RNA as a target for detection. 44 00:04:30,935 --> 00:04:31,635 Pratik Patel: Perfect. 45 00:04:31,710 --> 00:04:34,680 Now, how about the different modalities of molecular diagnostics? 46 00:04:34,680 --> 00:04:36,570 And we can start with PCR. 47 00:04:37,289 --> 00:04:39,990 How does that relate to DNA and RNA and how? 48 00:04:39,995 --> 00:04:43,380 How do we utilize DNA and RNA in PCR testing? 49 00:04:43,890 --> 00:04:55,020 Robin Patel: PCR which was described back in the 1980s, is a technique for amplifying small segments of DNA. 50 00:04:55,770 --> 00:04:58,210 It involves the use of primers. 51 00:04:58,935 --> 00:05:08,775 Typically a pair of primers that aneal and then synthesize the DNA between them using an enzyme called DNA polymerase. 52 00:05:09,645 --> 00:05:12,765 And then the cycle is repeated over and over again. 53 00:05:12,770 --> 00:05:19,784 So whatever region of DNA is being targeted is amplified exponentially. 54 00:05:20,414 --> 00:05:28,965 And so if you are looking for gene X, and gene X is just one of several thousand genes in a sample. 55 00:05:29,385 --> 00:05:40,335 You can really bring up the amount of Gen X a lot by pcr, and then you can detect that amplified material in a, a variety of different ways. 56 00:05:40,815 --> 00:05:47,474 Uh, traditionally, you know, we would run gels and do southern blots, but today we're commonly using probes. 57 00:05:47,655 --> 00:06:01,620 So, probes that are hybridizing at the same time that PCR is taking place so that we know we've amplified a very specific product which is important for diagnosis of infectious diseases. 58 00:06:02,130 --> 00:06:04,980 Pratik Patel: And I've heard of different types of PCR reactions. 59 00:06:05,190 --> 00:06:13,110 For example, there's a qRT PCR, transcription mediated amplification, nucleic acid amplification. 60 00:06:13,920 --> 00:06:21,360 And so what, is there a difference that we should be aware of, or are there important clinical differences between the different types? 61 00:06:22,050 --> 00:06:23,760 Robin Patel: Yeah, really good question. 62 00:06:23,760 --> 00:06:30,870 I'll try not to be too technical here because there are many, many ways of amplifying. 63 00:06:31,289 --> 00:06:40,260 Segments of DNA or RNA and PCR, as I described, is like the classic or original way of doing so. 64 00:06:40,265 --> 00:06:47,760 But there are many variations on how to do PCR that have to do with the starting material. 65 00:06:48,180 --> 00:06:56,250 So when you start with RNA you have to convert that to DNA before you can carry out your PCR. 66 00:06:56,940 --> 00:07:06,030 But also there's what's called real time pcr, which is where the probes are hybridizing while you're doing your amplification. 67 00:07:06,480 --> 00:07:25,020 Um, and then you mentioned TMA (transcription mediated amplification) and there are actually other, what we call nucleic acid amplification tests or NAATs that are out there and they work on different principles, but the idea is there in that you're amplifying a specific gene. 68 00:07:25,229 --> 00:07:34,620 So we see, um, lots of other sort of technical ways of amplifying and detecting a particular gene that are out there. 69 00:07:34,625 --> 00:07:41,099 And really, the term NAAT is a better term than PCR because it's, it's more general. 70 00:07:41,490 --> 00:07:49,710 Um, uh, but you know, technically some of the NAATs are pcr, so you can use that term in those scenarios. 71 00:07:50,490 --> 00:07:50,760 Pratik Patel: Great. 72 00:07:50,760 --> 00:07:56,640 And then can you comment a little bit on cycle threshold values, like CT values with pcr? 73 00:07:56,760 --> 00:08:06,780 There's a lot of kind of, uh, literature that has talked about it specifically with Covid recently, and so just highlighting that would be kind of useful for some trainees. 74 00:08:07,500 --> 00:08:08,040 Robin Patel: Yeah. 75 00:08:08,130 --> 00:08:10,200 Um, it's a really good question. 76 00:08:10,200 --> 00:08:16,140 There has been, um, a lot of discussion around CT values during the pandemic. 77 00:08:16,140 --> 00:08:18,780 These are cycle threshold values. 78 00:08:19,565 --> 00:08:44,579 And what this is, is when you're carrying out pcr, as I described, you're amplifying exponentially whatever gene you're targeting, and uh, then you have probes that are hybridizing or you can use specific stains that stain your amplified double stranded dna, but you're getting a signal from whatever is giving you that detection. 79 00:08:44,910 --> 00:08:49,620 And that signal will increase over time as you amplify your target. 80 00:08:50,099 --> 00:08:57,120 And at some point it will cross over whatever threshold it is that you are defining as a positive result. 81 00:08:57,689 --> 00:09:07,830 And so the cycle threshold means the number of cycles until you reach that threshold where you can say, Oh, I have a positive signal. 82 00:09:08,370 --> 00:09:19,170 And so a low cycle threshold means that you have a gene that you're targeting that's present in higher abundance than a high cycle threshold. 83 00:09:19,920 --> 00:09:24,390 Now, uh, that's a general principle and in real time pcr, 84 00:09:24,855 --> 00:09:28,275 we often time have a cycle threshold value. 85 00:09:28,275 --> 00:09:48,975 In some of the other nucleic acid application technologies, we, we don't have a cycle threshold, but I think what people have been trying to do is to take cycle threshold values and transition what is a qualitative assay into a quantitative assay in infectious diseases. 86 00:09:49,035 --> 00:09:56,385 We do have quantitative assays, for example, when we measure HIV viral load, we have quantitative assays. 87 00:09:56,385 --> 00:09:58,545 We're used to using those assays. 88 00:09:59,385 --> 00:10:12,625 Those assays are quantitative because they're specifically validated to be quantitative, and they include standards in the assay that tell you when you get a certain quantity, that's the correct quantity. 89 00:10:12,625 --> 00:10:15,504 They're not just a real time PCR assay. 90 00:10:15,739 --> 00:10:20,035 There's more in terms of quality control that's incorporated into those assays. 91 00:10:20,035 --> 00:10:34,005 And so there has been some concern about relying on just cycle threshold values without creating a true quantitative assay, uh, around the COVID 19 pandemic. 92 00:10:34,250 --> 00:10:38,180 And sort of reading into the lab data too much. 93 00:10:38,480 --> 00:10:45,380 I think what people are truly doing is trying to make that test a test of infectiousness, which is a whole different conversation. 94 00:10:45,530 --> 00:10:47,930 But hopefully that helps answer your question. 95 00:10:49,010 --> 00:10:50,030 Pratik Patel: Yeah, thank you. 96 00:10:50,810 --> 00:10:56,270 Now, I guess, can we highlight some of the specific tests in ID that are pCR based? 97 00:10:56,865 --> 00:10:57,135 Kevin Messacar: Sure. 98 00:10:57,135 --> 00:11:05,985 I think we've seen a, a transition in how we've used molecular testing for clinical infectious disease over the past decade or so. 99 00:11:06,255 --> 00:11:18,325 Traditionally, we've used a very pathogen specific approach, and as Robin was mentioning, you know, we can target specific genes of DNA or RNA uh, organisms or viruses or bacteria. 100 00:11:18,655 --> 00:11:21,175 Um, but that requires clinical suspicion. 101 00:11:21,175 --> 00:11:29,515 So we've always had the ability, you know, since molecular diagnostics have come around to send a influenza PCR test that tells us yes or no is influenza there. 102 00:11:29,845 --> 00:11:33,355 And HSV PCR test that tells us is HSV there. 103 00:11:33,740 --> 00:11:36,709 Other examples, uh, would be Mycoplasma tests. 104 00:11:36,709 --> 00:11:43,880 There's now group A strep tests, many different examples of just targeting a specific pathogen and saying yes or no, is that present. 105 00:11:44,569 --> 00:11:52,819 What we've seen is kind of a shift in the diagnostic approach from many of the clinical platforms that are coming out towards a more syndromic based testing. 106 00:11:53,209 --> 00:11:59,069 So we're combining multiple PCR test in one platform known as a syndromic panel. 107 00:11:59,370 --> 00:12:10,050 Um, and typically this is multiplex pcr, so semi nested PCR that can contain multiple targets, which can include viruses, bacteria, parasites, fungi. 108 00:12:10,050 --> 00:12:13,590 It can be DNA targets and RNA targets combined. 109 00:12:13,980 --> 00:12:20,069 And basically, instead of saying you have to have a clinical suspicion of, is this hsv, yes or no? 110 00:12:20,069 --> 00:12:23,970 You can just say, I'm concerned my patient has a central nervous system infection. 111 00:12:23,970 --> 00:12:27,869 I'm gonna look for all the, are most likely suspects at the same time. 112 00:12:27,869 --> 00:12:35,069 So we can get into the, uh, benefits and the drawbacks of that approach, cuz it definitely comes with both. 113 00:12:35,430 --> 00:12:44,950 Um, but you're seeing a significant shift kind of in the commercial platform field now that we have access to, uh, rapid identification of bloodstream infections. 114 00:12:44,950 --> 00:12:50,770 We have respiratory panels that can look for viruses and bacteria, both in the upper and lower respiratory tract. 115 00:12:51,220 --> 00:12:56,170 We have, uh, stool-based platforms that can look for viruses, bacteria, and parasites there. 116 00:12:56,410 --> 00:13:08,290 Um, and we have the, the newer meningitis, encephalitis panels, each of which when they have been introduced, uh, into the clinical realm, have led to some twists of how do we interpret those, how do we use them? 117 00:13:08,600 --> 00:13:10,190 What clinical impact do they have? 118 00:13:10,190 --> 00:13:11,570 Are they cost effective? 119 00:13:11,990 --> 00:13:15,020 Um, and I think that's still a really interesting area of inquiry. 120 00:13:15,020 --> 00:13:28,400 How do they change how we practice medicine day to day when we go from this era of having to have a clinical suspicion for what we're looking for versus kind of using a molecular platform to look for many things at. 121 00:13:29,430 --> 00:13:32,550 Pratik Patel: Yeah, I know there's lots of great benefits and drawbacks. 122 00:13:32,580 --> 00:13:40,800 Um, so speaking about that, can you highlight some of the limitations of PCR based testing specifically as it relates to infectious disease? 123 00:13:41,610 --> 00:13:41,880 Kevin Messacar: Sure. 124 00:13:41,880 --> 00:13:48,510 I think it, it really goes back to the basic principles of diagnostic reasoning and, and pretest probability. 125 00:13:48,510 --> 00:13:55,890 So how suspicious are you up upfront, uh, of a particular organism knowing that you're gonna lose some of that? 126 00:13:55,890 --> 00:14:04,079 So when you're using a syndromic panel, even though you may be looking for one target on that panel, you're gonna get results for everything else that's included on that panel. 127 00:14:04,079 --> 00:14:08,010 So you're stuck kind of interpreting data sometimes maybe that you didn't want. 128 00:14:08,430 --> 00:14:12,300 Um, so going back to, you know, the basic root of diagnostic reasoning. 129 00:14:12,585 --> 00:14:30,345 How do I interpret that result in the context of the patient in front of me and, and knowing some specific caveats that detection of nucleic acid does not necessarily mean the presence of an active infection or the presence of an infection that's causing the symptoms that I'm evaluating for in the patient in front of me. 130 00:14:30,345 --> 00:14:35,725 So, Knowing that many viruses shed long after the active infectious period is done. 131 00:14:36,175 --> 00:14:49,410 Uh, particularly common viruses like the rhino viruses, you frequently pick those up on the respiratory multiplex panels when they're not the cause of, of the disease in the patient in front of you, but they're just shedding virus from a, a previous infection. 132 00:14:49,680 --> 00:14:51,270 That's one aspect of it. 133 00:14:51,570 --> 00:14:56,070 Um, there is a potential for decreased sensitivity of some of the targets when you multiplex them. 134 00:14:56,075 --> 00:15:08,580 So in particular, for example, the HSV PCR of CSF is pretty sensitive when you use a singleplex assay, that, uh, limit of detection is, uh, a bit higher when you use a multiplex assay. 135 00:15:08,580 --> 00:15:14,250 So you may miss low viral load infections in the central nervous system on a multiplex assay. 136 00:15:14,670 --> 00:15:26,200 Um, and then in general, I think interpreting a result that really just doesn't fit your patient context is important to take a step back and say, Yes, I detected this. 137 00:15:26,685 --> 00:15:31,245 But is this truly what this patient looks like, smells like, sounds like is really important. 138 00:15:31,245 --> 00:15:33,795 And we've seen that time and time again with c diff. 139 00:15:33,975 --> 00:15:39,705 So detecting c diff shedding in stool when there's not the correct symptom complex in front of you. 140 00:15:40,035 --> 00:15:44,835 Um, as well as as many of the other targets like on the meningitis, encephalitis panel. 141 00:15:45,120 --> 00:15:48,120 Chromosomal integration of HHV6 is a huge problem. 142 00:15:48,120 --> 00:16:02,265 So we see, um, about 1% positivity in the general population who just have the, the viral DNA incorporated into their chromosomes, and therefore you're gonna detect it in every cell in their body on, you know, anytime you test it. 143 00:16:02,265 --> 00:16:13,665 And so if you test, you know, like our lab 800 CSFs a year on the meningitis encephalitis panel, you're gonna have eight patients a year that have detection of HHV6, but that's not the cause of their disease. 144 00:16:13,665 --> 00:16:16,035 So it's hard to go through every example. 145 00:16:16,035 --> 00:16:22,755 But just kind of going back to those roots of, of diagnostic reasoning and thinking, what was my pretest probability before I sent this test? 146 00:16:23,145 --> 00:16:25,725 And how do I interpret that in terms of the patient in front? 147 00:16:26,625 --> 00:16:26,835 Pratik Patel: Yeah. 148 00:16:26,835 --> 00:16:27,195 Great. 149 00:16:27,195 --> 00:16:27,825 Thank I. 150 00:16:27,855 --> 00:16:33,405 So it's great that we've talked about, you know, Singleplex pcr, Multiplex pcr. 151 00:16:34,185 --> 00:16:40,755 Well, let's shift to like another type of PCR testing that's, you know, becoming more widely used, which is broad range pcr. 152 00:16:41,475 --> 00:16:54,074 But first, let's cover the basics so you know, what exactly is broad range bacterial PCR testing and, and specifically, you know, here of 16S rRNA for broad range PCR testing. 153 00:16:54,074 --> 00:16:58,425 And so what is that and how, how is that useful in this, in this testing approach? 154 00:16:59,204 --> 00:17:00,344 Robin Patel: Yeah, great question. 155 00:17:00,349 --> 00:17:21,435 So, um, first of all, just the general approach, because before we get into the technical details, and we talked about how PCR assays typically are designed to be, specific, in other words, to only pick up what you're targeting that's, you know, very sought after. 156 00:17:21,435 --> 00:17:35,505 I mean, if you have different sort of versions of the same organism, you might need to make sure you capture that, but you actually don't wanna be capturing a lot of things because you don't want, you know, false positive results, if you will. 157 00:17:36,375 --> 00:17:46,725 But, um, there is another approach, and that is to go after a target that's present in every organism. 158 00:17:46,725 --> 00:17:51,525 And so the 16 S ribosomal RNA gene is a great example. 159 00:17:51,525 --> 00:18:01,725 It's present in every bacterial species, and so you could use it as a general indicator that there are bacteria there. 160 00:18:01,905 --> 00:18:18,495 That is actually used in some diagnostics, but is not the most common way that this is used because there's another characteristic of the 16 S ribosomal RNA gene that's really helpful clinically, and that is so it's present in all bacteria. 161 00:18:18,945 --> 00:18:38,475 It has areas of conservation and areas of variability, and so we can design PCR primers that target the conserved regions that will amplify a fragment of the 16S ribosomal RNA gene from any bacterium that's present in the sample. 162 00:18:38,925 --> 00:19:00,600 But then we can sequence the area in between those primers and sequence through variable regions that tell us based on looking at that sequence data, which bacterial species it is that we're looking at, and that gives you what we call a broad range bacterial approach. 163 00:19:01,020 --> 00:19:07,530 There are other genes that are conserved in bacteria that could be targeted. 164 00:19:08,400 --> 00:19:15,810 But we have the most information of any gene for the 16 S ribosomal RNA gene. 165 00:19:16,170 --> 00:19:21,870 So that's the one that you're most likely to see, um, in an assay. 166 00:19:22,500 --> 00:19:34,325 The sequencing itself is something that has been done routinely in clinical laboratories like ours since the 1990s, but that has used Sanger sequencing. 167 00:19:34,804 --> 00:19:53,625 Sanger sequencing essentially lets you interpret a very clean sequence read from a single bacterial species that has no copy variance of its 16S ribosomal RNA gene because oftentimes this is a multi copy gene in bacteria. 168 00:19:54,375 --> 00:20:24,435 When you have more than one sequence of the 16S ribosomal R RNA gene present in a sample, such as in the case of a polymicrobial infection, if you attempt to do Sanger sequencing, it's almost like reading two words at the same time with the letters on top of 'em, and you cannot easily decipher what it is you're looking at, but that can be sorted out now with next generation sequencing of that product. 169 00:20:24,435 --> 00:20:35,335 And then we can, we can really look at the full portfolio of anything contributing to that 16 s ribosomal RNA gene sequence data. 170 00:20:35,645 --> 00:21:01,155 It's actually a technique that's used to define the microbiome in microbiome research, but it can be used clinically on samples that don't typically have a normal microbiome to, um, sort out when, uh, there's either a very low amount of bacteria present in the context of some background or more than one bacterial species in the same sample. 171 00:21:01,605 --> 00:21:10,875 This gene is, uh, specific for bacteria so it doesn't pick up other organism types like fungi or parasites or virus. 172 00:21:12,660 --> 00:21:18,310 Pratik Patel: And speaking about fungi, is 18 s or sometimes I hear about 28 s. 173 00:21:18,389 --> 00:21:25,050 Is that the same kind of thinking, um, or the same approach, um, to doing broad range fungal testing? 174 00:21:25,800 --> 00:21:26,460 Robin Patel: Absolutely. 175 00:21:26,465 --> 00:21:48,000 So there are several, uh, parallel targets, I guess I would say in fungi that can be used, uh, that really follow that same pathway, you know, present in all fungi have areas of conservation and variability that can be targeted with a broad range PCR sequencing based approach. 176 00:21:48,360 --> 00:21:58,500 Um, and again, sequencing with either Sanger sequencing or next generation sequencing, or perhaps even both depending on how a particular assay is set up. 177 00:21:59,010 --> 00:22:09,480 And then Kevin had talked about multiplexing because we see a lot of these panels that are out there today that we use that can do multiple PCR at the same time. 178 00:22:09,480 --> 00:22:13,650 So theoretically you can do all of that, uh, together. 179 00:22:14,670 --> 00:22:26,490 Pratik Patel: And last bit about just mycobacteria, you know, I've heard it's a different process for them and I've heard something about heat shock protein, which sounds pretty cool, but I don't know how that relates to kind of identification, 180 00:22:27,600 --> 00:22:28,500 Robin Patel: Uh, fundamentally. 181 00:22:28,949 --> 00:22:31,500 You know, mycobacteria are bacteria. 182 00:22:32,040 --> 00:22:37,290 So, um, when, when I think about that, I, I put them together. 183 00:22:37,290 --> 00:22:49,380 They have 16 s ribosomal RNA genes, and so they can be detected with, um, a 16 s ribosomal RNA gene PCR sequencing assay. 184 00:22:49,680 --> 00:23:03,000 Depending on how the assay is designed for mycobacteria and other species of bacteria, um, you might be targeting different areas of the 16 S ribosomal RNA gene. 185 00:23:03,000 --> 00:23:12,870 And some areas are more informative than others in separating out species of different groups of bacteria such as Mycobacterium species. 186 00:23:13,605 --> 00:23:31,155 But certainly the 16 s ribosomal RNA gene can be used, but then you can look at other targets that might be perhaps more informative in Mycobacterium species to maybe get a higher level of, uh, separation. 187 00:23:31,455 --> 00:23:33,015 I think diagnostically. 188 00:23:34,650 --> 00:23:36,540 Uh, there are two questions. 189 00:23:36,540 --> 00:23:41,790 You know, if you're thinking mycobacteria, one question is, is there a mycobacterium present? 190 00:23:42,240 --> 00:23:46,860 And another question is, what is that species of mycobacteria. 191 00:23:47,070 --> 00:24:04,649 You know, sometimes, we can't get to the detailed species with, with many of these diagnostics, even Mycobacterium tuberculosis is a complex, uh, but many others are groups or complexes of organisms as well, which is probably fine clinically for the, for the most part. 192 00:24:05,399 --> 00:24:10,649 Uh, but again, I think the main message is that mycobacteria are bacteria. 193 00:24:11,525 --> 00:24:17,505 . Pratik Patel: And when you talk about sequencing, can you speak to how does the identification happen specifically? 194 00:24:17,505 --> 00:24:27,555 Like what, what is done with the sequencing data and then how does that match up to like figuring out which organism is causing the or is present. 195 00:24:28,725 --> 00:24:31,905 Robin Patel: Yeah, that's, uh, that's the fun of sequencing. 196 00:24:32,325 --> 00:24:34,545 So you generate a lot of data. 197 00:24:34,545 --> 00:24:39,195 And I'll talk first about Sanger sequencing because that's the most straightforward. 198 00:24:39,195 --> 00:24:50,460 You know, you have a string of nucleotides that comes off, typically, um, you're doing bidirectional sequencing because you're sequencing from both the forward and reverse primers. 199 00:24:50,460 --> 00:24:57,060 So that's nice because then you can overlap those and you know, you got the same answer twice in both directions. 200 00:24:57,060 --> 00:25:00,780 So it's a, a measure of quality, I guess, that, that you get. 201 00:25:01,260 --> 00:25:09,240 Um, so then you take that, um, concatenated sequence that's been put together and you have to run it against a database. 202 00:25:09,255 --> 00:25:14,625 And, and this is where, um, there can definitely be some variability. 203 00:25:14,625 --> 00:25:35,235 So either you're using a pre-constructed database where someone, either your team or others have put together a database that says, you know, this sequence is Mycobacterium tuberculosis complex, and now this sequence is Streptococcus agalactiae et cetera. 204 00:25:36,870 --> 00:25:46,080 Or you're using a public database such as NCBI, um, that database is going to have a lot of sequences in it. 205 00:25:46,139 --> 00:25:51,510 It's not completely curated, uh, but it's more comprehensive. 206 00:25:52,020 --> 00:26:05,181 Um, and so your analysis has to really look at what does my query against this database tell me this is, um, is it tell, And it could tell you. 207 00:26:05,790 --> 00:26:23,100 That it's this species, and then you have to determine whether it's all the related species to that species have been considered in your analysis, are in your database, and that there's maybe enough distance from anything else to be able to call that particular species. 208 00:26:23,669 --> 00:26:25,020 Uh, sometimes they're not. 209 00:26:25,050 --> 00:26:28,409 There's not, and there are a lot of organisms that read in together. 210 00:26:28,439 --> 00:26:29,729 Here's an example of that. 211 00:26:30,559 --> 00:26:33,945 Brucella species, they all pretty much have the same sequence. 212 00:26:34,485 --> 00:26:41,415 Actually, they're probably all the same species, and that'll happen probably sometime in your future ID fellows just to make things confusing. 213 00:26:41,419 --> 00:26:48,585 But, but so, you know, you couldn't possibly call a particular species of Brucella based on that analysis alone. 214 00:26:48,975 --> 00:26:53,205 But we know that, uh, clinicians like to know as much information as possible. 215 00:26:53,210 --> 00:27:14,865 So you try to get to the species where you can get to the species and otherwise you group, kind of, um, roll up to a genus level identification or a group or complex level identification, or most closely related to if there's something else reading in that's, um, maybe, you know, nipping at your, your heels, um, behind that sequence. 216 00:27:15,345 --> 00:27:27,870 Um, next generation sequencing is more complicated because there, if you're sequencing bidirectionally, you have forward and reverse sequences, but you don't necessarily know what goes with what. 217 00:27:28,230 --> 00:27:34,290 And oftentimes you have multiple different organisms that are reading in together at various abundances. 218 00:27:34,740 --> 00:27:42,690 And so interpretation of that is done in the same way against databases, uh, but, but is a lot more complex. 219 00:27:42,960 --> 00:28:00,060 Um, a lot of times, especially if you're looking at a clinical specimen, that doesn't have a lot of organism in it, you are also seeing, um, the background sequences of the assay because there are bacteria everywhere. 220 00:28:00,090 --> 00:28:01,320 They're all over us. 221 00:28:01,320 --> 00:28:02,760 They're in the environment. 222 00:28:02,760 --> 00:28:04,740 They're, they can be in reagents. 223 00:28:05,070 --> 00:28:15,090 And so if you, um, if you dig deep enough, you'll find bacterial sequences, 16S ribosomal RNA, gene sequences in pretty much everything. 224 00:28:15,090 --> 00:28:22,815 So then you have to sort out, not only what is it, You know, is this something that should be clinically reported? 225 00:28:22,815 --> 00:28:27,014 Because we, we all know how much confusion that can create. 226 00:28:27,075 --> 00:28:30,575 Um, Kevin spoke to that a little bit, even with the multiplex PCRs. 227 00:28:31,004 --> 00:28:41,595 Uh, but you know, when you report out something that maybe is just coming from your background, but um, you know, when it comes out in the report, looks like it could be clinically significant. 228 00:28:41,685 --> 00:28:45,465 So, um, databases are what, um, what you need. 229 00:28:45,470 --> 00:28:55,200 I will say another interesting and unique challenge here is that, uh, bacteria, bacterial taxonomy is rapidly changing. 230 00:28:55,200 --> 00:28:57,990 So exactly what you call things can change. 231 00:28:58,530 --> 00:29:05,670 And, um, in our experience we've also seen sequences of bacteria that probably are not yet named. 232 00:29:06,480 --> 00:29:10,710 That's a real challenge to report on the clinical side. 233 00:29:10,710 --> 00:29:17,280 So there's a lot of work that needs to be done to continue to describe bacterial species. 234 00:29:18,180 --> 00:29:27,660 Kevin Messacar: We all love it when the name of, uh, bacteria that we spent so long memorizing and putting in our memory bank gets their name changed and we have to learn everything all over again. 235 00:29:27,660 --> 00:29:31,950 So those taxonomy folks are not the most popular people in infectious disease 236 00:29:32,490 --> 00:29:34,230 Robin Patel: or my, or clinical microbiology. 237 00:29:34,230 --> 00:29:36,130 We, we don't like doing that either. 238 00:29:36,450 --> 00:29:40,440 We know how much confusion it creates and Yeah. 239 00:29:40,470 --> 00:29:58,320 But you know, it's maybe because of all this sequencing and understanding of microbes that the taxonomists are reclassifying things, because in the past we classified organisms based on their phenotypical and morphologic, um, characteristics. 240 00:29:58,325 --> 00:30:02,820 And then today when we get sequence data, we realize, well, that that was wrong. 241 00:30:02,820 --> 00:30:08,920 That that is not related to that and doesn't belong in this genus or, you know, et cetera. 242 00:30:08,925 --> 00:30:15,100 And so then they get around to renaming things and, and then we have to update systems and change the way we report things. 243 00:30:15,100 --> 00:30:22,330 And then that causes a lot of confusion on the clinical side and, you know, undoing of what's been taught in the past and so forth. 244 00:30:22,330 --> 00:30:25,090 So nobody, nobody loves those changes. 245 00:30:27,100 --> 00:30:27,640 . Pratik Patel: Yeah. 246 00:30:28,060 --> 00:30:30,790 And who does broad range PCR in the US? 247 00:30:31,890 --> 00:30:42,000 Can you guys speak, each of you speak to some clinical scenarios where there's data or, you know, personal experience of yours, that instances it could be useful for ID clinicians. 248 00:30:42,240 --> 00:30:53,250 Kevin Messacar: The two places that I know that, uh, you can send the 16 s and 28 s to clinically are Seattle, University of Washington, and the Mayo Clinic, and that's where we send our samples. 249 00:30:53,280 --> 00:31:15,225 There's a broad range of experiences with the use of them and I, I would, from a clinical research standpoint, just caution folks, when you're reading retrospective observational data sets, just know that that tends to be very heterogeneous population and oftentimes sent at various time points, often later in the course of disease, things like that. 250 00:31:15,225 --> 00:31:17,745 So there's a lot of caveats to that data. 251 00:31:18,195 --> 00:31:26,625 Um, I will say having read a lot of the observational experiences that the yield is lower than I would expect for many of them. 252 00:31:26,625 --> 00:31:42,075 Whether that's due to, you know, patients being pretreated or being sent late, um, in our hands, from a personal view standpoint, the 16S, 28S kind of platforms have been most useful in situations where you have source tissue, so you have a biopsy. 253 00:31:42,650 --> 00:31:58,430 You see organisms, so it's Gram stain positive or you see fungi there, and for whatever reason, whether it's a diagnostically challenging organism to grow or a situation with pretreatment, you can't get an identification by routine, uh, microbiological techniques. 254 00:31:58,435 --> 00:32:03,530 Those tend to be the situations in which we send those out, and those tend to be our highest yield situations 255 00:32:03,870 --> 00:32:10,409 Robin Patel: yeah, and I can comment a little bit because, uh, we're one of the labs that does this, uh, kind of testing. 256 00:32:10,889 --> 00:32:18,330 Uh, it's a relatively new area, so we don't have all, um, the answers to the questions you might have. 257 00:32:18,720 --> 00:32:24,490 We did, uh, recently publish a couple of articles that might be of interest. 258 00:32:24,690 --> 00:32:46,740 The most recent one is in clinical infectious diseases this year, and we did a look back our 16S ribosomal RNA gene PCR sequencing assay applied to 2,146 normally sterile tissue and body fluid samples in our routine clinical practice. 259 00:32:47,430 --> 00:32:57,150 Um, we do an algorithm where we run a PCR assay first, and um, we get a CT value from that PCR assay. 260 00:32:57,155 --> 00:33:00,750 And if the CT value is low, we run Sanger sequencing. 261 00:33:01,290 --> 00:33:13,364 If it's medium high, then we go to next generation sequencing because our validation data tells us that Sanger sequencing is unlikely to give us a definitive answer there. 262 00:33:13,695 --> 00:33:23,054 And if the CT value is high, we, we know that sequencing by and large doesn't give us a useful result, so we just report that result as negative. 263 00:33:23,084 --> 00:33:28,094 It's a relatively parsimonious way of applying this kind of testing. 264 00:33:29,100 --> 00:33:43,770 And, um, so what we found is that adding this next generation sequencing to um, just the Sanger sequencing approach increased our positivity rate by 87%. 265 00:33:44,370 --> 00:34:03,419 Um, and you're right, uh, Kevin, that maybe detection rate is the best way of looking at it is, is not maybe as high as you would love to see it, but I, I think you're also right that there are certain scenarios where this kind of testing can be particularly helpful. 266 00:34:03,425 --> 00:34:15,360 Obviously when you're suspecting a bacterial infection, but when you can see the organism on staining or there's a histopathologic response that suggests there might be a bacterium there, that can be really helpful. 267 00:34:16,149 --> 00:34:29,475 And um, one disease in particular that I'd like to highlight, where I think this is really standard of care is in infective endocarditis, but in a particular scenario. 268 00:34:29,804 --> 00:34:36,645 Um, we all know that blood cultures are the first microbiologic tests that you would do in that scenario. 269 00:34:36,645 --> 00:34:43,574 And of course, if you get positive blood cultures with a consistent organism, you don't really need to do other testing. 270 00:34:43,995 --> 00:35:01,560 And then if cultures are negative, which sometimes they are, oftentimes because of antibiotics that were given prior to blood cultures being collected, um, Then you go on with your culture negative endocarditis workup, uh, with a Brucella serology and a Coxiella burnetti serology. 271 00:35:01,565 --> 00:35:10,740 But we don't have great diagnostic tests, um, yet for some of the other common causes of infective endocarditis in culture negative cases. 272 00:35:10,740 --> 00:35:17,640 And so if patients do come to valve resection, um, that valve should be sent to histopathology for one thing. 273 00:35:18,090 --> 00:35:27,180 Uh, for an, an expert histopathologist, someone who has experience in infectious diseases and, um, cardiovascular pathology to take a look at that valve. 274 00:35:27,790 --> 00:35:43,375 And, um, if there's acute inflammation there, if it looks like it's consistent with infective endocarditis, then running a 16S ribosomal rna uh, gene PCR sequencing assay is really your test of choice, actually above and beyond culture. 275 00:35:44,155 --> 00:35:55,975 Um, we've also found some, some interesting detections in plural fluid, which I think is a clinical type of specimen that we're still learning a lot about as far. 276 00:35:56,805 --> 00:36:04,125 Um, you know, what's going on and what microbes are causing pathology in people with pleural effusion. 277 00:36:04,185 --> 00:36:06,675 So that can be very helpful as well. 278 00:36:07,155 --> 00:36:11,040 Um, But again, we continue to learn from this. 279 00:36:11,100 --> 00:36:28,140 Um, we know that antibiotics affect the sensitivity of culture and they also affect the sensitivity of 16 s ribosomal RNA gene pcr and sequencing, although to a lesser extent, uh, but not surprisingly, since they target bacteria. 280 00:36:28,710 --> 00:36:42,495 Um, Detection rate goes down in people on antibiotics and it goes down progressively, um, uh, depending on the amount and timing of antibiotics that have been received by the patient. 281 00:36:43,425 --> 00:36:51,255 Pratik Patel: All right, So we've talked a little bit about sequencing and, and the differences between Sanger sequencing and next generation sequencing. 282 00:36:51,855 --> 00:36:57,195 Can we unpack, um, the word meta-genomics and like how does that apply? 283 00:36:57,630 --> 00:37:04,200 When we hear about sequencing, like what is metagenomics and what, how does that relate to like ID? 284 00:37:04,680 --> 00:37:06,000 Robin Patel: That's a really good question. 285 00:37:06,000 --> 00:37:18,810 I think the term metagenomic has been used in a lot of different ways, but regardless of what assay you use in clinical infectious diseases, you should understand what it is that you've ordered. 286 00:37:19,200 --> 00:37:29,205 So typically, a metogenomic assay is going to involve next generation sequencing, ,which is probably better referred to as massively parallel sequencing. 287 00:37:29,805 --> 00:37:36,915 You can do it in a completely unbiased way where you just sequence everything in a sample. 288 00:37:37,755 --> 00:37:43,065 And if you do that, starting from DNA, you'll sequence the human genome. 289 00:37:43,785 --> 00:37:49,005 You'll sequence bacteria, fungi, parasites and DNA viruses. 290 00:37:49,305 --> 00:37:51,345 You'll miss RNA viruses, of course. 291 00:37:52,350 --> 00:38:02,550 As with PCR, you can introduce a step where you take RNA and you convert it to DNA and sequence that, and then you can detect RNA viruses as well. 292 00:38:03,480 --> 00:38:23,040 But you can also do next generation sequencing on amplified single genes like the 16 s ribosomal RNA gene that we just talked about, which is more of a targeted metagenomic approach as opposed to, uh, a shotgun metogenomic approach, which is used when you're sequencing everything. 293 00:38:23,850 --> 00:38:24,120 Pratik Patel: Yeah. 294 00:38:24,120 --> 00:38:34,710 It's, you know, very interesting, especially since sequencing is now penetrating more and more medical domains like genetics, oncology, I think it's very important to understand the tests that you're sending. 295 00:38:35,175 --> 00:38:44,535 What are some commercial platforms that clinicians can order sequencing for ID purposes and kind of what sources, um, are typically sequenced. 296 00:38:44,835 --> 00:38:44,895 Kevin Messacar: Yeah. 297 00:38:44,895 --> 00:38:49,500 So jumping off our last discussion of the kind of evolution of molecular testing. 298 00:38:49,500 --> 00:38:55,200 We talked about going from a pathogen specific approach to a syndromic approach using multiplex pcr. 299 00:38:55,200 --> 00:39:00,960 And now we're really moving into a more unbiased approach, meaning we're looking for everything at once. 300 00:39:01,319 --> 00:39:04,649 Um, not even just a list of most likely pathogens. 301 00:39:04,649 --> 00:39:10,575 And so there's a few kind of emerging technologies and places you can send samples. 302 00:39:10,965 --> 00:39:22,185 Um, I think the most, uh, pertinent one that we're seeing a lot emerging on the clinical side of things, um, is the plasma cell-free DNA sequencing technology. 303 00:39:22,190 --> 00:39:25,180 So the commercial test is called Karius currently. 304 00:39:25,510 --> 00:39:28,360 Um, so you send a, a plasma specimen out. 305 00:39:28,390 --> 00:39:33,910 It gets, uh, sequenced for trace amounts of microbial DNA and plasma. 306 00:39:34,330 --> 00:39:40,810 Um, it can detect both organisms in the bloodstream and trace amounts of, uh, DNA based organisms. 307 00:39:40,815 --> 00:39:49,295 Uh, even in tissues in some cases, we're still learning more about the sensitivity and, and specificity of various targets on that platform. 308 00:39:49,654 --> 00:39:55,384 Um, but we're seeing more and more of its use for clinical care in diagnostically challenging cases. 309 00:39:55,984 --> 00:40:02,564 Um, another is, uh, CSF uh, next gen sequencing for meningitis and encephalitis. 310 00:40:02,564 --> 00:40:12,810 This is a category where we've never been able to chip away at that 50% of suspected cases of CNS infection that we just can't get a microbial diagnosis. 311 00:40:13,380 --> 00:40:18,420 That can be caused by RNA viruses, DNA viruses, bacteria, fungi, parasites. 312 00:40:18,420 --> 00:40:30,715 So it's kind of the perfect application of a very common clinical presentation that's very hard to differentiate etiology based on clinical factors, um, and could be due to many different things. 313 00:40:30,715 --> 00:40:43,255 So there is a, a clear approved platform that, uh, is being used at, uh, U C S F, uh, Charles Chiu Lab, um, that can do sequencing of CSF for those diagnostically challenging cases. 314 00:40:43,615 --> 00:40:51,195 Unlike the Karius that we talked about that just detects DNA based organisms, this detects DNA and RNA based, uh, organism. 315 00:40:51,715 --> 00:40:54,985 Um, a little longer turnaround time, typically with that platform. 316 00:40:55,345 --> 00:41:06,175 And then we talked previously about the broad ranged, uh, and, and sequencing platforms used for, uh, tissue samples from source, uh, samples, biopsies, and others. 317 00:41:06,175 --> 00:41:09,785 So those are the main, uh, platforms currently clinically available. 318 00:41:10,435 --> 00:41:17,695 I will say that technology sometimes advances quicker than our knowledge on how best to use them and how to interpret them. 319 00:41:17,695 --> 00:41:23,105 So I think we're in the catching up phase of we've got these really neat new tools. 320 00:41:23,105 --> 00:41:27,095 We're learning how to, to best use them and how to best apply them for clinical care. 321 00:41:27,515 --> 00:41:28,595 Robin Patel: Completely agree. 322 00:41:28,595 --> 00:41:35,865 I would add too that the clinician really needs to understand what they're looking for. 323 00:41:36,195 --> 00:42:09,509 As an example, um, my team works a lot on periprosthetic joint infection, which is largely a bacterial infection, and we have a publication in press Clinical Infectious Diseases, where we compared, uh, 16 s ribosomal R gene PCR sequencing or targeted metogenomic sequencing based approach to shotgun metagenomic sequencing on a specimen we call sonication fluid, which comes from removing biofilms from explanted devices, and, um, the performance of the two approaches was the same. 324 00:42:09,899 --> 00:42:14,100 And that makes sense because you're just looking for bacteria. 325 00:42:14,310 --> 00:42:23,399 But I think it's also really helpful because it's a lot simpler to do a targeted approach and a lot more cost effective than a shotgun approach. 326 00:42:23,759 --> 00:42:35,250 Um, and so in clinical practice, I think we need to sort out disease by disease, when to use these tests and exactly which test, uh, to use. 327 00:42:35,610 --> 00:42:40,920 Uh, and so there's a lot of research for people interested in research in infectious diseases. 328 00:42:41,250 --> 00:42:45,030 A lot of very clinically relevant research that lies ahead. 329 00:42:46,395 --> 00:42:51,165 Kevin Messacar: I just wanna jump off on something Robin hit on with the disease by disease comment. 330 00:42:51,585 --> 00:42:53,295 Uh, cuz I couldn't agree more. 331 00:42:53,295 --> 00:43:13,575 I think as we've moved into these unbiased platforms, what's happened is they get, you know, approval for clinical use and then they get used in a widely heterogeneous population and then you get these retrospective case series describing how it impacted care or you know, the diagnostic accuracy of that platform. 332 00:43:13,995 --> 00:43:18,135 What we really need in the literature is indication driven data. 333 00:43:18,135 --> 00:43:32,895 So not just sending it at whatever time point for whatever disease process, but how does this particular platform work for culture negative endocarditis like Robin talked about, or prosthetic joint infection, um, or MSK infection in pediatrics. 334 00:43:33,165 --> 00:43:37,275 Cause I think that's gonna really inform when we should be using this test upfront. 335 00:43:37,275 --> 00:43:40,185 So not sending it after a week of, you know, getting. 336 00:43:40,660 --> 00:43:46,750 Getting no diagnosis from conventional diagnostics and when is it really clinically impactful and cost effective? 337 00:43:46,750 --> 00:43:49,540 And we are just not there yet with the data that we have. 338 00:43:49,990 --> 00:43:59,200 And in a way, it's kind of backwards to the way in which we do drug development, which you go after an indication, you show clinical impact of that drug for that indication. 339 00:43:59,685 --> 00:44:01,215 It's like we're doing that all backwards. 340 00:44:01,215 --> 00:44:03,195 We have the new tool and the new technology. 341 00:44:03,195 --> 00:44:05,145 It's approved because it works in the lab. 342 00:44:05,145 --> 00:44:06,765 It can detect what it's supposed to. 343 00:44:07,095 --> 00:44:10,005 But then we're trying to figure out what use it should have. 344 00:44:10,335 --> 00:44:15,825 And I think there's a lot of research work on the clinical research side, as Robin mentioned. 345 00:44:16,245 --> 00:44:25,485 Um, That's ripe for the picking of ID fellows and others interested in diagnostic stewardship to do that kind of backwards work of how do we take these new shiny tools and apply them to their best. 346 00:44:26,340 --> 00:44:26,550 Pratik Patel: Yeah. 347 00:44:26,550 --> 00:44:34,710 And we'll have some great references to some of the, the studies, albeit many of them are retrospective, but, um, some of the data that's out there. 348 00:44:35,310 --> 00:44:55,230 Um, and lastly, I guess, can we touch on some of the limitations of, you know, it sounds really great to have ability to shotgun or kind of, um, sequence everything that might be possible that's there., Are there some limitations that we should be, um, wary of and, and, and specific scenarios which, uh, which have come up for you guys? 349 00:44:55,410 --> 00:44:56,040 Kevin Messacar: Absolutely. 350 00:44:56,040 --> 00:45:04,035 I think everything we talked about with the syndromic panel applies to what we talk about with unbiased sequencing to an even greater extent. 351 00:45:04,035 --> 00:45:22,275 Robin talked about detection of, uh, commensal organisms, so skin organisms, gut organisms, uh, and interpretation of those results is really challenging, especially when we talk about our, um, immuno immunocompromised patient populations that may have a disrupted gut barrier. 352 00:45:22,275 --> 00:45:33,105 And, you know, we may do very sensitive plasma cell-free DNA based sequencing and detect all the things in their gut that are spilling over with trace amounts into the bloodstream. 353 00:45:33,555 --> 00:45:38,385 Likewise, in CSF, uh, there can be interference from host background as Robin talked about. 354 00:45:38,385 --> 00:45:46,065 When you do, uh, metagenomic next gen sequencing, you not only get the pathogenic sequences, but you get all the host background sequences in there. 355 00:45:46,065 --> 00:45:57,565 And if you have that tap that's either bloody or has a bunch of white blood cells that can actually interfere with the ability to detect organisms there so often you'll get uninterpretable result as far as that goes. 356 00:45:58,075 --> 00:46:08,095 And then it's the great unknown too, so you can sequence pathogens that have never been described before, that have never been described in that disease process. 357 00:46:08,455 --> 00:46:12,475 Um, that's one of the fun and interesting and challenging parts of this. 358 00:46:12,475 --> 00:46:17,845 So we were part of the initial study of the next gen platform for CSF that is being used at U C S F. 359 00:46:17,845 --> 00:46:36,910 And we used to have a weekly, we called it a tumor board, where we would go through all of the positives from the week prior from these really interesting encephalitis cases and try to interpret what had been detected on sequencing and what do you do when you have a patient with severe encephalitis and you detect a virus that's only been described in crickets? 360 00:46:36,910 --> 00:46:44,290 Is it truly a cause that we've just never found before or is it you're just detecting uh, some lab contaminant or something else? 361 00:46:44,290 --> 00:46:49,030 So there's a lot of clinical interpretation that needs to still occur with these assays. 362 00:46:49,030 --> 00:47:10,350 There's a lot of caution we need to take, but I do think every diagnostic test has its place and I think we've all had a case in which, you know, we haven't been able to get to the bottom of it, and we send a sequencing assay and all of a sudden it becomes clear like there's something that's been there that either we didn't think about, or it's so rare that it wouldn't have been on our radar and really improves the care of that patient. 363 00:47:10,350 --> 00:47:13,950 So I'm one who believes that every diagnostic test has its place. 364 00:47:13,950 --> 00:47:19,950 We just gotta figure out the best way to use it in the best situations and make it work for us the best of our ability. 365 00:47:20,700 --> 00:47:23,970 Robin Patel: Yeah, I, I couldn't agree more, I would say. 366 00:47:24,840 --> 00:47:30,060 The sequencing based tests are the hardest tests that I've ever o offered. 367 00:47:30,480 --> 00:47:35,580 I think, uh, it's really helpful to interpret results in the clinical context. 368 00:47:35,580 --> 00:47:40,140 And when you're sending things off to a reference lab, that can be complicated. 369 00:47:40,230 --> 00:47:51,970 We do tests on our own patients here and other people's patients, and I mean, the nice thing about testing our own patients is I can really look at what's going on with the patient when I'm sending those results out. 370 00:47:52,450 --> 00:47:59,530 I hope maybe in the future there's a way of sharing some background clinical data, um, on the patient. 371 00:47:59,530 --> 00:48:00,580 I agree with Kevin. 372 00:48:00,580 --> 00:48:18,420 It's, it's nice to know what people are looking for, but you really wanna understand, you know, is what I am seeing, in any way, shape or form, possibly fitting with what you're seeing because sometimes you don't know what you're looking for exactly when you're running these assays. 373 00:48:18,420 --> 00:48:21,060 So I think we're learning a lot. 374 00:48:21,420 --> 00:48:29,100 Um, we've discovered new organisms and new diseases along the way, and that will probably continue to happen. 375 00:48:29,100 --> 00:48:30,990 So it's really exciting. 376 00:48:30,990 --> 00:48:39,105 I, I definitely think these assays play a role for some patients, and we have to figure out who those patients are. 377 00:48:39,555 --> 00:48:55,260 And many times they're today being run as a test of last resort, which is okay because we're learning, but probably during the career of some of the ID fellows, these will become like a first line test so we can get the results back more rapidly. 378 00:48:55,260 --> 00:49:06,120 And I think you'll see improvements, I hope, in sequencing, uh, technology that will drive costs down because some of these tests are still very expensive. 379 00:49:06,690 --> 00:49:09,690 Um, and you know, that's a problem in healthcare. 380 00:49:09,840 --> 00:49:18,270 And then, results are not necessarily rapid, not rapid in the way, you know, some of the multiplex panels are delivering one hour, uh, results. 381 00:49:18,270 --> 00:49:28,830 So, um, you know, look, look forward to learning more and also seeing technology and, um, clinical understanding improvements over time. 382 00:49:29,595 --> 00:49:39,045 Kevin Messacar: And I just wanna put in a plug along those lines, uh, for an active diagnostic stewardship approach to the use of these new tests as they're rolled out. 383 00:49:39,045 --> 00:49:52,815 A test that looks for anything sounds great to most clinicians, and I think for the ID fellows on the call, you probably actually know more about these assays than most even experienced clinicians who aren't in the ID realm. 384 00:49:52,815 --> 00:49:52,964 And. 385 00:49:53,640 --> 00:50:07,950 Lots of institutions who've decided to roll out these tests have rolled them out with some hands on approach of requiring approval, either by the ID team and an ID consult or the clinical micro lab director to review the case ahead of time. 386 00:50:07,950 --> 00:50:20,045 So you don't get stuck in that situation either where you're sending it in a very low utility situation and wasting a whole lot of money or interpreting a test result that you never really should've sent that test in the first place. 387 00:50:20,045 --> 00:50:38,465 So I think thinking about from the micro lab and clinical ID standpoint, how to roll out these tests and potentially putting in a few more handholding, uh, ways to kind of guide their best use is probably the right way to do it, at least until we get better data to drive their use. 388 00:50:38,465 --> 00:50:46,205 Like Robin said, my goal hopefully is someday we have great data that says, for this indication, send it the day that patient comes in and we're gonna improve their care. 389 00:50:46,420 --> 00:50:53,380 But until we get there, there probably needs to be a little bit more infectious disease or, or lab involvement in the use of these tests. 390 00:50:53,830 --> 00:50:54,490 Sara Dong: Yeah, that's perfect. 391 00:50:54,490 --> 00:50:58,780 That's exactly what I was gonna say too, is plugging diagnostic stewardship. 392 00:50:58,780 --> 00:51:05,260 And I figured it was beyond the scope of us talking today about how different labs are approaching offering it to patients. 393 00:51:05,470 --> 00:51:11,700 Cuz I think it's very institution dependent how they're restricting or, or allowing people to send the test. 394 00:51:12,270 --> 00:51:20,460 Robin Patel: Now, one strategy that we've used at our place is, especially for the tissues and fluids, that there may be just one chance to collect them. 395 00:51:20,460 --> 00:51:36,730 Say intraoperatively is we have a pathway to collect and hold specimens so that then we can look at the other test results that are coming back rather quickly, including culture and decide whether we should move this specimen on, uh, to more sophisticated testing. 396 00:51:37,060 --> 00:51:53,145 Pratik Patel: Oh, one thing that could be fun, I don't know if anybody has any great future goggles, but like, are there tests that are coming down the pipeline that we should be aware of or things that ID fellows will see at some point in their careers coming down ten five years. 397 00:51:53,545 --> 00:52:11,850 Robin Patel: Yeah, so I think, um, we talked about bacterial detection, but beyond bacterial detection, ID fellows need to know what treatment to recommend, and at least for bacteria, uh, you know, that involves typically antibiotic susceptibility testing. 398 00:52:11,855 --> 00:52:14,910 But if you don't have an isolate, you can't really do that. 399 00:52:14,940 --> 00:52:28,500 However, If we sequence deep enough, if there's enough organism there, we can recapitulate the bacteriums, uh, chromosome and extra, uh, chromosomal genetic elements, plasmids and so forth. 400 00:52:28,505 --> 00:52:37,800 And if we know how to analyze that data and go from genomic data to phenotype susceptibility prediction, then we should be able to get all the way there. 401 00:52:37,800 --> 00:52:39,990 This is very futuristic, right? 402 00:52:39,990 --> 00:52:47,630 And it's probably going to be contingent on having enough organism in the sample to actually interrogate the genome in that way. 403 00:52:48,080 --> 00:52:55,640 But I believe it will be possible in the future to get susceptibility information as well, which hopefully people are excited about. 404 00:52:55,850 --> 00:53:05,180 Kevin Messacar: And I think from all the ID fellows perspectives who all know that by the end of ID fellowship you become an expert in oncologic diagnoses and rheumatologic diagnoses. 405 00:53:05,660 --> 00:53:16,444 I'm really interested in the group of patients who come in looking, by all means, like they have an infection, but even our best unbiased, deep sequencing, can't find an infectious process. 406 00:53:16,924 --> 00:53:19,444 How we can better classify what's going on in those patients. 407 00:53:19,444 --> 00:53:31,240 And one of the interesting part of, uh, Metogenomic next Gen sequencing is not only does it generate, you know, sequences that we can look for pathogen in, but also sequences out the RNA transcriptome. 408 00:53:31,240 --> 00:53:46,569 And so if you categorize the genes that are being turned on in the host, there's actually some machine learning work to say that response looks like an autoimmune response or a viral pathogen host response or a bacterial pathogen host response. 409 00:53:46,569 --> 00:54:01,605 And that might be enough to at least send us down the right pathway in those patients that we can't get an organism, but we need to know whether, you know, the neurologist should immunomodulate that patient or we should stick on empiric therapy cuz we might not have grown or detected an organism. 410 00:54:01,610 --> 00:54:15,055 So I think we might be actually stepping back from the precision diagnostics of getting a specific name for something in those patients and just saying, uh, better part of valor maybe is at least put them into a bucket of autoimmunity versus infection versus other. 411 00:54:15,415 --> 00:54:19,050 Sara Dong: Thanks again, Tik Robin, and Kevin for joining Febrile today. 412 00:54:19,260 --> 00:54:21,390 I thought this was a great overview. 413 00:54:21,930 --> 00:54:28,050 Don't forget to check out the website, febrilepodcast.com to find the Consult Notes, which are the written complements of the show. 414 00:54:28,050 --> 00:54:32,010 So we'll have links to tons of references and anything mentioned today. 415 00:54:32,610 --> 00:54:35,880 We have our library of ID infographics and a link to our merch store. 416 00:54:36,510 --> 00:54:40,800 Please reach out if you have any suggestions for future shows or want to be more involved with Febrile. 417 00:54:41,010 --> 00:54:41,820 Thanks for listening. 418 00:54:42,030 --> 00:54:43,650 Stay safe and I'll see you next time.