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Deep Dive: Microbiomes – An Update on Our Ten Tril ...
The Microbiome in ARDS and Other Critical Illness ...
The Microbiome in ARDS and Other Critical Illness States
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and I'm an associate professor at NYU School of Medicine. And I'm gonna be talking to you today about the microbiome in ARDS and critical illness. These are my disclosures and mainly related to grants that fund the work that we do in the translational lab here at NYU. And probably for this audience, I don't need to say this, but it's an important reminder that what we're talking about, sepsis and ARDS, is a very heterogeneous group. It's not that everybody has the same disease. And you can see that when you try to establish, you try to look at it from different angles, either from the imaging with the Berlin or with the type of affection that patients have, or mechanistically, if you have, if you look at the endothelial function, epithelial function, post-immune response. And this was in a nice review that was recently published. But I would add to these different ways to see the heterogeneity of the disease, that even if you look at the microbiome of these patients, you can identify very different type of patients that have different microbiome and remind you that the microbiome is a way to identify as a self as much as any other genetic information that we have. So we now have a much better understanding that there's a complex microbial community in our bodies. And this affects our immune system and it's been affected by the immune system. And it affects how pathogens that we're exposed to and invade us behave. And this has been quite a bit of a focus of study in areas where there's a lot of microbes, such as in the gut and in the upper airway environments where the microbial load is very high and there's movement of microbes throughout different pathways. But this can affect the lungs and it can affect through what is called the gut-lung axis where gut microbes affect immune system and can affect the lung, or there can be translocation of microbes that goes into the lung. But I would argue that the more frequent way in which microbes interact with the lung is through the actual exposure of microbes into the lower airways affecting the immune tone there. And that has been quite a bit of the focus of my lab and what I'm gonna be discussing here. And I would argue that we're in an unprecedented time to be able to harness this area of investigation to understand the microbial world. And this is thanks to these multiple technologies that we have that can study the microbial world and the host interaction by looking at targeted approaches such as 18S or 16S, and targeted approaches looking at the bacterial DNA or microbial DNA or microbial RNA through RNA transcription and metatranscriptomics that can also profile the host RNA in part of, or that can study metabolites and proteins, some of them that are coming from bacteria or fungi or other microbes, or that are being affected by metabolism by these other microbes. So how do we think about this and apply this in the context of our critically ill patients? So we are, as I said, there's an unprecedented time we can do this, and there's now a series of investigations and papers that have come out looking at this, where you look at patients in the ICU, where you can get all their clinical metadata from just demographics, clinical phenotypes, evolution of their disease course, imaging, medications, very complex data, but then you can take their samples and look at different methods to study. And I'm gonna give you a few examples where we look at viruses, hosts, host transcriptome, and how they are responding to these pathogens in the setting of a microbial world. So what we now understand is that there is this microbial world in the lower airways, it's changing, and it's a very dynamic system that becomes very much affected by inpatients with ARDS and septic patients. And this is because there are certain factors that are key that are occurring in these patients. These are patients that have oral tracheal intubation, which affects how much you are exposed to microbes from the upper airways, generate biofilms, and also they're impaired to eliminate microbes because you have an impaired cough and also impaired innate and adaptive host defenses. And also there are host factors, such as the ones listed here, that are very much specific of patients with ARDS and environmental factors in the setting of ARDS, sepsis, and other critical illness that will affect the microbial reproduction and the microbial environment in the lower airways. And I'm gonna give you some examples. So if you look at initial investigations looking at this, you want to, there are a few that I'm gonna highlight. So one is that there are investigations that have shown that by studying the airway microbes on these patients that are intubated, you can predict who's gonna develop ARDS. So this is a study published in Blue Journal in 2018, where they look at both smokers and no smokers. They look at the early time point samples and they were able to identify a set of microbes that were enriched on the left side in patients with ARDS that will eventually go on to develop ARDS. And some of them are pathogens such as semophilus or commonly considered pathogens. Some of them might not come as considered as pathogens. These are oral commensals, ribotella, porphyromonas, fusobacterium, rothia. These are oral commensals that are in the oral cavity. We all have it. But now when you sit in the lower airways, there seem to be associated with the development of ARDS. And I'm gonna go back to this issue. So that's why I wanted to highlight it here. But if you look at, you don't need to just look at the lower airways. There is also evidence that microbial signals can be detected in blood of patients that are septic that we can now recognize more broadly by using these culture independent techniques. We know that if you look at the prevalence of a positive blood cultures in people that have sepsis from lower airways, respiratory tract, or abdominal cavity, they have a lot of pain, a high percentage of patients have culture negative. But however, despite that, both patients with culture positive and culture negative have similar degree of mortality among patients with sepsis. So why is that? Well, maybe it's because we're not identifying those patients well. They have a lot of microbes that can be identified by using non-culture techniques. So if you look at the microbial world by using some of these sequencing approaches, you're gonna, this paper stratify patients into confirmed sepsis, sepsis with culture negative or sepsis suspected versus those that do not have sepsis. And sure, there are some differences in the bacterial burden that is detected in the blood of these patients. So patients with sepsis with culture positivity, they tend to have more bacterial DNA that you can identify circulating. But there's also a big dynamic range. Both in those that have culture positive and culture negative, you can see patients that have very high bacterial DNA circulating, and this is clearly higher than those patients that don't have sepsis. So going back to the airways. So this is important, no? Because if you look at the bacterial burden in the lower airways, it seems to be associated with outcome among patients with ARDS. So this is the paper that probably Bob Dixon mentioned early on in this series of talks where he measured the bacterial burden in the lower airways of patients with ARDS, stratified those that have high bacterial burden versus those that have low bacterial burden, and identified that those patients with high bacterial burden tried to have worse prognosis, and tried to have some differences in the composition. If you look at the comparing patient with ARDS versus those without ARDS, he started identifying that there is a clear enrichment with this enterobacteria cei, which is an interesting microbe because these are gut-related microbes that are now being seen in the lower airways, suggesting that there might be some bacterial translocation from the gut sitting into the lower airways that could not be recognized by culture techniques. So this data is not an isolation. So there's now two other papers, at least that they have shown that high bacterial burden and high fungal burden is associated with poor outcome among patients with ARDS. These are two examples of critically ill COVID-19 cohorts. One is a paper that we publish in Nature Microbiology showing that patients that die have high bacterial burden, but measured by 16S, and this is a European group showing that both the bacterial burden group and the high fungal group have worse prognosis, more prolonged mechanical intubation and mortality. So that starts telling us that looking at the microbial world and using these techniques can inform something that we didn't know before. So we have to study this in the setting of the pathogen of reducing the infection in these patients. So if you center on the pathogens, there's clearly important findings by using these techniques. So this is a study that we did looking at patients with critically ill COVID-19 that they were all intubated, and we measured the viral burden for SARS-CoV-2 in their upper airways and in their lower airways. So where these microbes are, are very important. So the data here shows two groups, one where the viral burden for SARS-CoV-2 was greater in the upper airway than in the lower airway, whereas there's this other group that the viral burden in the lower airways was greater than in the upper airway, saying that the burden in two different topographic locations do not correlate that well. And if you look at association with prognosis, you can look at the viral burden in the upper airway and the viral burden in the lower airways. You can see that only the viral burden in the lower airways is associated with prognosis. And this data can then be extended by looking at some of these culture-independent sequencing techniques. So if you look at the RNA sequencing data that we produce here, with what allows us to look at the metatranscriptome and the RNA viral. And this data concentrated on the RNA viral shows that there's difference in composition of those patients that died as compared to those patients that did better. And the difference is mainly driven by SARS-CoV-2 and region with SARS-CoV-2 by this identified by this RNA sequencing method. Which is unbiased and also shows similar to a targeted approach for SARS-CoV-2. So then the obvious next thing is this also promoted by a problem with how the host responds to the pathogen. In this case, SARS-CoV-2 prior studies have shown that levels of SARS-CoV-2 antibodies correlate with COVID outcome. These are some longitudinal data looking into systemic antibody response on association with prognosis, with severity of disease, I'm sorry. And we show that this is the levels of viral loads in the lower airways is associated with prognosis. Now, so patients that died have a blunted anti-spike IgG immunoglobulin response. Why is this important? Because we know that the adaptive immune response is critical in this disease. But what we didn't know is how important is the local lower airway immune response. So this is data showing how things are for different sub-fractions of epitopes of the virus. And both IgG in anti-spike and anti-RBD were significant associated with outcome. But if you look at the same measurements in the blood, you don't see, at least with this sample size, you don't see a very statistically significant association with mortality. But if you look at the lower airways is when you start seeing these differences in a blunted adaptive immune response associated with poor outcome. So understanding the local environment, the local immune environment is very important. But here I focus on the pathogen and the adaptive immune response to the pathogens. But as I said at the beginning, these pathogens are not on isolation. So this is a co-occurrence network of the microbes that you can identify in these patients with ARDS due to SARS-CoV-2 infection. And the network highlights microbes that are co-occurring, that they go along, some that are positively associated with worse outcome in red, and some that are negatively associated with worse outcome in blue. And you can see that there's a lot of complex associations and that there's not just SARS-CoV-2 associated with worse prognosis, but also many others ones are associated with worse prognosis. This is important because before, when we tried to understand what was leading to the worst outcome among patients infected with this disease, there were a couple of fundamental theories. One that was saying, it's probably because they're getting infected by another respiratory pathogen. And this is based on some evidence from the 1918 influenza pandemics that suggested that that was so co-occurring. There's data on the H1N1 2009 pandemic suggesting that patients were acquiring novel respiratory infections by a secondary pathogen in the setting of the influenza. Infection. The other leading theory was that there was a cytokine storm that was really this aberrant host immune response that was really driving the poor outcome in patients with SARS-CoV-2. So we thought that we wanted to use these methodologies that allows us to harness the complexity of the microbial and the host system to better understand this and to test the hypothesis. In this patient was critically ill SARS-CoV-2 infection. Is the poor outcome associated with secondary infection, with viral toxicity or with an aberrant inflammatory response? So that's what we seek to do by sampling via bronchoscopy a large number of patients that were intubated affected by SARS-CoV-2 infection. And we could use these methods to profile the metatranscriptome via RNA-seq that allows us to study the microbial world by characterizing the RNA viral, but also the metatranscriptome of DNA viruses, bacterial and fungi, as well as profiling the host transcriptome. In parallel, we did the DNA sequencing approach to characterize the metagenome, the DNA viral and bacterial and fungal metagenome. So in parallel, we were collecting the culture data on these patients. And if you look at patients that have better or worse outcomes, again, all critically ill intubated patients, ARDS patients infected with SARS-CoV-2, we profile those that have better outcome defined as they were extubated within 28 days and remain alive versus those that have more prolonged mechanical ventilation more than 28 days or those that died. And if you look at the culture data, you don't see anything that is consistently going one way or another. So we did with culture data throughout the whole hospitalization date. And this is a little bit affected by the survival effect. The more time you are in mechanical ventilation, the more of these cultures that you're gonna get. So we restricted the analysis to the two week period. And here in the two week period analysis, you don't see anything clearly associated with worse or better outcome, except I would highlight this issue that both in the hospitalization date and for the two weeks, there was some interesting signal with those that were identified oral bacteria as a contaminant that is commonly regarded as a contaminant seem to be associated with some differences in outcome. So this goes back to then looking at the bacterial burden in these patients, patients that died have a higher bacterial burden. And if you look at the metagenome data and the metatranscriptome data, you can start seeing what are those microbes that were unreaching those that have worse outcome that either died or that remain on the mechanical ventilation for more than 28 days. So if you look at this graph, what's in color are those that are statistically significantly associated with this outcome. And in the size of the bubble is related to how much there is the relative abundance of this. And you can see here the labels. And for the most part, you're not gonna identify that those that became significant are the ones that we suspect as common respiratory pathogens. Now, you don't see pseudomonas, you don't see acinetobacter, you don't see the usual respiratory pathogens that we see in critically ill leading to VAP or other conditions. You actually see a lot of other stuff. And I'm gonna highlight this big bubble that you see here, which is mycoplasma salivarium, which is an interesting organism on its own that is associated with worse outcome, more higher mortality or more prolonged mechanical ventilation. And it's interesting because this mycoplasma salivarium is an oral commensal and it's a mycoplasma. So it means that in these patients that are commonly are treated with antibiotics, the antibiotics may not be treating this mycoplasma because it's an atypical microbe. And so what we can then do, we can use this to identify microbial signatures associated with prognosis. We can, in parallel, do look at host signatures associated with prognosis and then combine the two to develop better models that understands the prognosis of these patients. So this is not a novel idea. Actually, before the pandemic, the group in San Francisco published this wonderful paper where they were looking at two cohorts of patients that have suspected lower respiratory tract infection and they have lower with samples to do metagenome work, metatranscriptome work and host transcriptomics. So the first thing that I want to show is that in these patients that have a suspicious suspicious of lower respiratory tract infection, there's a lot of pathogens that you can identify by using culture-independent methods that otherwise you would not have been able to identify. Two, if you look at the host transcriptome, those patients that have lower respiratory tract infection, which are the red, they tend to have a different pattern of host transcripts that are upregulated or downregulated. So there's this regulation of the host transcriptome that can be used to, that is associated to identify lower respiratory tract infection among these patients that are intubated. You can then look at each of these in parallel. So if you look at this diversity of the microbes, those that have lower respiratory tract infection have lower diversity. And if you look at the compositional data, you can start building models based on the microbiome data to get a pretty accurate prediction pattern. For those that have lower respiratory tract infection, you can use the host transcript to do the same thing. And you can combine host predictions markers with microbial prediction markers to try to establish a model for predicting lower respiratory tract infection that has greater accuracy. So we did something similar with the SARS-CoV-2 critically ill patients. So if you look at the host transcriptomic data, we identified a lot of differentially regulated genes that can then be concentrated into pathways that were upregulated in red, downregulated in blue, associated with worse prognosis. Interestingly, if you look at the pathways, you don't see the common pro-inflammatory, cytokine storm type of pathways that people have described. And maybe this is because most of those studies have focused on comparing patients with very advanced disease or very severe disease with mild disease, with no disease. Here, everybody has the same severity and we're looking at the ability to predict outcome among patients with similar disease severity. And we see some interesting pathways such as eryptosis and surgery, which are key pathways that regulates the response to viruses, but we don't see the classical pro-inflammatory IL-17, IL-8, TNF, that sort of pathways. But we did identify some interesting pathways with interferon signaling that seems to be dysregulated. So these are a module analysis where we identify key modules that were dysregulated associated with prognosis. We see some other modules related to basic cellular functions such as selection, stress, respiratory, electro transport function, that then we can build into models for prediction. So each data set can be used to develop a model of prediction of mortality among critically ill patients suffering SARS-CoV-2 infection. So you can use the metatranscriptome data, you can use the metagenome data, you can use the host transcriptomic data. Each of them will actually find a way to have a predictive model, but then you can start combining them. So if you build a combined model between the host, the metatranscriptome and the metagenome, you can start looking as a way to predict those patients that have worse outcome. You wanna predict these patients with disease. You want them to capture among these quadrants. You don't want to have anybody in this lower quadrant that are low risk for this. And you can see if you combine metatranscriptome with metagenome, there are still some patients that end up having high, that end up having mortality that are falling in this quadrant. If you combine, if you develop a model combining the metagenome and the host transcriptome, you also still have a few that do not, a few with mortality that end up falling in the lower risk model. But if you now combine the metatranscriptome with the host transcriptome, you can see that every patient that have worse outcome are falling within the high risk scores. So you can harness that to identify what are the key molecular signals identified in the microbes or the host that are useful to re-stratify patients and determine who's gonna have worse outcome. You can then extract from this combined module, microbes and host, extract the host signatures. What are those key host signatures that are failing, that are dysregulated in patients that have worse outcome? And you can start seeing these genes that fall into pathways. Some are well known to probably be important in this condition. Some related to adaptive immune responses, some with interferon responses. These different genes fall into categories of disease and functions in the ingenuity pathway analysis that are clearly related to infection by RNA viruses, but also by other pathways of interest. So this is, as I said, this is not something that we are the only one finding. This is another paper that recently published in microbiology that I referred before they were looking at the presence of microbial signatures circulating. If you look at the host signatures, they also find that those that have increase in viral infection rates, they have dysregulated of similar pathways as I was telling you, a lot of interferon pathways. So why is this all important? Well, I think there's still a lot of heterogeneity in this disease that needs to be explained now. And this is mortality data from the first year or year and a half of the pandemic. And we know that among patients that are admitted to ICU and that they are intubated, there is a high rate of mortality. And the mortality bar is clearly among different age group. But we don't have a good way to predict who's gonna have worse outcome. If you look at how the virus is affecting different organs, there's a lot of heterogeneity among patients. So this is a series of 26 autopsy cases where they look at how the virus is affecting different individuals that end up dying. Some that have clearly a lot of intrapulmonary distribution, but some that each different organs has been affected in different ways. Some with much more clearly enrichment by SARS-CoV-2 infection, some by less. So just looking at the pathogen can give you a lot of heterogeneity. What I hope I convinced you is that there's more than just that pathogen. There's a lot of other microbes involved. In ARDS with the changing in the environment is clearly now that there are many reasons by which microbes in the lungs are gonna be affected. So one example is that if you just look at the protein concentration in the bronchial virulovascular patient with ARDS, there's many low higher concentration of proteins in those lower airways. These are nutrients that microbes use. The carbon sources that microbes use. So if you have local inflammation and you have increased alveolar edema, you are changing your nutrient environment, promoting or giving more carbon sources for microbial growth. So therefore it's not surprising that one is in the setting of ARDS, you can start identifying one gut-associated bacteria that comes from bronchial translocation and two, a combination of mixed oral commensals that are now being more exposed to because of the conditions in which patients are intubated and they cannot cough. But also because now you have a microbial environment that is promoting the growth of these microbes. So this is a complex series of interaction which has led to now a framework for better understanding of what's happening with this microbial world in the setting of sepsis and in the setting of ARDS. So there's many reasons by which the microbiome in these patients with sepsis and ARDS is being disrupted. I mentioned some of that. I should mention that obviously antibiotics, changing diet complicates the picture on these patients but only contributes to the disruption of the microbiome. And I hope I show you data that kind of gives you a better understanding that these microbes can actually contribute to the dysregulation of the immune response and that can lead to further organ dysfunction. So this ultimately is a vicious circle that we have to do something to try to break. And that goes beyond what we're doing right now that is pretty much focusing on just targeting the pathogen and maintaining patients alive. And that will require a consideration that will affect other components that can contribute to the disruption of the microbiome such as thinking about what do we have to do with these factors? Can we restrict which antibiotics we use or the amount of antibiotics that we use? Can we affect the kind of source of infection of carbons that these microbes are exposed to? So these are importance of related to fiber intake type of diet that patients are exposed to or can we affect directly the microbial compositions through the use of transferring distinct microbiota such as FMT approaches or fecal transplantation also to affect the gut microbiome or can we do something similar but people have exposed much less that transferring a healthier microbiome to the lower airways. So if we can maintain and improve that there's a chance to have a different approach to treating this condition, helping the immune response to become more efficient against the pathogen but also less damaging through inflammatory injury. Overall, what we know is that the lung microbiome it's very disruptive in the setting of SARS-CoV-2 and this is complex. So if you look at different microbial communities in different topographical regions the lung have definitely a very different composition than what you see in the upper airways or in the gut. In many ways the lungs are more closely related to the oral microbiota but it more distantly can be affected by the nasal and the gut. And this is what happens normally in people that are not in these critical conditions. This interaction between these different mucosa I think it's pretty clear now that the association between these different topographical regions gets disruptive. And there's much more translocation of microbes from the gut to the lower airways and then the topographical differentiation of microbial microbes from the upper airway to the lower airways gets also affected. And I think if we can kind of harness the complexity and start looking at causal pathways that are related to the inflammatory injury we are gonna find a different way in which we can approach this disease and intervene to promote the healing process and how the host is interacting with the pathogens. To summarize, I hope I convinced you that the lower airways are not sterile but rather frequently exposed to microbes and their products and this is much more dysregulated and much more prominent in the setting of ARDS. On top of these conditions such as ARDS and critically ill can further contribute to the changes in the lower airway microbial world. There's more promotion, there's more carbon sources available for them. There are effects in the way the lower airway immune system works that makes it to respond differently to microbes. And this affects the ability of microbes to see the lower airways or the ability to produce different factors and metabolize some proteins that can affect the host. I think dissecting those functions, those microbial functions will uncover key factors that may lead to the inflammatory damage that we see in these conditions but also how the host is responding to pathogens. I want to finish by acknowledging the people working in the lab the many contributions that we have across different institutions and the funding that supports the work presented here. And I'll be looking forward to the discussion and hopefully we'll get to address any questions that you guys may have. Thank you very much.
Video Summary
The speaker, an associate professor at NYU School of Medicine, discussed the role of the microbiome in acute respiratory distress syndrome (ARDS) and critical illness. They highlighted the heterogeneity of diseases like sepsis and ARDS and emphasized the importance of understanding the complex microbial community in the body and its impact on the immune system and pathogen behavior. The discussion focused on the gut-lung axis, microbial translocation, and microbial interactions in the lower airways. The speaker presented research findings on the microbial burden in ARDS patients, the association of specific microbes with poor outcomes, and the potential for microbial signatures to predict prognosis. They emphasized the need to consider factors disrupting the microbiome, such as antibiotics and diet, and suggested potential interventions like fecal microbiota transplantation. Overall, the talk stressed the importance of unraveling the microbiome's role in inflammatory injury in critical illnesses to improve treatment approaches.
Asset Caption
Leopoldo N. Segal
Keywords
microbiome
Acute Respiratory Distress Syndrome
critical illness
sepsis
immune response
disease progression
microbial communities
lower airways
ARDS
gut-lung axis
microbial community
immune system
microbial burden
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