Major alteration of Lung Microbiome and the Host Reaction in critically ill COVID-19 Patients with high viral load

Background Patients with COVID-19 under invasive mechanical ventilation are at higher risk of developing ventilator-associated pneumonia (VAP), associated with increased healthcare costs, and unfavorable prognosis. The underlying mechanisms of this phenomenon have not been thoroughly dissected. Therefore, this study attempted to bridge this gap by performing a lung microbiota analysis and evaluating the host immune responses that could drive the development of VAP. Materials and methods In this prospective cohort study, mechanically ventilated patients with confirmed SARS-CoV-2 infection were enrolled. Nasal swabs (NS), endotracheal aspirates (ETA), and blood samples were collected initially within 12 hours of intubation and again at 72 hours post-intubation. Plasma samples underwent cytokine and metabolomic analyses, while NS and ETA samples were sequenced for lung microbiome examination. The cohort was categorized based on the development of VAP. Data analysis was conducted using RStudio version 4.3.1. Results In a study of 36 COVID-19 patients on mechanical ventilation, significant differences were found in the nasal and pulmonary microbiome, notably in Staphylococcus and Enterobacteriaceae, linked to VAP. Patients with VAP showed a higher SARS-CoV-2 viral load, elevated neutralizing antibodies, and reduced inflammatory cytokines, including IFN-δ, IL-1β, IL-12p70, IL-18, IL-6, TNF-α, and CCL4. Metabolomic analysis revealed changes in 22 metabolites in non-VAP patients and 27 in VAP patients, highlighting D-Maltose-Lactose, Histidinyl-Glycine, and various phosphatidylcholines, indicating a metabolic predisposition to VAP. Conclusions This study reveals a critical link between respiratory microbiome alterations and ventilator-associated pneumonia in COVID-19 patients, with elevated SARS-CoV-2 levels and metabolic changes, providing novel insights into the underlying mechanisms of VAP with potential management and prevention implications.


Background
Patients with COVID-19 under invasive mechanical ventilation are at higher risk of developing ventilatorassociated pneumonia (VAP), associated with increased healthcare costs, and unfavorable prognosis.
The underlying mechanisms of this phenomenon have not been thoroughly dissected.Therefore, this study attempted to bridge this gap by performing a lung microbiota analysis and evaluating the host immune responses that could drive the development of VAP.

Materials and methods
In this prospective cohort study, mechanically ventilated patients with con rmed SARS-CoV-2 infection were enrolled.Nasal swabs (NS), endotracheal aspirates (ETA), and blood samples were collected initially within 12 hours of intubation and again at 72 hours post-intubation.Plasma samples underwent cytokine and metabolomic analyses, while NS and ETA samples were sequenced for lung microbiome examination.The cohort was categorized based on the development of VAP.Data analysis was conducted using RStudio version 4.3.1.

Results
In a study of 36 COVID-19 patients on mechanical ventilation, signi cant differences were found in the nasal and pulmonary microbiome, notably in Staphylococcus and Enterobacteriaceae, linked to VAP.Patients with VAP showed a higher SARS-CoV-2 viral load, elevated neutralizing antibodies, and reduced in ammatory cytokines, including IFN-δ, IL-1β, IL-12p70, IL-18, IL-6, TNF-α, and CCL4.Metabolomic analysis revealed changes in 22 metabolites in non-VAP patients and 27 in VAP patients, highlighting D-Maltose-Lactose, Histidinyl-Glycine, and various phosphatidylcholines, indicating a metabolic predisposition to VAP.

Conclusions
This study reveals a critical link between respiratory microbiome alterations and ventilator-associated pneumonia in COVID-19 patients, with elevated SARS-CoV-2 levels and metabolic changes, providing novel insights into the underlying mechanisms of VAP with potential management and prevention implications.

BACKGROUND
Since the emergence of the highly contagious Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in 2019, the COVID-19 pandemic has rapidly spread worldwide, leading to profound global health and economic consequences (1).The World Health Organization (WHO) has reported a staggering 770 million cases and nearly 7 million deaths globally by July 2023 (2,3).Notably, 5-12% of patients progress to severe or critical stages, necessitating invasive mechanical ventilation (IMV) and signi cantly increasing mortality rates (4-6).However, IMV often triggers complications, including secondary infections, which can worsen clinical outcomes and extend stays in intensive care units (ICUs) and hospitals (4,7).Critically ill COVID-19 patients often experience bacterial superinfections, further complicating their condition.
In the intensive care setting, individuals with severe COVID-19 pneumonia show a marked propensity for respiratory superinfections, with mechanical ventilation-associated pneumonia (VAP) being especially prevalent.This tendency is thought to be associated with SARS-CoV-2 virus-induced alterations of the pulmonary microbiota (8,9).The occurrence of VAP and other superinfections may be attributed to the invasion of new pathogens or bacterial strains, which diversify from primary SARS-CoV-2 infection (10,11).Data suggests that at least 32% of these patients will develop bacterial superinfections, increasing morbidity and mortality rates (12,13).However, the exact prevalence and impact of initial bacterial superinfections on progression to VAP in patients with severe COVID-19 pneumonia are not yet fully understood (11).The dynamics of the pulmonary microbiome are thought to play an integral role in initiating and shaping the course of superinfections and in uencing patient response to treatment.Understanding these interactions is essential to improve therapeutic strategies and patient outcomes in severe cases of COVID-19 (14).
The lungs harbor a diverse microbiome comprising approximately 100 different bacteria, viruses, and fungi (15,16).This complex microbiome is crucial in maintaining immune balance and signi cantly in uences the severity and duration of respiratory infections, such as SARS-CoV-2 (1,17).The intricate interplay between the commensal microbiota and the immune system is vital for regulating immune responses, with microbiota-derived metabolites mediating these interactions.Additionally, metabolic changes have been observed, but their connection to bacterial superinfections in severe COVID-19 patients remains unclear (1,18,19).Changes in the microbiome-immune system interplay due to hostmicrobiome dysbiosis may lead to dysregulated immune responses and conditions like systemic in ammation (20,21).It is crucial to comprehend these interactions.This study explores how SARS-CoV-2 affects the lung microbiome in critically ill COVID-19 patients on mechanical ventilation.We analyze the microbiome, metabolites, and host immune response to understand better the underlying mechanisms responsible for VAP in 36 mechanically ventilated COVID-19 patients.

MATERIALS AND METHODS
This prospective cohort study was conducted at Clinica Universidad de La Sabana in Chia, Colombia, between January 2021 and July 2021, including all critically ill COVID-19 patients requiring invasive mechanical ventilation admitted to the ICU.The attending physicians prospectively gathered data by reviewing medical records and laboratory results in the platform for data storage REDCap every time the patient was screened and selected.Nasal swabs (NS), Endotracheal aspirates (ETA), and blood samples were collected in the initial 12 hours following intubation, and a follow-up was conducted 72 hours postintubation.Then, we performed microbiological analysis, cytokines, and metabolomic characterization.The Institutional Review Board (IRB) of Clinica Universidad de La Sabana approved the study, and all patients provided informed consent to participate (CUS-20190903).All methods and research procedures were performed in accordance with the local and international regulations for good clinical practices in clinical research and did not change the clinical treatment of the patients participating in the study as per local clinical guidelines.

Study population
Patients diagnosed with COVID-19 and required ICU admission and invasive mechanical ventilation within 12 hours of hospital admission for more than 72 hours were included in this study (Table 1).The severity of COVID-19 was classi ed based on WHO guidelines, and critical illness was identi ed in patients who needed invasive mechanical ventilation, extracorporeal membrane oxygenation (ECMO), or suffered from end-organ dysfunction (22).We excluded pregnant patients who had been invasively ventilated in another hospital.Patients who had been administered more than two doses of antibiotics before intubation, those who had IMV for over 24 hours before the sample collection, and patients who had a documented coinfection within 48 hours of admission were also excluded.Demographic data, comorbidities, symptoms, physiological variables, systemic complications, and laboratory reports from the rst 24 hours of admission were recorded and monitored every 48 hours until the patient was extubated.We retrospectively reviewed the data from medical records at the time of hospital discharge to ensure the accuracy of the recorded information uploaded to the REDCap platform hosted at the Universidad de La Sabana (Plataforma REDCap -Universidad de La Sabana [Internet].Universidad de La Sabana; [2023].Disponible en: https://redcap.unisabana.edu.co/).Recollection and sample processing ETA and NS samples were meticulously collected following established protocols employing sterile saline (0.9%).Immediately post-collection, these samples were frozen at -80°C segregated into distinct aliquots for future sequencing and metabolomics analyses.Prior to these analyses, the samples underwent thawing and thorough mixing to eradicate any particulate matter.Concurrently, blood samples were obtained through an intravenous catheter, utilizing 5-or 10-mL Becton Dickinson Vacutainers (red top tubes), and then centrifuged at 1,970 x g for 10 minutes.Subsequently, the supernatant was methodically apportioned into aliquots and preserved at -80°C for ensuing processing.To maintain consistency in handling and storage, thereby minimizing potential contamination or degradation risks, the research team collected all blood samples, ensuring rigorous standardization and enhancing the accuracy of the analyses.Samples were obtained from eligible patients on invasive mechanical ventilation within the initial 24 hours (day 0) and subsequently on days 3, 5, and 7 or the day of diagnosis of mechanical VAP.

Diagnosis Criteria for VAP
The diagnosis of VAP was based on current clinical guidelines published by the Infectious Diseases Society of America and the American Thoracic Society (IDSA/ATS) for the management and diagnosis of VAP (23).Diagnostic criteria included patients on mechanical ventilation for at least 72 h, a new or progressive radiographic in ltrate, and at least two of the following symptoms: fever (body temperature > 38 °C), purulent tracheal secretions, or leukocytosis or leukopenia (leukocyte count > 10,000/μL or < 4,000/μL, respectively).Patients were included in the VAP category only if, after being intubated to the ICU for 48 hours or more, they had at least one respiratory pathogen isolated from their ETA (>106 CFU) or bronchoalveolar lavage (>104 CFU) that is known to cause pneumonia.

DNA extraction
DNA isolation was performed using the DNeasy® Blood & Tissue Kit from QIAGEN, a commercially available kit.Initially, a 500 µL sample obtained from either a ETA or NS was centrifuged at 6,750 x g for 10 minutes at room temperature.Subsequently, the supernatant was removed, and the pellet was resuspended in 200 µL of PBS.The isolation process followed the manufacturer's instructions.The quality and concentration of DNA samples were assessed using the NanoDrop™ One instrument.

16S r RNA ampli cation and sequencing
Ampli cation and sequencing of the V4 region of the 16S rRNA gene were performed using primers 515-533F forward (GTGCCAGCMGCCGCGGTAA) and 806-787R reverse (GGACTACHVGGGTWTCTAAT) with 8bp barcode and Illumina adaptor (24).The polymerase chain reaction (PCR) was carried out using approximately 100 ng of gDNA per sample and Thermo Fisher Platinum Taq DNA Polymerase (Cat# 10966-026, Life Technologies, Carlsbad, CA).The ampli cation conditions were as follows: 94°C for 5 min, 94°C for 30 s, 55°C for 30 s, 72°C for 30 s for 35 cycles, 72°C for 7 min.The libraries were puri ed using QIAquick PCR puri cation kit to remove primer-dimers and short reads (<100bp) and quanti ed using Qubit 1X dsDNA HS Assay (Cat# 28106, QIAGEN, Hilden, Germany).The libraries were normalized, and fragment size was examined using a sensitivity DNA Kit (Cat# 5067-4,626, Agilent, Santa Clara, CA).
The library pool was sequenced using the Illumina MiSeq system as instructed by the manufacturer (Cat# MS-102-3,003, Illumina Inc., La Jolla, USA).A low amount of environmental and reagent contamination was detected in most of the PCR-negative controls (Supplemental Fig. 1).
Cytokines/Chemokines/growth factor measures The analysis of various protein targets was conducted utilizing the Invitrogen™ multiplexed immunoassay panel, speci cally, the Cytokine/Chemokine/Growth Factor 45-Plex Human ProcartaPlex™ Panel 1 (Cat #EPX450-12171-901, ThermoFisher Scienti c, Vienna, Austria), in accordance with the manufacturer's instructions.Serum samples were processed using a compatible Luminex 200 instrument (Luminex Corporation, Austin, Texas, USA), utilizing lot# 313189-002 for bead mixes, detection antibody mixes, and standard mixes, all prepared as per manufacturer's instructions.To ensure accuracy, the combined standards were diluted fourfold and run in duplicate alongside two blanks containing assay buffer only.Prior to analysis, samples were thawed on ice, subjected to centrifugation at 1,000 x g for 10 minutes, and the supernatant was analyzed without further dilution.
Following data collection, quality control measures were implemented according to a speci ed protocol (30).All samples had a bead count exceeding 100, with a minimum requirement of 30 beads.After analysis with the Luminex, Mean Fluorescence Intensity (MFI) was provided and was transformed to Net MFI after subtracting the background from the blank wells.Using the ProcartaPlex Analysis App (ThermoFisher Scienti c, Vienna, Austria), concentration values were generated via transformation of Net MFI based on the standard curves for each analyte, as we previously reported for saliva (31) and serum (32) .Target concentrations were adjusted to standardized values.Values labeled OOR< or OOR> were adjusted to match the lowest (Standard 7) or highest (Standard 1) limit of detection, respectively.The ranges of concentrations (pg/ml) for each target are included in the Supplementary Materials.After this transformation, all values were log10-transformed.The samples from the VAP-COVID and NO VAP-COVID groups were analyzed separately for each target using Mann-Whitney tests.Results were visually represented through box graphs displaying mean values and standard deviations.

Untargeted Metabolomic Analysis
The untargeted metabolomic investigation employed two methods: RP-LC-QTOF-MS and HILIC-LC-QTOF-MS.Sample preparation involved adding cold methanol (3:1 ratio) to plasma, vortexing for 5 minutes, and centrifugation at 7,310 x g for 10 minutes at 4°C.The analysis integrated an Agilent 1260 In nity LC System with a 6545 Q-TOF LC/MS system from Agilent Technologies in Waldbronn, Germany.A 2 µL sample was injected into a ZORBAX Eclipse Plus C18 column (2.1 x 50 mm, 1.8 µm particle size) at 60°C.Mobile phases were 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) with a ow rate of 0.6 mL/min.
The HILIC-LC-QTOF-MS analysis involved injecting 5 µL of the sample into an In nity Lab Poroshell HILIC-Z column (2.1 x 100 mm, 1.9 µm particle size) maintained at a constant temperature of 30°C.The mobile phases comprised 10% (200 mM ammonium format in Milli-Q water, pH 3) with 90% water (phase A) and 10% (200 mM ammonium format in water, pH 3) mixed with 90% acetonitrile (phase B).The ow rate remained constant at 0.6 mL/min, employing a gradient elution program.Data acquisition was conducted in negative electrospray ionization mode (ESI-), covering a mass-to-charge ratio spectrum from 50 to 1100 m/z.

Statistical analysis
Statistical analysis was performed using GraphPad Prism 9 software and R statistical framework (version 4.3.1).Initially, we used the Shapiro-Wilk test to assess the data distribution rigorously.Descriptive statistics were systematically applied to summarize the data set, encompassing the mean with standard error and the median coupled with the interquartile range (IQR).Chi-square tests were judiciously applied for categorical variables to compare patient characteristics between distinct groups, while independent t-tests were utilized for continuous variables.
We estimated microbial diversity using the sophisticated vegan package implemented within the R environment.Alpha diversity was meticulously evaluated employing both Shannon and Chao1 indices.
The signi cance of differences in alpha diversity between groups was determined by applying Wilcoxon's rank sum test or the Mann-Whitney U-test.The selection of these tests was contingent on whether the data were paired or unpaired.Beta diversity was quanti ed using the Bray-Curtis dissimilarity index and the weighted UniFrac distance.Principal Coordinate Analysis (PCoA) was conducted to assess beta diversity across varying groups.This involved using permutational multivariate analysis of variance (PERMANOVA), incorporating 9,999 permutations facilitated by the adonis2 function in the Vegan R package (v2.6-4).
To analyze the differences between groups, ratios were evaluated employing Fisher's exact probability test.Furthermore, correlations between clinical indicators and the lung microbiota were analyzed using Spearman Correlation Analysis.Throughout, a p-value threshold of less than 0.05 was adhered to, denoting statistical signi cance in all analytical determinations.For metabolomics comparative analysis, two-sample t-tests were applied, and the mean of groups was used to calculate the fold-change values.

RESULTS
106 samples were collected from 36 COVID-19 patients undergoing mechanical ventilation in the ICU.This collection comprised 36 NS and 70 ETA samples (Fig. 1).Utilizing 16S RNA gene sequencing, the study delved into investigating the microbial composition within the respiratory tracts of these patients.
The cohort was characterized by its diversity, encompassing individuals who either developed or did not develop VAP, thereby permitting a thorough evaluation of microbial diversity in severe COVID-19 cases.Demographic data, clinical characteristics, and laboratory test results are systematically presented in Table 1.COVID-19 Patients with VAP and without VAP show differential nasal microbiome abundance changes upon ICU admission.We rst used Chao and Shannon diversity measures to test for differences in microbial abundance changes between the groups.Although no signi cant alterations were discerned among the groups in the overall microbial composition (Fig. 2A), further investigations were conducted to probe for speci c abundance shifts among the predominantly present organisms within the samples.
This in-depth analysis was designed to unearth subtle discrepancies potentially obscured in the broader comparative framework, yielding a more intricate and nuanced understanding of microbial dynamics.These showed signi cant differences between the VAP and NO VAP groups (Fig. 2B), speci cally in bacteria from the genus Staphylococcus and Enterobacteriaceae (Fig. 2C).Staphylococci are Grampositive bacteria that are common skin, pulmonary, and oral commensals, and members of this genus can also be pathobionts (33,34).In contrast, members of the genus Enterobacteriaceae are part of a family of Gram-negative bacteria that includes pathogens such as Klebsiella, Enterobacter, Citrobacter, Salmonella, Escherichia, Shigella, Proteus, Serratia among others (35).These data suggest a possible shift in nasal colonizers that may predispose the patient to VAP from the members of the Enterobacteriaceae bacterial genus.Endotracheal aspirates from COVID-19 patients who develop VAP have a reduction of Staphylococcus and increased Gram-negative bacterial pathogens.To further assess pulmonary microbiome changes in the cohort, ETA samples were collected from patients upon intubation and at a follow-up time point (72 hours).At baseline, the Chao test did not show changes in total microbial richness.However, an increase in the Shannon index showed that richness and evenness were higher in the VAP group (Fig. 3A).
Changes in abundance of the top 15 microbial genus showed drastic differences between the group who developed VAP and those who did not (Fig. 3B).Statistical analysis of the most abundant genus revealed a reduction in Staphylococcus and an increase in members of the Enterobacteriaceae group (Fig. 2).More precisely, a signi cant alteration in the abundance of Escherichia was observed, alongside a notable trend approaching signi cance in Acinetobacter.
Furthermore, increases in Prevotella and Haemophilus were also detected.However, these changes did not reach statistical signi cance (Fig. 3C).We also tested for signi cant changes in less abundant bacteria as they may in uence the growth of pathogens by alteration of the local microenvironment.Of note, we observed a signi cant increase in the abundance of Parvimonas, Anaerococcus, Psychrobacter, and Enterococcus (Fig. 3D).Upon testing microbial changes in a follow-up time point, similar trends in microbial abundance were observed (Supplemental Fig. 2).Taken together, these results suggest that patients who develop VAP have an altered nasal and pulmonary microbiome that may predispose them to this severe form of disease.A higher abundance of SARS-CoV-2 in serum correlates with dynamic changes in nasal and pulmonary microbiome in VAP patients.To determine the potential association between serum viral load and shifts in nasal and pulmonary microbiota, the research quanti ed levels of SARS-CoV-2 in the nasopharynx and lungs of the cohort at key intervals: upon hospital admission, during mechanical ventilation and at a subsequent follow-up.The ndings indicated that patients who developed VAP exhibited higher Log copies/mL of SARS-CoV-2 at admission (the initial assessment point), as determined via quantitative real time polymerase chain reaction (RT-PCR) (Fig. 4A).Notably, signi cant variations in bacterial abundance were observed among patients with differing viral titers of SARS-CoV-2, compared to those without detectable virus at the time of sample collection, in both nasal and lung samples (Fig. 4B-C).
In nasal samples, the group with a higher viral load displayed a reduction in Corynebacterium and Staphylococcus and an increase in Proteus, Enterobacteriaceae, and Escherichia-Shigella (Fig. 4B).
Conversely, the group with a lower viral load demonstrated an increase in Corynebacterium and Enterobacteriaceae, and a decrease in Streptococcus (Fig. 4B).In cases with no detectable SARS-CoV-2 in nasal samples, a reduction in Acinetobacter and Prevotella, and an increase in Corynebacterium and Haemophilus were noted (Fig. 4B).
Regarding pulmonary samples, the high viral load group also exhibited a decrease in Corynebacterium and Staphylococcus and an increase in Acinetobacter, Enterobacteriaceae, and Haemophilus (Fig. 4B).In contrast, the lower viral load group showed an increase in Acinetobacter, Neisseria, and Haemophilus, and a decrease in Streptococcus (Fig. 4B).For pulmonary samples with undetectable SARS-CoV-2, a reduction in Enterobacteriaceae and Staphylococcus and an elevation in Streptococcus and Haemophilus were observed (Fig. 4B).
When analyzing all samples collectively, statistical differences in the relative abundance of Bradyrhizobium, Methylobacterium, Reyranella, Sediminibacterium, and Sphingomonas were also noted (Supplemental Fig. 3).In summary, the data suggest that viral titers are linked with a diminution in commensal bacteria and an escalation in Gram-negative pathogenic bacteria, potentially contributing to the development of VAP in patients under mechanical ventilation.COVID-19 patients who developed VAP showed SARS-CoV-2 neutralizing antibodies and decreased in ammatory cytokines and chemokines.Spike-speci c neutralizing antibodies are widely acknowledged as key indicators of the immune response against viruses and bacteria.Given that all patients in the study were diagnosed with COVID-19, the research aimed to ascertain if notable differences existed in neutralization titers between the groups with and without VAP.The analysis revealed no signi cant disparities in the capacity to neutralize pseudoviruses from variants of concern, namely Beta, Gamma, Delta, and Omicron subvariants BA.1 and BA.2.However, a marked elevation in the neutralization of D614 (closest to the original strain from 2019-2020) was discerned in the VAP group (Fig. 5A).
Additionally, plasma samples were procured from the 36 COVID-19 patients to quantify cytokines and chemokines.A signi cant reduction was observed in IFN-δ (p=0.01;(MIP-1) (p=0.0479;Fig. 5H) in patients who developed VAP compared to those who did not.These ndings suggest that in the VAP group, at the time of ICU admission, both a pronounced e cacy of neutralizing antibody activity and a decrease in in ammatory cytokines and chemokines are implicated in an antiviral response that might diminish host effectiveness against bacterial infections.Furthermore, the data indicates that the development of VAP was not linked to any speci c viral variant of concern.Dihydroxy-1H-indole, and Glucuronide I (Figure 6C).For the patients who developed VAP, we observed that 27 metabolites had signi cant changes between the two-time points (Figure 6D).Upon evaluating the adjusted p-values of metabolites surpassing the -log10 threshold of 1.3 (Figure 6E), it was discerned that the most signi cantly altered metabolites in the VAP cohort included Histidinyl-Glycine, a combination of Maltose and Lactose, Phosphatidylcholine with a total of 34 carbons and 1 double bond (PC 34:1), Pyroglutamic acid, Phosphatidylcholine with a total of 38 carbons and 6 double bonds (PC 38:6), a derivative of oleoyl methionine, Phosphatidylserine with 34 carbons and 1 double bond (PS 16:0/18:1), Phosphatidylcholine with 34 carbons and 2 double bonds (PC 34:2), Phosphatidylserine with 37 carbons (PS 37:0), and Phosphatidylcholine with 36 carbons and 4 double bonds (PC 36:4) (Figure 6F).
When contrasting the NO VAP and VAP groups at the baseline, only Urobilin and Triglyceride with a total of 33 carbons differed signi cantly between the groups (Figure 6G).At the follow-up, the comparison revealed a signi cant difference in the levels of the maltose-lactose combination (Figure 6H).Urobilin and maltose-lactose exhibited higher concentrations in the NO VAP group compared to the VAP group.The uctuations in Urobilin might be linked to hepatic involvement, either as a direct consequence of the disease or due to certain medications administered.Alterations in the maltose-lactose combination are associated with shifts in the gut microbiome and a decrease in Short-Chain Fatty Acid (SCFA) producing bacteria (PMC7002114).SCFAs are crucial in modulating bacterial pathogen load and the level of in ammation (PMC8370681).Notably, Triglyceride with 33 carbons, also referred to as TG 17:0/8:0/8:0, was signi cantly elevated in patients who developed VAP (Figure 6G).This triglyceride variant has been implicated in in ammation modulation and lipid metabolism, corroborating the ndings presented in

DISCUSSION
The microbiomes of the upper respiratory tract (URT) and lower respiratory tract (LRT) play a pivotal role in maintaining respiratory health by exerting in uence over the severity of respiratory viruses, such as SARS-CoV-2, and potentially shaping acute immune responses (36-38).Given that the URT serves as the primary entry point for the COVID-19 virus, it is imperative to gain a thorough understanding of how the URT microbiome may impact the severity and outcomes of COVID-19 (39,40).In our study, we observed notable disparities in the abundance of nasal microbiomes between COVID-19 patients who developed VAP and those who did not.While there were no signi cant alterations in the overall microbial diversity, discernible differences emerged in speci c microbial abundances, particularly within the bacterial genera Staphylococcus and Enterobacteriaceae.These ndings suggest a potential shift in nasal microbial colonization patterns that may contribute to an elevated susceptibility to VAP in a icted patients.This observation aligns with prior studies, which have demonstrated overlap in the composition of the upper respiratory tract and lung microbiomes, indicating a role in overall pulmonary health (41).Furthermore, it is consistent with existing literature implicating opportunistic pathogens, such as Staphylococcus, in the severity of respiratory viral infections (42).
In ETA samples collected at the time of intubation and during a follow-up assessment, we observed a heightened microbial diversity and evenness in patients with VAP.Shifts in microbial abundance patterns indicated a reduction in Staphylococcus and an increase in Enterobacteriaceae, particularly Escherichia spp.These ndings align with reports from other studies where S. aureus and E. coli were identi ed as the most common causative microorganisms of VAP in COVID-19 patients (43)(44)(45).Additionally, less abundant bacteria, typically undetectable through conventional culture methods for VAP diagnosis, such as Parvimonas, Anaerococcus, Psychrobacter, Prevotella, and Enterococcus, also exhibited signi cant increases in patients who developed VAP.Similar trends were observed in the follow-up samples, indicating altered nasal and pulmonary microbiomes in VAP patients.Furthermore, an initial higher abundance of Streptococcus was observed in the baseline samples, followed by a subsequent decrease in the follow-up samples, irrespective of their classi cation as VAP or non-VAP cases.
Dysbiosis in the microbiome can foster an in ammatory milieu that facilitates the invasion and replication of the coronavirus, thereby constituting a risk factor for disease severity (46, 47 Glycerophospholipids indicate an optimal cellular membrane composition, vital for immune e ciency and cellular integrity, while variations in dihydroxyl, a glucuronide and indole compound, highlight its role in liver detoxi cation (49).Elevated urobilin levels in non-VAP patients point to preserved hepatic function, crucial for processing heme byproducts, contrasting with lower levels in VAP patients, possibly indicating liver impairment due to disease severity or medication side effects.
A primary limitation of this study is its relatively small sample size.Nevertheless, a comprehensive and multifaceted methodology was adopted to enhance the understanding of pulmonary microbiota dynamics in COVID-19 patients, focusing on developing secondary infections.These ndings underscore the complex impact of COVID-19 and emphasize the critical importance of a holistic research approach.Such an approach deepens the understanding of the disease's complexities and opens new avenues for prevention and treatment strategies, thereby making substantial contributions to the advancement of COVID-19 and respiratory infection management and patient care.

CONCLUSION
Our study elucidates the intricate interplay between the respiratory microbiota and COVID-19, emphasizing the signi cance of microbiome variations in patients with and without VAP.Employing advanced 16S RNA gene sequencing on samples from COVID-19 patients, we identi ed distinct microbial compositions correlated with disease severity.These ndings reveal a critical link between microbial dysbiosis and the severity of COVID-19, suggesting that speci c alterations in microbiota, alongside patient immunology, and metabolites, may in uence both viral and bacterial pathogenesis.Despite the constraints of a small sample size, this research substantially contributes to the COVID-19 eld, advocating for a holistic approach to treatment strategies and patient care.It also foregrounds the necessity for further exploration into the microbiome's role in respiratory diseases, particularly in severe viral infections, highlighting the imperative for a comprehensive understanding of these complex interactions to enhance patient outcomes amid ongoing global health challenges.

Figure 1 .
Figure 1.Study Flow Chart.Flow diagram for the study showing the number of patients included in the analysis.

Figure 2 :
Figure 2: Nasal swabs of patients with COVID-19 that develop ventilator-associated pneumonia showed differential abundance of Staphylococcus and Enterobacteriaceae. A. Alpha diversity of nasal microbiome from COVID-19 patients that developed VAP or did not (NO VAP).B. Percent relative abundance of the top 15 most abundant microbes in the nasal cavity.C. Relative abundance bar graphs of Staphylococcus and Enterobacteriaceae genus.Student's t-test was used to calculate the p-value.Asterisks denote the level of signi cance observed: * = p ≤ 0.05; ** = p ≤ 0.01; *** = p ≤ 0.001.

Figure 3 :
Figure 3: Endotracheal aspiration of patients with COVID-19 shows differential abundance of pulmonary microbiome upon mechanical ventilation.A. Alpha diversity of pulmonary microbiome from COVID-19 patients that developed VAP or did not (NO VAP).B. Percent of relative abundance of the top 15 most abundant microbes in the lungs.C. Relative abundance bar graphs of Staphylococcus, Escherichia, Acinetobacter, Prevotella, and Haemophilus genus.D. Relative abundance bar graphs of Parvimonas, Anaerococcus, Psychrobacter, and Enterococcus genus.Student's t-test was used to calculate the p-value.Asterisks denote the level of signi cance observed: * = p ≤ 0.05; ** = p ≤ 0.01; *** = p ≤ 0.001.

Figure 4 :
Figure 4: Differential abundance of SARS-CoV-2 modulates the nasal and lung microbiome. A. Log per mL of were tested quantitative RT-PCR.B. Percentage of relative abundance of the top 20 most abundant microbes in the nasal cavity and the lungs.Student's t-test was used to calculate the p-value.Asterisks denote the level of signi cance observed: * = p ≤ 0.05; ** = p ≤ 0.01; *** = p ≤ 0.001.

Figure 5 .
Figure 5. Collectively, this data suggests that changes in speci c metabolites might serve as a mechanism predisposing COVID-19 patients to VAP.

Figure 6 :
Figure 6: Metabolomic changes in serum are observed during COVID-19-associated VAP. A. Heat map of the signi cantly metabolites when comparing NO VAP group at baseline and follow-up time

Figure 1 Study
Figure 1

Table 1 .
Demographic information, clinical characteristics, and laboratory test indices of patients were strati ed into two groups: those with VAP and those without VAP.
We discovered that spike-speci c neutralizing antibodies exhibited similar e cacy against various SARS-CoV-2 variants in both groups, except for a notable increase in neutralization against the D614 variant within the VAP group.Furthermore, we assessed cytokine levels and observed diminished concentrations of pivotal cytokines in the VAP cohort, indicative of a subdued in ammatory response.These ndings imply that individuals with VAP mount a robust neutralizing antibody response while concurrently This metabolomic analysis of COVID-19 patients unveils distinct metabolic pro les, sharply differentiating those with VAP from those without.The study identi ed 47 metabolites across various chemical classes and metabolic pathways, signi cantly altering phospholipid, sphingolipid, and glutathione metabolism in VAP patients.These changes, affecting cell membrane integrity and oxidative stress, could play a crucial role in VAP's pathogenesis, potentially enhancing bacterial adhesion and destabilizing immune responses (48).Additionally, imbalances in metabolites critical for glutathione and sphingolipid synthesis may exacerbate these effects, underlining their importance in VAP's complex pathophysiology.Conversely, patients without VAP exhibited variations in glycerophospholipids, glucuronides, and indole compounds, suggesting robust immune and metabolic responses.
).Our study conducted a comparative analysis of immune responses in COVID-19 patients with and without VAP.