The Gut-Lung Axis in Critical Illness: Microbiome Composition as a Pr edictor of Mortality at Day 28 in Mechanically Ventilated Patients

doi:10.21203/rs.3.rs-3232111/v1

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Research Article

The Gut-Lung Axis in Critical Illness: Microbiome Composition as a Pr edictor of Mortality at Day 28 in Mechanically Ventilated Patients

https://doi.org/10.21203/rs.3.rs-3232111/v1

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published 18 Dec, 2023

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Background

The gut-lung axis is a crucial interface in the complex interplay between microbial communities and their human hosts. Despite being two of the most important microbial populations in the human body, there is still a significant knowledge gap regarding the distinct variations between lung and gut microbiota and how they affect clinical outcomes in critically ill patients.

Methods

A retrospective observational cohort study was conducted in the intensive care unit (ICU) of an affiliated university hospital. Sequential specimens were collected from two anatomical sites, namely the respiratory and intestinal tracts, at two time points: within 48 hours and on day 7 after intubation. These specimens underwent comprehensive analysis to characterize microbial profiles and assess concentrations of fecal short-chain fatty acids (SCFAs) using 16S ribosomal RNA (rRNA) gene sequencing. Lung and gut microbial diversity, along with indicator species, served as the primary predictors in this investigation. The primary outcome of interest was survival status at 28 days after mechanical ventilation.

Results

A total of 247 specimens, comprising 123 bronchoalveolar lavage and 124 fecal samples, were meticulously collected from 62 critically ill patients in the intensive care unit (ICU). Our analysis revealed significant differences in the composition of respiratory and intestinal microflora between the deceased and survivor groups. Specifically, the alpha diversity of respiratory and intestinal microorganisms was significantly reduced in the deceased group with prolonged mechanical ventilation compared to the survivor group (p < 0.05). Furthermore, there were significant disparities in respiratory and intestinal β-diversity observed among the surviving and deceased groups (p < 0.05). Interestingly, Enterobacteriaceae exhibited enrichment in the respiratory tract (p < 0.05), while Enterococcaceae were enriched in the intestine (p < 0.05) in the deceased group. Notably, lower concentrations of short-chain fatty acids (SCFAs) were detected in the fecal samples from patients who succumbed. Leveraging a multivariate Cox regression analysis, we established robust associations between lung microbial diversity and Enterococcaceae abundance in the intestine with a risk ratio for mortality at day-28 adjusted for confounding factors (aHR = 0.773; CI:95%: [0.652 ~ 0.916], p = 0.003; and aHR = 1 .022; CI:95%: [1 .008 − 1 .037], p = O.OO2). These findings underscored potential clinical significance of microbial profiling to predict critical outcomes among ICU patients.

Conclusion

Following mechanical ventilation in critically ill patients within the ICU, we observed a significant decline in respiratory microbial diversity, accompanied by an imbalance in the intestinal flora, resulting in the predominant proliferation of Enterococcaceae. This dysbiotic shift holds predictive value for adverse clinical outcomes. Furthermore, the levels of short-chain fatty acids (SCFAs) serve as an indicator of the balance between commensal and pathogenic flora within the gut, signifying its protective role against respiratory diseases. Such insights could pave the way for future strategies aimed at mitigating adverse outcomes in critically ill patients.

intestinal microbiota

lung microbiota

gut-lung axis

intensive care unit

longitudinal study

The microbiota of healthy individuals exhibits remarkable stability and independence over time, primarily due to the distinctiveness of various anatomical regions, including the intestinal, lungs, nasal cavity, and vagina[1–3]. The microbiota residing in the intestinal and respiratory tract exist in a state of mutual restraint and balance, performing essential functions in maintaining the host's nutritional metabolism, structural integrity, and defense against pathogenic invaders[4–7].

The intestinal and respiratory microbiomes are interconnected via the gut-lung axis[8–11]. The immune responses of the mucosal and distant organs, including the lungs, are regulated by the intestinal microbiota[12, 13]. Metabolic products such as SCFAs, the neuroendocrine system, and various pathways participate in the regulation of the body's immune and inflammatory responses by the intestinal microbiota[14–16]. Microbiological research is currently focused on particular strains of gut commensal microorganisms referred to as "probiotics," which are believed to confer positive effects on host immunity and/or act against pathogenic microorganisms[17–19]. Emerging research has demonstrated that a decrease in the production of SCFAs by gut commensal bacteria can stimulate inflammation in the lungs[20]. Nevertheless, our knowledge of this interaction remains constrained[21].

The ICU represents the primary treatment facility for critically ill patients who undergo various treatments, including antibiotic therapy, mechanical ventilation, continuous blood purification, immunosuppressive therapy, and other non-invasive interventions[22]. Consequently, the microbial composition and diversity of ICU patients are affected, often resulting in dysbiosis and multiple infections[23–25]. Changes in the patient's microbiome may influence potential disease progression, drug response, systemic inflammatory response, and organ dysfunction in critically ill patients[26–29]. There is a significant interplay between the respiratory and intestinal microbiota that impacts human health, particularly disease severity and mortality[30–33]. A profound comprehension of this interplay is vital for understanding the underlying mechanisms and implementing preventive measures to enhance disease prognosis[34, 35].

Microbiome research in the intensive care field is still in its nascent stage, largely due to the complex clinical features of ICU patients, resulting in highly heterogeneous microbiome composition[36]. Moreover, most studies only analyze microbiome samples from a single anatomical site or non-longitudinal samples, which limits our comprehension of dynamic changes in the microbiome of critically ill patients[37–39]. To provide a comprehensive understanding of the gut-lung axis in critically ill patients, we conducted a retrospective observational study analyzing the microbial community composition of two anatomical sites (the intestinal and respiratory tract) at two time points (within 48 hours and 1 week after mechanical ventilation) and detecting early intestinal microbial metabolites such as SCFAs. We aim to delineate the dynamic changes and interactions between the intestinal and respiratory microbiomes and correlate them with the 28-day clinical outcomes of patients.

Patient Enrollment

We conducted a prospective observational cohort study at the ICU of Fujian Medical University Union Hospital, which caters to medical, surgical, neurosurgical, and trauma patients, treating approximately 1,000 patients every year. The ethics committee of Fujian Medical University Union Hospital approved this study, and all participants or their guardians provided informed consent. All subjects voluntarily signed the informed consent form, which met the requirements of the World Medical Association Declaration of Helsinki (2013), the Universal Declaration on Bioethics and Human Rights adopted on October 19, 2005, and the Universal Declaration of Human Rights adopted by the United Nations General Assembly on December 10, 1948. The study protocol was approved by the ethics committee of Fujian Medical University Union Hospital. All research was performed in accordance with relevant guidelines and regulations.

From December 2020 to December 2022, we recruited adult patients who received invasive mechanical ventilation due to non-pulmonary and non-abdominal infections, were intubated for less than 48 hours, and were expected to require mechanical ventilation for more than 4 days. We excluded patients who were pregnant, had received antibiotic, immunosuppressive drug, or glucocorticoid therapy in the past 3 months up to the time of enrollment, refused to participate, or lacked adequate clinical data. We collected all samples within 48 hours after initiating invasive mechanical ventilation via tracheal intubation. Patients with poor or inadequate specimen quality due to delayed fiberoptic bronchoscopy, constipation, etc. were considered timeout and were excluded. During their ICU stay, we requested patients to provide two pairs of BALF and stool samples, and sampling ceased automatically once the patient was discharged or no longer required mechanical ventilation. If no sample was available during the specified time period, the next day's sample was used (still within 48 hours ± 24 hours of mechanical ventilation and 1 week ± 24 hours). The detailed procedure is shown in Fig. 1.

Data Collection

Age, sex, basic clinical information, antimicrobials, and routine cultures were extracted from medical records. As all patients were sedated during intubation, a Glasgow Coma Scale (GCS) score could not be determined for the Sequential Organ Failure Assessment (SOFA). Acute respiratory distress syndrome (ARDS) was defined according to the Berlin criteria and evaluated by two attending physicians. Clinical outcomes, including 28-day mortality, duration of mechanical ventilation, and length of stay in the ICU, were also documented.

Sample collection

To facilitate clinical diagnosis and treatment, we obtained approximately 10–20 ml of bronchoalveolar lavage fluid (BALF) through fiberoptic bronchoscopy, while simultaneously collecting stool samples. The alveolar lavage fluid was sent for conventional culture, and the remaining quantity (usually around 5 ml) along with feces were frozen at -80°C and promptly sent for sequential 16sRNA testing.

Alveolar lavage fluid samples

Lower respiratory tract samples can be used to study the lung microbiota of an individual[40, 41]. The site for lavage should be selected based on chest imaging, prioritizing the most prominent or rapidly advancing area. To perform bronchoalveolar lavage, the bronchoscope's top is fixed at the opening of the target lung segment or sub-segmental bronchus. 0.9% sterile saline is then rapidly injected via syringe through the bronchoscope's operating hole. A total of 60–120 ml volume with 3–4 routine lavages should be collected. Prior to entering the lavage site, it's important to ensure that the negative pressure tube is properly connected to the recovery container. After the injection of normal saline into the target lung segment, the BALF should be immediately suctioned and recovered, with a recovery rate of at least 30% and a lavage time of less than 5 minutes. Tube 1 is usually discarded, and tube 2 is submitted for microbiological testing.

Stool samples

Be sure to wash your hands and wear gloves before handling bedpans and stools. With the sampling spoon of the stool collection tube, pick up the interior of the middle part of the stool (the surface of the stool contains exfoliated intestinal mucosa cells). Use sterile centrifuge tubes to weigh approximately 0.5-10g of stool samples per tube.

DNA extraction, Bioinformatics analysis and Taxonomy annotation

We isolated bacterial DNA directly from BALF and fecal samples, targeting the V3 + V4 region of the ribosomal RNA gene using PCR[42]. Microbial DNA was extracted using the HiPure Soil DNA Kits (or HiPure Stool DNA Kits) (Magen, Guangzhou, China) according to manufacturer’s protocols. The resulting amplicons were purified and pooled in equimolar before being subjected to paired-end sequencing (PE250) on an Illumina platform according to standard protocols. The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database. Adapters and low-quality reads were removed as they can affect downstream analyses, leaving us with a set of high-quality clean tags[43]. These tags were then clustered into operational taxonomic units (OTUs) with at least 97% similarity using the UPARSE (version 9.2.64) pipeline[44]. We removed any chimeric tags using the UCHIME software and finally obtained a set of effective tags for further analysis. To represent each cluster, the tag sequence with the highest abundance was selected.

Microbial analysis and Statistical analysis

After processing the sequencing data, we utilized the vegan software package version 2.5.3 to analyze microbial ecology. The abundance statistics for each taxonomic group were visualized using Krona version 2.6[45], and the stacked bar plot of community composition was generated using the gplot2 package version 2.2.1 in the R project[46]. The Shannon index was computed using QIIME version 1.9.1. Furthermore, we conducted a Venn diagram analysis using the vegan package in R, considering a species present in a group only when the mean tag count of the OTU/species was greater than 1 in that group. To generate the species abundance heatmap, we employed the Pheatmap package in R, which displays species with a relative abundance of at least 0.1% in at least one sample. We calculated the Bray-Curtis distance matrix using the Vegan package version 2.5.3 and visualized it using a non-metric multidimensional scaling (NMDS) plot and a partial least squares discriminant analysis (PLS-DA) model[47]. Moreover, we computed Adonis (also known as Permanova) using the Vegan package version 2.5.3 in the R project[48]. We performed between-group species comparisons using Welch's t-test and Wilcoxon rank sum test in the Vegan package version 2.5.3, as well as Kruskal-Wallis H test in the same package.

Quantitative variables are presented as medians and interquartile ranges (IQR) and were compared with the use of the Mann-Whitney Wilcoxon rank-sum test. Categorical variables were expressed as the number of patients (percentages) and were compared with the use of the chi-square test or Fisher's test. Univariate and multivariate Cox proportional hazards models were used to analyze the risk factors for 28-day mortality. Univariate analysis was performed first, and only variables with a p-value < 0.05 were included in the multivariate analysis.

Bioinformatics analysis Methods of SCFAs and Statistical analysis

We measured the levels of SCFAs in stool samples within 48 hours of mechanical ventilation using Waters Acquity UPLC and AB SCIEX 5500 QQQ-MS. We used multivariate statistical analysis OPLS-DA VIP values and univariate statistical analysis T-test P values to identify significant differences. Those with a p value of T test<0.05 and VIP ≥ 1 were considered differential lipid between two groups.

The study enrolled 62 patients, of whom 27 died (44%) and 35 survived (56%) after 28 days of mechanical ventilation. Each patient provided two sets of BALF and stool samples, one within 48 hours (Group 01) and the other at one week after mechanical ventilation (Group 02). Table 1 displays the clinical characteristics of the patients. Importantly, there were no significant differences in clinical features, culture results, or antibiotic use between the deceased and survivor groups.

Table 1

Baseline characteristics and outcome.
	Deceased(27)	Survival(35)	P
Age	63(53, 69)	58(45, 75)	0.121
length of ICU stay(d)	17(11, 23)	13(8, 23)	0.286
Enteral nutrition time(d)	2(1, 4)	2(1, 4)	0.896
SOFA	14(12, 16)	13(8, 14)	0.098
Antifungal	11	11	0.447
Sex(Male)	15	17	0.585
Emergency Surgery	11	12	0.602
Smoking history	7	6	0.400
Alcohol history	7	7	0.580
VAP	17	19	0.492
ARDS	21	22	0.206
Early enteral nutrition (72h)	20	26	0.985
Diagnosis-abdominal surgery	3	8	0.321
Diagnosis-hematopathy	3	4	1.000
Diagnosis-Rheumatic immune disease	2	3	1.000
Diagnosis-respiratory disease	9	5	0.124
Diagnosis-neurosurgery	10	10	0.480
Diagnosis-respiratory disease	0	5	0.063
complication-hypertension	4	5	1.000
complication-diabetes	4	3	0.698
complication-cardiovascular disease	5	4	0.485
complication-tumour	4	5	1.000
complication-Neuropathy	4	6	1.000
Antibiotics
Enterobacter aerogenes	6	6	0.616
Carbapenems	16	16	0.290
Cephalosporins	9	13	0.756
β- Lactam antibiotics	12	14	0.725
Tegacyclin	9	6	0.140
Aminoglycoside	0	2	0.500
Anti positive bacteria	16	16	0.290
Quinolone	3	3	1.000
Polymyxin	2	3	1.000
Anti anaerobic bacteria	1	0	0.436
BALF traditional culture
CRAB	3	7	0.491
CRKP	14	18	0.974
CRPA	6	7	0.831
MRSA	4	10	0.199
Neisseria	5	5	0.735
Stenotrophomonas maltophilia	4	4	0.719
Burkholderia cepacia	2	3	1.000
Chryseobacterium indologenes	3	3	1.000

Table 1: Data presented as median with [IQR].SOFA:Sequential Organ Failure Assessment. Definition of abbreviations: VAP:Ventilator associated pneumonia; ARDS:acute respiratory distress syndrome; CRAB:Cabapemne Resistant Acinetobacter Baumannii; CRKP:carbapenem-resistant Klebsiella pneumoniae; CRPA:Carbapenem-resistant Pseudomonas aeruginosa; MASA:methicillin-susceptible Staphylococcus aureus.

Comparison of microbial Alpha diversity in survivors and deceased

We performed 16S rRNA gene sequencing on all samples, with BALF and fecal matter diluted to saturation levels (Fig. 2A,B), indicating sufficient sample coverage and distribution. The α-diversity, estimated by the Shannon index, reflects the diversity and abundance of microbiota. Our results demonstrate a decreasing trend in α-diversity over time in both the BALF and fecal microbiome, with the deceased group exhibiting significantly lower microbial diversity in both the lung (Fig. 2C) and gut (Fig. 2D) compared to the survival group (p < 0.05). To further investigate the association between alpha diversity and clinical outcomes, we analyzed Kaplan-Meier curves for Group 01 patients (Fig. 2E,F). The findings revealed that individuals with low α diversity of lung microbes had a higher likelihood of mortality than those with high α-diversity (p < 0.05).

Comparison of the relative abundance of BALF and fecal microbiota in survivors and deceased

The microbial composition of intestinal and respiratory samples was investigated to identify the prevailing microbial taxa at each site. In respiratory samples, Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria emerged as the dominant phylum, collectively representing almost 70% of the total sequencing reads. Likewise, in the intestinal samples, Firmicutes, Proteobacteria, and Bacteroidetes predominated, accounting for nearly 90% of the total sequencing reads. Prolonged mechanical ventilation was found to accentuate the relative abundance of these phylum. In BALF samples, the abundance of Proteobacteria was higher in the deceased (Group 01: 57.5656% vs. 23.1363%; Group 02: 75.8837% vs. 21.6580%) compared to survivors, whereas the reverse pattern was observed for Firmicutes (Group 01: 18.819% vs. 54.2770%; Group 02: 19.0444% vs. 40.8464%), and this trend intensified over time (Fig. 3A). In feces, the abundance of Bacteroidetes in deceased was lower than that in survivors (Group 01: 19.0477% vs. 17.5287%; Group 02: 21.9145% vs. 21.9145%). While no conspicuous difference was found, Proteobacteria exhibited higher levels in the deceased group (group 01: 18.2066% vs. 32.2428%; group 02: 32.2428% vs. 21.6580%), whereas Firmicutes were more abundant in the survivor group (group 01: 54.2770% vs. 53.1729%; group 02: 40.8464% vs. 64.059%)(Fig. 3B). The Venn diagram reveals a substantial reduction in specific bacterial genera in the deceased group compared to the surviving group, with the exception of shared microbes (Fig. 3C,D). In the respiratory tract of the survival group, Prevotellaceae, Moraxellaceae, and Staphylococcaceae were the predominant families. Interestingly, the respiratory tract of the deceased group displayed distinct characteristics, including the presence of Enterobacteriaceae (p < 0.05), enrichment of Enterococcaceae, and an increase in opportunistic pathogens Xanthomonadaceae (p < 0.05) (Fig. 3E, G). Conversely, the intestines of the survival group exhibited a dominance of symbiotic bacteria, such as Veillonellaceae (p<0.05), Peptostreptococcaceae (p<0.05), Bacteroidaceae, Lachnospiraceae, Lactobacillaceae, among others. However, the intestines of the deceased group were primarily characterized by Enterococcaceae (p < 0.05)(Fig. 3F, H).

Comparison of microbial Beta diversity in survivors and deceased

Beta diversity is a measure of species diversity among ecosystems, according to the absence or presence of species and their abundance. To further clarify the association between respiratory and intestinal microorganisms, we used NMDS and PLS-DA to construct a model for differential analysis of specimen clustering results.It is known that biogeographic barriers exist between respiratory tract and intestine microbial community within individuals. Our results indicate that beta diversity is significantly different between lung and gut in survivors, but not in deceased(p < 0.05)(Fig. 4A,B). There were significant differences between microorganisms in these two regions. However, in the survivor group, Akkermansia, Streptococcus, Prevotella, and Lactobacillus were enriched in the intestinal and respiratory tracts at the beginning of mechanical ventilation, and the commensal flora of both intermingled with each other (Fig. 4C). The respiratory tract was enriched with intestinal beneficial bacteria. However, in the deceased group, the respiratory and intestinal tracts were simultaneously dominated by an increase in pathogenic bacteria and a decrease in commensal bacteria. The aggregation of highly pathogenic Enterobacteriaceae and Enterococcus in the intestine seems to explain the large enrichment of intestinal microbiota in the respiratory tract among the deceased (p < 0.05)(Fig. 4D).

Comparing the fecal SCFA concentrations in survivors and deceased

SCFAs are metabolic by-products of symbiotic intestinal bacteria fermenting dietary fiber, known for their anti-inflammatory, immune-modulatory effects, and distal lung protection. We quantified seven SCFAs, including Caproic acid, Pentanoic acid, Butyric acid, Isobutyric acid, Isovaleric acid, Propionic acid and Acetic acid, in the fecal microbiota of Group 01. Our findings reveal significantly lower concentrations of Pentanoic acid, Butyric acid, Isobutyric acid, and Isovaleric acid in the deceased group compared to the surviving group(P < 0.05)(Fig. 5A).

Associations between lung and gut microbiota and 28-day mortality

We employed Cox proportional hazard regression models to investigate the potential links between changes in intestinal and pulmonary microbiota and 28-day mortality after the initiation of mechanical ventilation. During the follow-up period, a total of 27 patients (44%) succumbed within 28 days. After conducting a preliminary univariate analysis, we specifically focused on lung and gut microbial diversity, BALF Enterobacteriaceae, and fecal Enterococcus, undertaking a multifactor Cox regression analysis. Our results revealed significant associations between lung microbial diversity and fecal Enterococcaceae, both of which independently correlated with 28-day mortality. The results showed that pulmonary microbial diversity and fecal Enterococcaceae were correlated and independently associated with 28-day mortality (adjusted hazard ratio (aHR) = 0.773 ; 95% confidence interval (CI) 0.652 ~ 0.916, p = 0.003; and aHR = 1.022; 95% CI, 1.008–1.037, p = 0.002)(Table 2). These findings underscore the potential prognostic value of analyzing intestinal and pulmonary microbiota in predicting 28-day mortality among mechanically ventilated patients.

Table 2 Definition of abbreviations: HR hazard ratio, aHR adjusted hazard ratio, 95%CI 95% confidence interval.

In this study, there were profound changes in the taxonomic composition and diversity of the intestinal and respiratory microbiota in critically ill patients.Through these retrospective observational cohort study, we uncover the fundamental significance of the gut and lung microbiome in orchestrating dynamic interactions between the microbiota and the Critical illness patients host. A noteworthy focus of our investigation was on exploring the predictive potential of respiratory microbiota dysbiosis and Enterococcaceae overgrowth concerning 28-day mortality among mechanically ventilated critically ill patients. Our research elucidates the profound implications of alterations in gut and lung microbiota diversity in this cohort, offering crucial insights into the adverse clinical outcomes associated with these changes.

A healthy intestinal microbiota is in a dynamic state of equilibrium, with each component mutually constraining the others[49]. Commensal bacteria can prevent the expansion of pathogens by mechanisms such as nutritional competition, indirect stimulation of host immunity, or support for epithelial barrier integrity[50, 51]. In previous studies, low fecal microbial diversity has been associated with increased risk of death or infection, while the dominance of a single bacterial genus has been identified as an independent risk factor for adverse outcomes[52, 53]. Our results confirm that lower microbial diversity in the lung is associated with a higher risk of mortality. The deceased group lost key barobiota, such as respiratory cterial genera that represent important components of a healthy individual's miccommensal bacteria: Prevotellaceae, Moraxellaceae, Staphylococcaceae and intestinal commensal bacteria: Veillonellaceae, Peptostreptococcaceae, Bacteroidaceae, Lachnospiraceae, Lactobacillaceae. These commensal intestinal bacteria are known to produce SCFAs. We measured a significant decrease in levels of Pentanoic acid, Butyric acid, Isobutyric acid, and Isovaleric acid in ICU deceased individuals. SCFAs derived from commensal bacterial sources are known to affect the functions of various cells through mechanisms such as involvement in G protein-coupled receptor signaling (GPCR)and inhibition of histone deacetylase activity (HDAC) in different cell types, thereby regulating immune responses[54]. Research has shown that the intestinal microbiota has a regulatory effect on the respiratory microbiota, with its main impact on the body's defense mechanisms against lung infections[55–57]. Previous studies showed that disrupting the postnatal symbiotic colonization or selectively losing dendritic cells interrupts the migration program of lung IL-22 + ILC3, rendering newborn mice more susceptible to pneumonia. Conversely, the transfer of postnatal symbiotic bacteria restores this ability and demonstrates that the presence of symbiotic bacteria is essential for newborn mice to resist pneumonia[58, 59]. An increasing body of evidence supports the anti-inflammatory and immune-regulatory roles of SCFAs along the gut-lung axis[60]. Emerging evidence suggests that along the gut-lung axis, SCFAs can stimulate myeloid cells in the bone marrow, which then migrate to the lungs and form an anti-inflammatory environment[61]. In the results of this study, the concentration of intestinal microbiota-derived SCFAs had a protective effect on the body and was positively correlated with clinical outcomes. Therefore, the concentration of SCFAs can reflect the microbial ecological balance between intestinal commensal microbiota and pathogens, indirectly reflecting their protective effects against respiratory diseases, which can help predict clinical outcomes.

Mechanical ventilation has been shown to lead to a reduction in the diversity of both the respiratory and intestinal microbiota [62, 63]. Research has found that this phenomenon is independently linked to poorer survival rates and longer duration of mechanical ventilation[64]. Similar to the results of previous studies, we detected a significant depletion of commensal bacteria and overgrowth of pathogenic dominant taxa (such as Enterococcaceae or Enterobacteriaceae) in the intestinal of deceased group[65–67]. In a single-center ICU prospective study of 301 patients, rectal swabs were collected upon admission, and the results showed that VRE colonization and domination of Enterococcaceae (which accounted for 30% and 15% of patients, respectively) were associated with death or all-cause infection[68]. The enrichment of intestinal microbiota in the lungs of critically ill patients is gaining increasing attention[69]. Enrichment of enteric microorganisms, specifically pathogenic Enterobacteriaceae and Mechanical ventilation has been shown to lead to a reduction in the diversity of both the respiratory and intestinal microbiota [62, 63]. Research has found that this phenomenon is independently linked to poorer survival rates and longer duration of mechanical ventilation[64]. Similar to the results of previous studies, we detected a significant depletion of commensal bacteria and overgrowth of pathogenic dominant taxa (such as Enterococcaceae or Enterobacteriaceae) in the intestinal of deceased group[65–67]. In a single-center ICU prospective study of 301 patients, rectal swabs were collected upon admission, and the results showed that VRE colonization and domination of Enterococcaceae (which accounted for 30% and 15% of patients, respectively) were associated with death or all-cause infection[68]. The enrichment of intestinal microbiota in the lungs of critically ill patients is gaining increasing attention[69]., in the pulmonary microbiota has been linked to the development of ARDS in mechanically ventilated patients[70–72]. However, studies have also shown that transmission and transfer of commensal bacteria from the host intestinal can cause infectious complications in the lungs[73]. Our findings support the aforementioned study, wherein enrichment of intestinal bacteria, including both commensal bacteria and pathogens, was observed in the respiratory tract of nearly all patients in the ICU. The abundance of pathogenic enteric bacteria Enterobacteriaceae in the respiratory tract of patients in the deceased group correlates with their heavy colonization in the intestinal tract. In critically ill patients, especially those with compromised intestinal barrier, the intestinal microbiota can migrate to distant organs via intestinal drainage lymphatics, portal vein, and systemic circulation, a process that requires increased permeability of both the intestine and alveoli. When the intestinal and alveolar capillary permeability increases, the migration of intestinal-lung microbiota in critically ill patients is enhanced[74]. Our findings indicate that the interruption of microbiota communication between the intestines and lungs, resulting from a barrier disruption in critically ill patients, could have notable clinical implications. We believe that the imbalance between respiratory microbiota and pathogenic bacteria is parallel to the intestinal microbiota. In the early stages of mechanical ventilation, intestinal dysbiosis may be a predictor of respiratory microbiota, and low microbial diversity and high dominance of a single species may predict poor outcomes.

We need to emphasize that we are a small single-center study, and considering that endotracheal intubation and mechanical ventilation are the main influencing factors of the airway, we conducted a comparative study under the same mechanical ventilation conditions. However, the clinical complexity of ICU patients and the use of various treatment measures made it impossible for us to determine a sufficient number of subjects for each group, and we could not standardize antibiotic treatment, resulting in our inability to conduct etiological research. In future studies, more participants need to be recruited to obtain better patient characteristic stratification at baseline and to collect more clinical data.

The intestinal and respiratory microbiota of critically ill patients who died underwent a marked transformation following admission to the ICU, which was further exacerbated over time. The study also showed a correlation between the overabundance of pathogenic Enterococcus or Enterobacteriaceae in the respiratory tracts of patients in the group of deceased patients and their heavy colonization of the intestine. The dysbiosis of the gut flora may be a predictor of respiratory microbiota, with diminished microbial diversity and a high dominance of a single species possibly predicting unfavorable outcomes.

ICU:intensive care;16S ribosomal RNA: DNA region coding for ribosomal 16S RNA subunit; SCFAs: short-chain fatty acids; BALF :bronchoalveolar lavage fluid; GCS:Glasgow Coma Scale; SOFA:Sequential Organ Failure Assessment;ARDS:Acute respiratory distress syndrome; SRA:Sequence Read Archive; OTUs:operational taxonomic units; NMDS:non-metric multidimensional scaling; PLS-DA:partial least squares discriminant analysis; VAP:Ventilator associated pneumonia; CRAB:Cabapemne Resistant Acinetobacter Baumannii; CRKP:carbapenem-resistant Klebsiella pneumoniae; CRPA:Carbapenem-resistant Pseudomonas aeruginosa; MASA:methicillin-susceptible Staphylococcus aureus; GPCR:G protein-coupled receptor signaling; HDAC:histone deacetylase activity;

Acknowledgements

We would like to thank all the medical and nursing staff in this ICU.16S rRNA gene sequencing analysis was performed using the OmicShare tools, a free online platform for data analysis (https://www.omicshare.com/tools).

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA010794) that are publicly accessible at (https://ngdc.cncb.ac.cn/gsa).

Authors' contributions

PZ designed the study, analyzed the data, and drafted the paper. ZZ, WW, SZ, HZ, CC completed the collection and analysis of clinical patient specimens. SW, XT and WH collect and process clinical information. QW and SZ are the project leader, designed experiments, participating in drafting the article, and approved the final version of the article.

Funding

This work was supported by institutional funding.

Code availability

The scripts used for bioinformatics analysis during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The clinical specimen study was approved by the Ethical Commission of the Fujian Medical University Union Hospital and written informed consent was obtained from all subjects. An informed consent was obtained from all patients or from their legal representatives.

Consent for publication

Not applicable.

Competing interests

On behalf of all authors, the corresponding author states that there are no competing interests.

Structure, function and diversity of the healthy human microbiome. Nature, 2012. 486(7402): p. 207-14.
Stewart, C.J., et al., Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature, 2018. 562(7728): p. 583-588.
Gallacher, D., et al., Dissimilarity of the gut-lung axis and dysbiosis of the lower airways in ventilated preterm infants. Eur Respir J, 2020. 55(5).
Lozupone, C.A., et al., Diversity, stability and resilience of the human gut microbiota. Nature, 2012. 489(7415): p. 220-30.
Man, W.H., P.W. de Steenhuijsen and D. Bogaert, The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol, 2017. 15(5): p. 259-270.
Clarke, T.B., et al., Recognition of peptidoglycan from the microbiota by Nod1 enhances systemic innate immunity. Nat Med, 2010. 16(2): p. 228-31.
Mathieu, E., et al., Paradigms of Lung Microbiota Functions in Health and Disease, Particularly, in Asthma. Front Physiol, 2018. 9: p. 1168.
Enaud, R., et al., The Gut-Lung Axis in Health and Respiratory Diseases: A Place for Inter-Organ and Inter-Kingdom Crosstalks. Front Cell Infect Microbiol, 2020. 10: p. 9.
Winglee, K., et al., Aerosol Mycobacterium tuberculosis infection causes rapid loss of diversity in gut microbiota. PLoS One, 2014. 9(5): p. e97048.
Wypych, T.P., L.C. Wickramasinghe and B.J. Marsland, The influence of the microbiome on respiratory health. Nat Immunol, 2019. 20(10): p. 1279-1290.
Marsland, B.J., A. Trompette and E.S. Gollwitzer, The Gut-Lung Axis in Respiratory Disease. Ann Am Thorac Soc, 2015. 12 Suppl 2: p. S150-6.
Ran, X., et al., Effect of antibiotic-induced intestinal dysbacteriosis on bronchopulmonary dysplasia and related mechanisms. J Transl Med, 2021. 19(1): p. 155.
Steed, A.L., et al., The microbial metabolite desaminotyrosine protects from influenza through type I interferon. Science, 2017. 357(6350): p. 498-502.
Zeng, M.Y., et al., Gut Microbiota-Induced Immunoglobulin G Controls Systemic Infection by Symbiotic Bacteria and Pathogens. Immunity, 2016. 44(3): p. 647-658.
Wilmore, J.R., et al., Commensal Microbes Induce Serum IgA Responses that Protect against Polymicrobial Sepsis. Cell Host Microbe, 2018. 23(3): p. 302-311.e3.
Yamada, T., et al., Rapid and Sustained Long-Term Decrease of Fecal Short-Chain Fatty Acids in Critically Ill Patients With Systemic Inflammatory Response Syndrome. JPEN J Parenter Enteral Nutr, 2015. 39(5): p. 569-77.
Dumas, A., et al., The role of the lung microbiota and the gut-lung axis in respiratory infectious diseases. Cell Microbiol, 2018. 20(12): p. e12966.
Schuijt, T.J., et al., The gut microbiota plays a protective role in the host defence against pneumococcal pneumonia. Gut, 2016. 65(4): p. 575-83.
Tamburini, S. and J.C. Clemente, Gut microbiota: Neonatal gut microbiota induces lung immunity against pneumonia. Nat Rev Gastroenterol Hepatol, 2017. 14(5): p. 263-264.
Li, N., et al., Gut microbiota dysbiosis contributes to the development of chronic obstructive pulmonary disease. Respir Res, 2021. 22(1): p. 274.
Madan, J.C., et al., Serial analysis of the gut and respiratory microbiome in cystic fibrosis in infancy: interaction between intestinal and respiratory tracts and impact of nutritional exposures. mBio, 2012. 3(4).
Martin-Loeches, I., et al., The importance of airway and lung microbiome in the critically ill. Crit Care, 2020. 24(1): p. 537.
Dickson, R.P., The microbiome and critical illness. Lancet Respir Med, 2016. 4(1): p. 59-72.
Agudelo-Ochoa, G.M., et al., Gut microbiota profiles in critically ill patients, potential biomarkers and risk variables for sepsis. Gut Microbes, 2020. 12(1): p. 1707610.
Szychowiak, P., et al., The role of the microbiota in the management of intensive care patients. Ann Intensive Care, 2022. 12(1): p. 3.
Kim, Y.G., et al., Gut dysbiosis promotes M2 macrophage polarization and allergic airway inflammation via fungi-induced PGE(2). Cell Host Microbe, 2014. 15(1): p. 95-102.
Ojima, M., et al., Metagenomic Analysis Reveals Dynamic Changes of Whole Gut Microbiota in the Acute Phase of Intensive Care Unit Patients. Dig Dis Sci, 2016. 61(6): p. 1628-34.
Garcia, E.R., et al., Changes in the gut microbiota and risk of colonization by multidrug-resistant bacteria, infection, and death in critical care patients. Clin Microbiol Infect, 2022.
Klingensmith, N.J. and C.M. Coopersmith, The Gut as the Motor of Multiple Organ Dysfunction in Critical Illness. Crit Care Clin, 2016. 32(2): p. 203-12.
Russell, S.L., et al., Perinatal antibiotic-induced shifts in gut microbiota have differential effects on inflammatory lung diseases. J Allergy Clin Immunol, 2015. 135(1): p. 100-9.
Dickson, R.P., et al., Enrichment of the lung microbiome with gut bacteria in sepsis and the acute respiratory distress syndrome. Nat Microbiol, 2016. 1(10): p. 16113.
Negi, S., et al., Gut Microbiota Regulates Mincle Mediated Activation of Lung Dendritic Cells to Protect Against Mycobacterium tuberculosis. Front Immunol, 2019. 10: p. 1142.
Scheffold, A. and P. Bacher, Anti-fungal T cell responses in the lung and modulation by the gut-lung axis. Curr Opin Microbiol, 2020. 56: p. 67-73.
Zhang, D., et al., The Cross-Talk Between Gut Microbiota and Lungs in Common Lung Diseases. Front Microbiol, 2020. 11: p. 301.
Willis, K.A., J.D. Stewart and N. Ambalavanan, Recent advances in understanding the ecology of the lung microbiota and deciphering the gut-lung axis. Am J Physiol Lung Cell Mol Physiol, 2020. 319(4): p. L710-L716.
Lankelma, J.M., et al., Critically ill patients demonstrate large interpersonal variation in intestinal microbiota dysregulation: a pilot study. Intensive Care Med, 2017. 43(1): p. 59-68.
Aardema, H., et al., Marked Changes in Gut Microbiota in Cardio-Surgical Intensive Care Patients: A Longitudinal Cohort Study. Front Cell Infect Microbiol, 2019. 9: p. 467.
Panzer, A.R., et al., Lung Microbiota Is Related to Smoking Status and to Development of Acute Respiratory Distress Syndrome in Critically Ill Trauma Patients. Am J Respir Crit Care Med, 2018. 197(5): p. 621-631.
Lamarche, D., et al., Microbial dysbiosis and mortality during mechanical ventilation: a prospective observational study. Respir Res, 2018. 19(1): p. 245.
Conway Morris, A., et al., 16S pan-bacterial PCR can accurately identify patients with ventilator-associated pneumonia. Thorax, 2017. 72(11): p. 1046-1048.
Cabrera-Rubio, R., et al., Microbiome diversity in the bronchial tracts of patients with chronic obstructive pulmonary disease. J Clin Microbiol, 2012. 50(11): p. 3562-8.
Guo, M., et al., Bacillus subtilis Improves Immunity and Disease Resistance in Rabbits. Front Immunol, 2017. 8: p. 354.
Edgar, R.C., MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res, 2004. 32(5): p. 1792-7.
Edgar, R.C., et al., UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 2011. 27(16): p. 2194-200.
Ondov, B.D., N.H. Bergman and A.M. Phillippy, Interactive metagenomic visualization in a Web browser. BMC Bioinformatics, 2011. 12: p. 385.
Abdi, H. and L.J. Williams, Wiley Interdisciplinary Reviews: Computational Statistics. 2010: Wiley Interdisciplinary Reviews: Computational Statistics.
Wagner, H., Vegan: Community ecology package. R package version 1.17-4. 2010.
Segata, N., et al., Metagenomic biomarker discovery and explanation. Genome Biol, 2011. 12(6): p. R60.
Hilty, M., et al., Disordered microbial communities in asthmatic airways. PLoS One, 2010. 5(1): p. e8578.
Kim, S., A. Covington and E.G. Pamer, The intestinal microbiota: Antibiotics, colonization resistance, and enteric pathogens. Immunol Rev, 2017. 279(1): p. 90-105.
Lathrop, S.K., et al., Peripheral education of the immune system by colonic commensal microbiota. Nature, 2011. 478(7368): p. 250-4.
Zaborin, A., et al., Membership and behavior of ultra-low-diversity pathogen communities present in the gut of humans during prolonged critical illness. mBio, 2014. 5(5): p. e01361-14.
Chernevskaya, E., et al., Serum and fecal profiles of aromatic microbial metabolites reflect gut microbiota disruption in critically ill patients: a prospective observational pilot study. Crit Care, 2020. 24(1): p. 312.
Maslowski, K.M., et al., Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature, 2009. 461(7268): p. 1282-6.
Huang, F., et al., Early-Life Exposure to Clostridium leptum Causes Pulmonary Immunosuppression. PLoS One, 2015. 10(11): p. e0141717.
Haak, B.W., et al., Impact of gut colonization with butyrate producing microbiota on respiratory viral infection following allo-HCT. Blood, 2018: p. blood-2018-01-828996.
Samuelson, D.R., D.A. Welsh and J.E. Shellito, Regulation of lung immunity and host defense by the intestinal microbiota. Front Microbiol, 2015. 6: p. 1085.
Gray, J., et al., Intestinal commensal bacteria mediate lung mucosal immunity and promote resistance of newborn mice to infection. Sci Transl Med, 2017. 9(376).
Trompette, A., et al., Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat Med, 2014. 20(2): p. 159-66.
Cait, A., et al., Microbiome-driven allergic lung inflammation is ameliorated by short-chain fatty acids. Mucosal Immunol, 2018. 11(3): p. 785-795.
Dang, A.T. and B.J. Marsland, Microbes, metabolites, and the gut-lung axis. Mucosal Immunol, 2019. 12(4): p. 843-850.
Zakharkina, T., et al., The dynamics of the pulmonary microbiome during mechanical ventilation in the intensive care unit and the association with occurrence of pneumonia. Thorax, 2017. 72(9): p. 803-810.
Kelly, B.J., et al., Composition and dynamics of the respiratory tract microbiome in intubated patients. Microbiome, 2016. 4: p. 7.
Kitsios, G.D., et al., c. Am J Respir Crit Care Med, 2020. 202(12): p. 1666-1677.
Xu, R., et al., Dysbiosis of the intestinal microbiota in neurocritically ill patients and the risk for death. Crit Care, 2019. 23(1): p. 195.
Iapichino, G., J.M. Lankelma and W.W. Joost, Gut microbiota disruption in critically ill patients : Discussion on "Critically ill patients demonstrate large interpersonal variation of intestinal microbiota dysregulation: a pilot study". Intensive Care Med, 2017. 43(5): p. 718-719.
Buelow, E., et al., Comparative gut microbiota and resistome profiling of intensive care patients receiving selective digestive tract decontamination and healthy subjects. Microbiome, 2017. 5(1): p. 88.
Freedberg, D.E., et al., Pathogen colonization of the gastrointestinal microbiome at intensive care unit admission and risk for subsequent death or infection. Intensive Care Med, 2018. 44(8): p. 1203-1211.
Fromentin, M., J.D. Ricard and D. Roux, Respiratory microbiome in mechanically ventilated patients: a narrative review. Intensive Care Med, 2021. 47(3): p. 292-306.
Mukherjee, S. and D. Hanidziar, More of the Gut in the Lung: How Two Microbiomes Meet in ARDS. Yale J Biol Med, 2018. 91(2): p. 143-149.
Kyo, M., et al., Unique patterns of lower respiratory tract microbiota are associated with inflammation and hospital mortality in acute respiratory distress syndrome. Respir Res, 2019. 20(1): p. 246.
Dickson, R.P., et al., Lung Microbiota Predict Clinical Outcomes in Critically Ill Patients. Am J Respir Crit Care Med, 2020. 201(5): p. 555-563.
Stanley, D., et al., Translocation and dissemination of commensal bacteria in post-stroke infection. Nat Med, 2016. 22(11): p. 1277-1284.
Haak, B.W. and W.J. Wiersinga, The role of the gut microbiota in sepsis. Lancet Gastroenterol Hepatol, 2017. 2(2): p. 135-143.

No competing interests reported.

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published 18 Dec, 2023

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The Gut-Lung Axis in Critical Illness: Microbiome Composition as a Pr edictor of Mortality at Day 28 in Mechanically Ventilated Patients

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods and Materials

Patient Enrollment

Data Collection

Sample collection

Alveolar lavage fluid samples

Stool samples

DNA extraction, Bioinformatics analysis and Taxonomy annotation

Microbial analysis and Statistical analysis

Bioinformatics analysis Methods of SCFAs and Statistical analysis

RESULT

Comparison of microbial Alpha diversity in survivors and deceased

Comparison of the relative abundance of BALF and fecal microbiota in survivors and deceased

Comparing the fecal SCFA concentrations in survivors and deceased

Associations between lung and gut microbiota and 28-day mortality

DISCUSSION

CONLUSION

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1