Deciphering the Lung Microbiota in COVID-19 patients: Insights from Culture Analysis, FilmArray Pneumonia Panel, Ventilation Impact, and Mortality Trends

Deciphering the Lung Microbiota in COVID-19 patients: Insights from Culture Analysis, FilmArray Pneumonia Panel, Ventilation Impact, and Mortality Trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deciphering the Lung Microbiota in COVID-19 patients: Insights from Culture Analysis, FilmArray Pneumonia Panel, Ventilation Impact, and Mortality Trends Francisco José Molina, Luz Elena Botero, Juan Pablo Isaza, Luz Elena Cano, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4659818/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Few studies have analyzed the role of the lung microbiome in the diagnosis of pulmonary coinfection in ventilated ICU COVID-19 patients. We characterized the lung microbiota in COVID-19 patients with severe pneumonia on invasive mechanical ventilation using full-length 16S rRNA gene sequencing and established its relationship with coinfections, mortality, and the need for mechanical ventilation for more than 7 days. This study included 67 COVID-19 ICU patients. DNA extracted from mini-bronchoalveolar lavage fluid and endotracheal aspirates was amplified by PCR with specific 16S primers (27F and 1492R). General and relative bacterial abundance analysis was also conducted. Alpha diversity was measured by the Shannon and Simpson indices, and differences in the microbiota were established using beta diversity. A linear discriminant analysis (LDA) effect size algorithm was implemented to describe biomarkers. Streptococcus spp. represented 51% of the overall microbial abundance. There were no differences in alpha diversity between the analyzed variables. There was variation in bacterial composition between samples that had positive and negative cultures. The genera Veillonella sp., Granulicatella sp., Enterococcus sp. and Lactiplantibacillus sp., with LDA scores > 2, were biomarkers associated with negative cultures. Rothia sp., with an LDA score > 2, was a biomarker associated with mortality. Biological sciences/Microbiology Health sciences/Diseases Health sciences/Medical research Health sciences/Molecular medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The lung microbiome has a profound impact on susceptibility to COVID-19 infection( 1 ). Mostafa H et al. suggested that there is dysbiosis of the lung microbiome in patients with COVID-19 and that the microbiome participates in the development of critical illness by influencing both homeostasis and proinflammatory conditions( 2 ). In COVID-19, respiratory microbiota dysbiosis could be associated with underactive and overactive immune responses, which can result in various clinical complications; however, it is not clear whether the alteration of the respiratory microbiome is an effect or cause of COVID-19( 3 ). The dysbiotic microbiome triggers lung inflammation through different pathways, such as kynurenine and the endocannabinoid system, and produces metabolites such as short-chain fatty acids and trimethylamine oxide that play a role in the excessive secretion of proinflammatory cytokines such as IL. -1β, IL-6, IL-17α and TNF-α( 4 ). Kullberg et al. found that in 114 patients with COVID-19 and acute respiratory distress syndrome (ARDS) on mechanical ventilation, there was an association between increased bacterial and fungal loads in the lung microbiota and a decreased probability of release from invasive mechanical ventilation (IMV) and increased mortality( 5 ). They included patients who were hospitalized longer than 48 hours, reflecting an analysis of the nosocomial microbiota with possible pulmonary superinfection and not of the community microbiota with pulmonary coinfection. Merenstein C et al. analyzed 56 studies that investigated the lung microbiota in patients with COVID-19, 31 of which performed sequencing with amplification of the 16S rRNA gene; of the latter, only three took samples from the lower respiratory tract (LRT) of patients hospitalized in the intensive care unit (ICU)( 6 ). Only one study evaluated the contribution of the 16S/18S rRNA gene to the diagnosis of pulmonary coinfections by endotracheal aspiration in patients on IMV( 7 ). The sample included 34 patients whose mean number of lung samples taken was greater than 48 hours prior to admission to the ICU [2.7 (0–16) days], and most patients (59%) had received antibiotics prior to respiratory sample collection, which interferes with the results. We characterized the lung microbiota of COVID-19 ICU patients on IMV who were hospitalized for less than 48 hours and who had not received antibiotics to evaluate lung bacterial taxonomy through full-length 16S rRNA gene sequencing of LRT samples and its relationship with lung coinfection based on the positivity of respiratory cultures and/or molecular tests of the Biofire® FilmArray® pneumonia panel (FA-PNEU). We also evaluated the relationships of lung bacterial taxonomy with mortality, prolonged mechanical ventilation (greater than 7 days) and the inflammatory response measured by cytokines. Results The clinical characteristics of the 67 patients are described in Table 1. Twenty-two (32.4%) patients died, 59 (88.1%) patients were on IMV on day 7 of their ICU stay, and 22.5% and 30.9% of the cultures and FA-PNEU were positive, respectively. Supplementary Fig. 1 describes the overall library size of the 67 samples. A total of 2009 ASVs were filtered, leaving 279. A total of 8,509,922 reads were obtained, with a median of 127,562 reads (p25-75, 7,765 − 457,678 reads). The rarefaction curve analysis of the samples is shown in supplementary Fig. 2. Visual exploration Figure 2a shows the relative abundance of the top ten taxa in all the samples. The three most common genera are Streptococcus , Veillonella and Staphylococcus . Figure 2b shows the general abundance profile, that is, the sum of each of the genera in all the samples, for the ten main taxa. Streptococcus accounted for 51%, and Haemophilus accounted for 12%. Bacterial community profile The heat profiles of the core microbiota are shown in Fig. 3. The genus Streptococcus was present in 90% of the samples with a minimum relative abundance of 0.027%, and Bacillus was present in 80% of the samples with a minimum relative abundance of 0.01%. The richness that determines the number of unique ASVs found in each sample at the genus level is shown in supplementary Fig. 3. There were no differences in richness between groups for the four variables: mortality (a), being ventilated on day 7 (b), and the positivity or not of the cultures (c) and the FA-PNEU (d). We also did not find differences in α diversity, measured with the Shannon and Simpson indices, between the four variables. Supplementary Fig. 4. The β diversity for each of the four variables, mortality, mechanical ventilation on day 7, cultures and FA-PNEU, is shown in Fig. 4. There was a significant difference (F value: 4.2393; R squared: 0.061227; p value: 0.004) only between the bacterial communities when the culture was positive or negative. Using LefSe, we found that only the culture and mortality variables were significantly different between the groups. Figure 5a shows the LEfSe at the sex level based on the culture results (positive or negative). At the genus level, the presence of Veillonella , Granulicatella , Enterococcus and Lactiplantibacillus , with an LDA score > 2, are biomarkers of negative cultures; in other words, the patients who had that taxon in their lung samples were potentially protected from bacterial coinfection. Figure 5b shows the unifactor statistical comparison at the genus level with Veillonella and the culture variable (positive or negative); Veillonella with p = 0.0289, is associated with negative culture. Figure 5c shows the effect size of the linear discriminant analysis (LEfSe) at the genus level based on the mortality variable (living or dead); the presence of Rothia , with an LDA score > 2, and the presence of Rothia , with an LDA score > 2, were associated with mortality. In other words, patients who had Rothia in their lung samples were more likely to die. Correlations between metataxonomy and serum cytokine levels Only three of the 8 evaluated cytokines were correlated with any microbial genus. Supplementary Table 1. Human granulocyte and macrophage colony-stimulating factor (GM-CSF) has a strong negative correlation with several microorganisms ( Catellicoccus , Microbacterium , Lachnospiraceae , Agathobacter ); that is, the lower the concentration of GM-CSF is, the greater the abundance of these bacterial genera. Interleukin 17 (IL-17A) has a strong positive correlation with the genera Acidibacillus and Anaerovorax , and interferon gamma (IFN-γ) has a moderate positive correlation with the genera Cupriavidus , Psychrobacillus and Erysipelatoclostridium . None of the 7 most frequently isolated microorganisms according to sequencing were correlated with cytokines. Discussion The most important findings of the present study are as follows: first, the genus Streptococcus spp. has the highest overall abundance; second, there are differences in bacterial composition between samples that had positive culture results and negative cultures; third, the presence of Veillonella sp., Granulicatella sp., Enterococcus sp. and Lactiplantibacillus sp. are biomarkers of negative cultures; and fourth, the presence of Rothia is a biomarker of mortality. Rothia sp. is associated with the development of ARDS in patients with COVID-19 pneumonia ( 11 ). Han Y et al. performed a metatranscriptomic study in BAL fluid from 19 patients with COVID-19 and 23 healthy controls and found a correlation between Rothia mucilaginosa and SARS-CoV-2, suggesting that it plays a role in the host's inflammatory response. We did not find a correlation between Rothia and IL-1β; we previously described that patients with IL-1β levels < 1,365 pg/mL had increased mortality( 12 ). The levels of only three cytokines, IFN-γ, GM-CSF and IL-17A, were correlated with the levels of some genera; however, we did not find studies on COVID-19 with similar findings. Veillonella sp., Granulicatella sp. and other opportunistic oral pathogens have been detected in the BAL fluid of patients with COVID-19( 13 ). Kumar D et al. reported that Veillonella was a biomarker for survival in COVID-19 pneumonia patients and improved the inflammatory response in these patients( 14 ). Meng H et al. performed metatranscriptomic sequencing in 72 patients with severe COVID-19 pneumonia and 57 patients who had already recovered, finding that Veillonella rodentium was a marker of recovery. We found that the presence of Veillonella in the lung samples of patients was a biomarker of negative cultures. Whether this lower probability of bacterial coinfection is a possible explanation for the higher probability of recovery is a possible hypothesis. We did not find similar studies in severe COVID-19 pneumonia that correlated the genera Veillonella sp., Granulicatella sp., Enterococcus sp. and Lactiplantibacillus sp. by metataxonomy with negative cultures. The main difficulty we had was the low concentration of the isolated DNA. The lung samples from these patients with severe COVID-19 pneumonia in the IMV were scant, bloody, and contained a large amount of mucus, so we performed pretreatment to break up and inactivate the mucus. We suggest conducting studies on how to optimize DNA and RNA extraction techniques in these patients. Other limitations were as follows: first, we did not have control samples from ventilated patients without pneumonia or from patients with pneumonia not related to COVID-9, which could serve as a reference for the community microbiota and be able to determine whether there was dysbiosis. Second, there was variability in the number of reads obtained from the samples, some with few reads and others with many reads. Third, metataxonomy does not provide species-level resolution for comparisons with cultures and the FA-PNEU. The strength of the study is the scarcity of literature on patients with severe pneumonia in the ICU at VMI; with less than 48 hours of hospitalization, the isolation of bacteria reflects possible bacterial coinfection. Thomsen K et al. evaluated the diagnosis of respiratory coinfections with the analysis of 16S/18S rRNA gene amplicons in 34 patients with severe COVID-19. They detected potential pathogens in four patients (12%) with the 16S gene, seven patients (21%) using conventional cultures, and one patient (3%) using a molecular respiratory panel. Fourteen patients had not received antibiotics, and four microorganisms (3 Haemophilus influenzae and 1 Fusobacterium necrophorum ) were detected in the 16S gene. They concluded that metataxonomy complements conventional microbial diagnosis( 7 ). Lloréns-Rico V et al. studied 21 patients with V4 amplification of the 16S gene in BAL fluid samples and demonstrated that the length of stay in the ICU, IMV and the use of antibiotics explained the greatest variation within the lung microbiota( 15 ). We did not find differences in the composition of the bacterial communities between the samples of patients who were or were not on IMV for more than 7 days or in mortality. Merenstein C et al analyzed V1-V2 of the 16S rRNA gene in endotracheal aspirates from 24 patients with COVID-19 on IMV, with a median of 4 days of hospitalization. Six patients had taxa dominated by Staphylococcus , and three were a prominent minority; only 3 patients had Staphylococcus aureus identified by culture, suggesting that sequencing was more sensitive than culture( 16 ). In our study, Staphylococcus was detected by taxonomy in 58 of the 67 lung samples (86.56%). Another strength is that the patients did not receive previous antibiotics, and their samples were taken in the first 12 hours of IMV, which reduces the probability of alteration of the bacterial composition. Castilhos et al. demonstrated that the use of antibiotics leads to greater dysbiosis in critically ill patients with COVID-19( 17 ). IMV alters the microbiome, allowing the growth of opportunistic pathogens( 6 ). We used Oxford Nanopore technology with a GridION (ONT) device, and the articles used Illumina MiSeq technology. Sequence reads are longer with Nanopore, and its accuracy, per base, is lower than that of MiSeq (95% vs. 99.9%)( 18 ), so we would be biased in performing comparisons. Heikema AP et al. compared both technologies in 59 nasal swab samples in which Nanopore presented problems in the detection of bacteria of the genus Corynebacterium ( 19 ). Another difference is that we sequenced the complete 16S rRNA gene and not the individual hypervariable regions, which improved the precision of the measurements of bacterial diversity( 20 ). Conclusion We found that the Streptococcus genus had the highest overall abundance. The presence of Veillonella , Granulicatella , Enterococcus and Lactiplantibacillus are biomarkers of negative cultures, and the presence of Rothia is a biomarker of mortality. Materials and Methods This was a case‒control study nested within a cohort of 139 patients included in the original study( 8 ). The patients were > 18 years of age and admitted to nine ICUs in Medellín, Colombia, with severe COVID-19 infection on mechanical ventilation. To meet the coinfection criteria, patients could not have been hospitalized for more than 48 h at the time of LRT sampling. Patients who had received any dose of empiric antimicrobial therapy were excluded. The study was conducted between March 1 and July 30, 2021. Respiratory therapists in each ICU collected samples from the lower respiratory tract on the first day of intubation with mini-bronchoalveolar lavage fluid [mini-BAL] or endotracheal aspirate [ETA]. Of the total volume of samples, 5–10 ml (mL) was distributed for BioFire® FilmArray® Pneumonia Panel (FA-PNEU) testing, 5–10 ml for conventional culturing, and another 5–10 ml for lung microbiota analysis with the extraction of deoxyribonucleic acid (DNA). The samples were not processed when a quality level of 0 or 1 was detected by the Murray/Washington criteria, based on the number of squamous cells and neutrophils per field( 9 ). A total of 139 samples were pretreated with 300 mL of isopropanol, 20 mL of 10 M NaOH, 179.1 mL of water and 0.9 mL of Tween-20 (volume of 500 mL) to break up mucus and inactivate respiratory samples. The protocol used was as follows: in a 15 ml conical tube, 400 µl of sample and 1,200 µl of pretreatment reagent were added, the tube was vortexed for 30 to 60 seconds, incubated at room temperature for a maximum of 2 hours and vortexed again. A total of 450 µl of the mixture was removed, placed in 1.5 ml vials and centrifuged at 13,000 rpm for 1 minute. Finally, 400 µl was removed without disturbing the generated pellet. The automated extraction protocol was then carried out with the MagMAX DNA Multi-Sample Ultra kit (Applied Biosystems, San Francisco, USA) using the KingFisher Flex System following the manufacturer's instructions for a final elution of 40 µl of genetic material. Qubit quantification was performed by fluorometric DNA quantification (Qubit dsDNA HS Assay kit, for Qubit 3.0, Life Technologies). Of the 139 samples, 89 had an optimal concentration (30 ng). Quality filtering was performed with conventional PCR using the universal primers 27F and 1492R, with the objective of verifying the amplification of the band corresponding to the 16S rRNA gene. A total of 67 samples were considered optimal, and the 16S rRNA gene was sequenced using Oxford Nanopore technology on a GridION (ONT) device. The flow chart of the study is shown in Fig. 1. Four negative controls (no DNA template control) were included in the analyses to control for contamination of the reagents. 16S DNA Sequencing DNA was amplified by PCR using specific 16S primers (27F and 1492R) containing 5' tags facilitating ligase-free ligation of rapid sequencing adapters ( https://store.nanoporetech.com/16 s-barcoding-kit-1-24.html). The 16S Barcoding Kit 1–24 SQK-16S024 (Oxford, Nanopore, Florida, USA) was used for library preparation and sequencing following the protocol recommended by the manufacturer. One microliter of the purified library was quantified using a Qubit 3.0 (Life Technologies, USA) following the manufacturer’s instructions. Subsequently, all the libraries were loaded into an R9.4 flow cell (Oxford Nanopore, UK) and run in the GrdION Mk1 system (Oxford Nanopore, UK). Predictor variables The main objective was to determine whether the bacterial taxonomy was positively related to the FA-PNEU and conventional culture results. Other objectives were to determine the relationships of the metataxonomy with mortality and remaining on IMV on day 7 of the ICU stay. We hypothesize that bacterial DNA richness, α diversity, and bacterial community composition would serve as predictors of the outcomes defined by these four variables. Additionally, we examined the relationship of bacterial taxonomy with the serum levels of eight cytokines (pg/ml) taken at ICU admission: IL-1β, IL-2, IL-6, IL-10, IL-12p70, IL-18, IFN-γ and TNFα. The Human ProcartaPlex TM Multiplex Immunoassay Mix & Match of 10-Plex system based on magnetic beads was used to detect the serum biomarker PPX-10-MX323G4 (Invitrogen, Whatman, Massachusetts, United States). The 10 cytokines were analyzed using the LuminexR MAGPIX R System (Thermo Fisher Scientific, Luminex Corporation 12212 Technology Blvd. Austin, Texas 78727)( 10 ). Bioinformatics analysis The generation of raw data in fastq format was performed with MinKNOW software. The adapters and chimeras were removed (porechop v:0.2.4 and fastp v0.23.4), leaving only reads between 1000 and 2000 base pairs with quality scores > 9. Taxonomic classification was carried out with Kraken v2.1.3 and the SILVA v138.1 database. We used the open access analysis platform MicrobiomeAnalyst ( https://www.microbiomeanalyst.ca ) with 4 variables: mortality (alive-dead), mechanical ventilation on day 7 (yes-no), culture (positive-negative) and FA-PNEU (positive-negative). Filtering of the ASVs was performed, excluding those with a low count of 3 reads in less than 20% of the samples. Cumulative sum scaling (CSS) was used for data normalization. We performed a visual exploration by calculating the relative abundance of all samples and an overall abundance profile using a pie chart. A profile of the bacterial community was generated through the following steps: one, a heatmap of the core microbiota at the genus taxonomic level, with a minimum sample prevalence of 20% and a relative abundance of 0.01%; two, the calculation of microbial richness at the genus level, with the use of the Mann‒Whitney test as a statistical method to compare the richness between the four variables; three, the α diversity measured with the Shannon and Simpson indices, with the use of the Mann‒Whitney test as a statistical method to compare the indices between the four variables; and four, the differences in the composition of the bacterial community, with β diversity using the main coordinate analysis, with the Bray‒Curtis index at the genus level, with the PERMANOVA statistical method. We used the linear discriminant analysis effect size (LEfSe) algorithm to identify and interpret sex-related biomarkers for each of the four variables, with the Kruskal‒Wallis rank sum test (p value of 0.1) and univariate statistical comparisons with the t test, with a p value < 0.05 indicating statistical significance. Statistical analyses The median and interquartile range were calculated for the continuous variables, and the categorical variables are presented as frequencies and percentages. To compare the concentrations of serum cytokines with those of microorganisms identified by metataxonomy, the Spearman correlation coefficient was used, with a predefined value greater or less than 0.6 and a p < 0.05. The data were analyzed using R version 4.2.3 software. Abbreviations COVID-19: Coronavirus disease 2019. SARS-CoV-2: severe acute respiratory syndrome coronavirus 2. LDA: Linear discriminant analysis. ARDS: Acute respiratory distress syndrome. IMV: invasive mechanical ventilation. LRT: Lower respiratory tract. ICU: intensive care unit. FA-PNEU: Biofire® FilmArray® pneumonia panel. DNA: Deoxyribonucleic acid. CSS: Cumulative sum scaling. LEfSe: discriminant analysis effect size. GM-CSF: Human granulocyte and macrophage colony-stimulating factor. IFN-γ: Interferon gamma. Declarations Ethical approval This study was approved by the ethics committee of the Universidad Pontificia Bolivariana and by the committees of the clinics and hospitals that participated in the study. Written informed consent was obtained from the participants or their legal representatives. All experiments were performed in accordance with relevant guidelines and regulations. Contributions FJM, LEB, JPI, LEC, LL, LV and AT designed the study; collected, compiled, analyzed and interpreted the data; and wrote the manuscript. AJA, IM, LSP, JU, KC, and JPH and QM performed laboratory analyses, interpreted the data and wrote the manuscript. RLA and LF interpreted the data and wrote the manuscript. All authors approved the final version of the manuscript. Ethical approval and consent to participate This study was approved by the ethics committee of the Universidad Pontificia Bolivariana and by the committees of the clinics and hospitals that participated in the study. Written informed consent was obtained from the participants or their legal representatives. Consent for publication Not applicable. Competing interests The authors have nothing to disclose and no competing interests. Funding This study was funded by Minciencias, Colombia, 121084468048. Author Contribution FJM, LEB, JPI, LEC, LL, LV and AT designed the study; collected, compiled, analyzed and interpreted the data; and wrote the manuscript. AJA, IM, LSP, JU, KC, and JPH and QM performed laboratory analyses, interpreted the data and wrote the manuscript. RLA and LF interpreted the data and wrote the manuscript. All authors approved the final version of the manuscript. Acknowledgement The authors acknowledge the institutions participating in the study with theRespective collaborators: Clínica Universitaria Bolivariana, Francisco Molina;Clínica El Rosario Tesoro, Álvaro Ochoa; Clínica CardioVid, Juan David Uribe;Clínica Sagrado Corazón, Nelson Fonseca; Clínica Las Américas Auna, BladimirGil; Clínica Medellín, Juan Echeverry; Hospital La María, Marco González;Hospital Manuel Uribe Ángel, Victoria Ángel; and Hospital Pablo Tobón Uribe,Gisella de la Rosa. Data Availability The datasets generated during and analysed during the current study are available in the National Center for Biotechnology Information Repository. The BioProject accession number is PRJNA1129550. The SRA records will be accessible with the following link: https://www.ncbi.nlm.nih.gov/sra/PRJNA1129550. References Khatiwadaa S, Subedic A. Lung microbiome and coronavirus disease 2019 (COVID-19): Possible link and implications. 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Genes (Basel). 2020;11(9):1–17. doi: 10.3390/genes11091105 . Rodicio MDR, Mendoza MDC. Identificación bacteriana mediante secuenciación del ARNr 16S: Fundamento, metodología y aplicaciones en microbiología clínica. Enferm Infecc Microbiol Clin. 2004;22(4):238–45. doi: 10.1157/13059055 . Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.pdf Graphicalabstract.pdf Supplementaryfigure1.pdf Supplementaryfigure2.pdf Supplementaryfigure3.pdf Supplementaryfigure4.pdf Supplementarytable1..pdf Cite Share Download PDF Status: Published Journal Publication published 03 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Oct, 2024 Reviews received at journal 07 Oct, 2024 Reviewers agreed at journal 19 Sep, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviews received at journal 30 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers invited by journal 15 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Editor invited by journal 01 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 29 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4659818","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329439777,"identity":"3c38d723-7327-4c6c-8c4f-e2db0128f4e0","order_by":0,"name":"Francisco José Molina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBAC9gYQaQDlPQAy+BkY2IBMZpxaeA4ga0kAMiQbiNICAwkg7QcIaZE+/OzBj4I6eX4G7sQHCQV35I1v5B57wFBhndjAfvgAVi18aeaGPQaHDWc28G42SDB4ZrjtRl66AcOZ9MQGnrQEbFrseRjMpIGOSTA4wLtNIsHgMOO2GzlmEoxthxMbJHgMsGnh4WH/BtRSB9div3kGSMs/fFp4QLYww7UkbpAAaWnAq6VMEuyXZrBfDifPOPMu3SDhWLpxGw6/AB22TeLHH2CIsfdufPDhz2Hb/nZgiH2osZbtxxFiCICICB5IBLHhV49qMwlqR8EoGAWjYCQAAA49WUGOW5+SAAAAAElFTkSuQmCC","orcid":"","institution":"Pontifical Bolivarian University","correspondingAuthor":true,"prefix":"","firstName":"Francisco","middleName":"José","lastName":"Molina","suffix":""},{"id":329439778,"identity":"20948ff4-0eeb-457f-8440-6ba45ab1f6af","order_by":1,"name":"Luz Elena Botero","email":"","orcid":"","institution":"Pontifical Bolivarian University","correspondingAuthor":false,"prefix":"","firstName":"Luz","middleName":"Elena","lastName":"Botero","suffix":""},{"id":329439779,"identity":"fd94c3c5-8760-4d95-aa0c-c39f7c7fb18e","order_by":2,"name":"Juan Pablo Isaza","email":"","orcid":"","institution":"Pontifical Bolivarian University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Pablo","lastName":"Isaza","suffix":""},{"id":329439782,"identity":"93a3d161-3802-4ecf-9a64-011a4b1af073","order_by":3,"name":"Luz Elena Cano","email":"","orcid":"","institution":"Pontifical Bolivarian University","correspondingAuthor":false,"prefix":"","firstName":"Luz","middleName":"Elena","lastName":"Cano","suffix":""},{"id":329439783,"identity":"7e85999a-2643-49d6-b104-e60072d4bfd2","order_by":4,"name":"Lucelly López","email":"","orcid":"","institution":"Pontifical Bolivarian University","correspondingAuthor":false,"prefix":"","firstName":"Lucelly","middleName":"","lastName":"López","suffix":""},{"id":329439786,"identity":"e99c11c2-066c-4c56-99a8-455d81bfa4b8","order_by":5,"name":"Luis Valdés","email":"","orcid":"","institution":"Pontifical Bolivarian University","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Valdés","suffix":""},{"id":329439787,"identity":"f85779be-2d1d-45ce-88b3-8134808c6422","order_by":6,"name":"Angela J. Arévalo Arbeláez","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"J. Arévalo","lastName":"Arbeláez","suffix":""},{"id":329439788,"identity":"8518b1ed-f5df-4b80-98ec-eb220810c7f9","order_by":7,"name":"Isabel Moreno","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Moreno","suffix":""},{"id":329439789,"identity":"b87cb7c0-45ea-4baf-8a2c-6a895ee3ca5d","order_by":8,"name":"Laura S. Pérez Restrepo","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"S. Pérez","lastName":"Restrepo","suffix":""},{"id":329439790,"identity":"ff0a9a69-13d7-42e4-88b1-6d6f56074ccd","order_by":9,"name":"Jaime Usuga","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Usuga","suffix":""},{"id":329439791,"identity":"ae298e7b-1341-4174-89f1-6f05ca582c6e","order_by":10,"name":"Karl Ciuoderis","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"","lastName":"Ciuoderis","suffix":""},{"id":329439793,"identity":"c9d7b5c1-61e6-4f85-9d07-47703d752144","order_by":11,"name":"Juan Pablo Hernandez","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Pablo","lastName":"Hernandez","suffix":""},{"id":329439796,"identity":"f353f3ff-b41d-4221-a06f-da5c9adf9611","order_by":12,"name":"Rubén López-Aladid","email":"","orcid":"","institution":"August Pi i Sunyer Biomedical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Rubén","middleName":"","lastName":"López-Aladid","suffix":""},{"id":329439798,"identity":"db68899d-b4ea-4c3c-b82f-68039caf6462","order_by":13,"name":"Laia Fernández","email":"","orcid":"","institution":"August Pi i Sunyer Biomedical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Laia","middleName":"","lastName":"Fernández","suffix":""},{"id":329439800,"identity":"980cfd79-261b-49f2-bdeb-f5eb54b3e36a","order_by":14,"name":"Antoni Torres","email":"","orcid":"","institution":"August Pi i Sunyer Biomedical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Antoni","middleName":"","lastName":"Torres","suffix":""}],"badges":[],"createdAt":"2024-06-29 14:54:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4659818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4659818/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-81738-8","type":"published","date":"2024-12-03T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61004961,"identity":"24e92376-01bb-4e14-8738-73705d6b000a","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37272,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/691c9bc92008a97e17f7745b.png"},{"id":61004963,"identity":"53852eca-106a-427a-9e1c-7bb62ae191f7","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30448,"visible":true,"origin":"","legend":"\u003cp\u003ea) Relative bacterial abundance of the 67 lung samples. b) General\u003c/p\u003e\n\u003cp\u003ebacterial abundance.\u003c/p\u003e\n\u003cp\u003eAbbreviations: g, genus.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/b8c7a022799b02895d8e016f.png"},{"id":61004959,"identity":"d4400472-9aab-41dc-80bd-8faf4db44223","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41820,"visible":true,"origin":"","legend":"\u003cp\u003eCore microbiota heat map\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/a858ae2f45a52446aa7ace87.png"},{"id":61004962,"identity":"adb42a9d-f70b-43f7-9252-71625897de43","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity β in each of the four variables: mortality (a), mechanical ventilation on day 7 (b), cultures (c) and AF-PNEU (d) in the 67 lung samples from the 67 patients with severe COVID-19 pneumonia. 19 admitted to Intensive Care Units in Colombia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is only statistical significance between the bacterial communities with the cultures variable.\u003c/p\u003e\n\u003cp\u003eAbbreviations: p: p value with the PERNANOVA statistical method; R: coefficient of determination; F: F test, ratio of two variances; FA-PNEU: Biofire® FilmArray® Pneumonia Panel.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/927715c03e05ab20649fb393.png"},{"id":61004970,"identity":"f00700b4-83f6-4e29-9aee-279381c18ae8","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132341,"visible":true,"origin":"","legend":"\u003cp\u003ea) LEfSe at the gender level based on the culture results (positive or\u003c/p\u003e\n\u003cp\u003enegative), in the 67 lung samples of the 67 patients with COVID-19 pneumonia\u003c/p\u003e\n\u003cp\u003eadmitted to Care Units Intensives in Colombia. b) Unifactor statistical\u003c/p\u003e\n\u003cp\u003ecomparison, at the genus level with Veillonella and the culture variable\u003c/p\u003e\n\u003cp\u003e(positive or negative). c) Effect size of the linear discriminant analysis (LEfSe)\u003c/p\u003e\n\u003cp\u003eat the genus level based on the mortality variable (living or dead).\u003c/p\u003e\n\u003cp\u003eAbbreviations: LDA: linear discriminant analysis; g: genus. High: high probability; Low: low probability.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/9d5356f2884a2585d3c42ae1.png"},{"id":70965254,"identity":"4c21656a-561e-4c31-bc01-c3385114a328","added_by":"auto","created_at":"2024-12-09 16:18:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":823685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/58bcb6d4-967d-4cd5-9a38-25b167b3b9b1.pdf"},{"id":61004958,"identity":"d091b350-c2a0-4144-a494-979a6ee5603a","added_by":"auto","created_at":"2024-07-24 13:40:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62471,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/988de42113e402938ac0dd81.pdf"},{"id":61004966,"identity":"abaf58d4-a59a-4db0-9977-853b67f87874","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":171067,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/14daedcc97de3e2446e5b8e3.pdf"},{"id":61004965,"identity":"b7fdefe6-f4d3-4df6-98b0-0b6c31f787c4","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":233077,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/66fd3f8e857a45f470f7d445.pdf"},{"id":61006217,"identity":"025af7e7-1273-44bd-941c-f3c4d8f5379d","added_by":"auto","created_at":"2024-07-24 13:48:43","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":114782,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/fbc6f4f851315638134850d4.pdf"},{"id":61006219,"identity":"8300d446-22b6-4ba2-a96a-ebbd4c779fc7","added_by":"auto","created_at":"2024-07-24 13:48:43","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":30591,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/caf58fb977f90e7294c985a7.pdf"},{"id":61004968,"identity":"b7658d90-147a-4aad-978b-c57d5c9fbd1d","added_by":"auto","created_at":"2024-07-24 13:40:43","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":116853,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/8d73d5e4e9cef50de1c7f30f.pdf"},{"id":61006218,"identity":"e19e2138-cbb8-42bb-815f-6e315e63cdc9","added_by":"auto","created_at":"2024-07-24 13:48:43","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":103511,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1..pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659818/v1/831f136fa2078c1f4374a84e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering the Lung Microbiota in COVID-19 patients: Insights from Culture Analysis, FilmArray Pneumonia Panel, Ventilation Impact, and Mortality Trends","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe lung microbiome has a profound impact on susceptibility to COVID-19 infection(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Mostafa H et al. suggested that there is dysbiosis of the lung microbiome in patients with COVID-19 and that the microbiome participates in the development of critical illness by influencing both homeostasis and proinflammatory conditions(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In COVID-19, respiratory microbiota dysbiosis could be associated with underactive and overactive immune responses, which can result in various clinical complications; however, it is not clear whether the alteration of the respiratory microbiome is an effect or cause of COVID-19(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dysbiotic microbiome triggers lung inflammation through different pathways, such as kynurenine and the endocannabinoid system, and produces metabolites such as short-chain fatty acids and trimethylamine oxide that play a role in the excessive secretion of proinflammatory cytokines such as IL. -1β, IL-6, IL-17α and TNF-α(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKullberg et al. found that in 114 patients with COVID-19 and acute respiratory distress syndrome (ARDS) on mechanical ventilation, there was an association between increased bacterial and fungal loads in the lung microbiota and a decreased probability of release from invasive mechanical ventilation (IMV) and increased mortality(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). They included patients who were hospitalized longer than 48 hours, reflecting an analysis of the nosocomial microbiota with possible pulmonary superinfection and not of the community microbiota with pulmonary coinfection. Merenstein C et al. analyzed 56 studies that investigated the lung microbiota in patients with COVID-19, 31 of which performed sequencing with amplification of the 16S rRNA gene; of the latter, only three took samples from the lower respiratory tract (LRT) of patients hospitalized in the intensive care unit (ICU)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Only one study evaluated the contribution of the 16S/18S rRNA gene to the diagnosis of pulmonary coinfections by endotracheal aspiration in patients on IMV(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The sample included 34 patients whose mean number of lung samples taken was greater than 48 hours prior to admission to the ICU [2.7 (0\u0026ndash;16) days], and most patients (59%) had received antibiotics prior to respiratory sample collection, which interferes with the results.\u003c/p\u003e \u003cp\u003eWe characterized the lung microbiota of COVID-19 ICU patients on IMV who were hospitalized for less than 48 hours and who had not received antibiotics to evaluate lung bacterial taxonomy through full-length 16S rRNA gene sequencing of LRT samples and its relationship with lung coinfection based on the positivity of respiratory cultures and/or molecular tests of the Biofire\u0026reg; FilmArray\u0026reg; pneumonia panel (FA-PNEU). We also evaluated the relationships of lung bacterial taxonomy with mortality, prolonged mechanical ventilation (greater than 7 days) and the inflammatory response measured by cytokines.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe clinical characteristics of the 67 patients are described in Table\u0026nbsp;1. Twenty-two (32.4%) patients died, 59 (88.1%) patients were on IMV on day 7 of their ICU stay, and 22.5% and 30.9% of the cultures and FA-PNEU were positive, respectively.\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;1 describes the overall library size of the 67 samples. A total of 2009 ASVs were filtered, leaving 279. A total of 8,509,922 reads were obtained, with a median of 127,562 reads (p25-75, 7,765\u0026thinsp;\u0026minus;\u0026thinsp;457,678 reads). The rarefaction curve analysis of the samples is shown in supplementary Fig.\u0026nbsp;2.\u003c/p\u003e\n\u003ch3\u003eVisual exploration\u003c/h3\u003e\n\u003cp\u003eFigure 2a shows the relative abundance of the top ten taxa in all the samples. The three most common genera are \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e. Figure\u0026nbsp;2b shows the general abundance profile, that is, the sum of each of the genera in all the samples, for the ten main taxa. \u003cem\u003eStreptococcus\u003c/em\u003e accounted for 51%, and \u003cem\u003eHaemophilus\u003c/em\u003e accounted for 12%.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBacterial community profile\u003c/h2\u003e \u003cp\u003eThe heat profiles of the core microbiota are shown in Fig.\u0026nbsp;3. The genus \u003cem\u003eStreptococcus\u003c/em\u003e was present in 90% of the samples with a minimum relative abundance of 0.027%, and \u003cem\u003eBacillus\u003c/em\u003e was present in 80% of the samples with a minimum relative abundance of 0.01%.\u003c/p\u003e \u003cp\u003eThe richness that determines the number of unique ASVs found in each sample at the genus level is shown in supplementary Fig.\u0026nbsp;3. There were no differences in richness between groups for the four variables: mortality (a), being ventilated on day 7 (b), and the positivity or not of the cultures (c) and the FA-PNEU (d).\u003c/p\u003e \u003cp\u003eWe also did not find differences in α diversity, measured with the Shannon and Simpson indices, between the four variables. Supplementary Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eThe β diversity for each of the four variables, mortality, mechanical ventilation on day 7, cultures and FA-PNEU, is shown in Fig.\u0026nbsp;4. There was a significant difference (F value: 4.2393; R squared: 0.061227; p value: 0.004) only between the bacterial communities when the culture was positive or negative.\u003c/p\u003e \u003cp\u003eUsing LefSe, we found that only the culture and mortality variables were significantly different between the groups. Figure\u0026nbsp;5a shows the LEfSe at the sex level based on the culture results (positive or negative). At the genus level, the presence of \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eGranulicatella\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eLactiplantibacillus\u003c/em\u003e, with an LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2, are biomarkers of negative cultures; in other words, the patients who had that taxon in their lung samples were potentially protected from bacterial coinfection. Figure\u0026nbsp;5b shows the unifactor statistical comparison at the genus level with \u003cem\u003eVeillonella\u003c/em\u003e and the culture variable (positive or negative); \u003cem\u003eVeillonella\u003c/em\u003e with p\u0026thinsp;=\u0026thinsp;0.0289, is associated with negative culture. Figure\u0026nbsp;5c shows the effect size of the linear discriminant analysis (LEfSe) at the genus level based on the mortality variable (living or dead); the presence of \u003cem\u003eRothia\u003c/em\u003e, with an LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2, and the presence of \u003cem\u003eRothia\u003c/em\u003e, with an LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2, were associated with mortality. In other words, patients who had \u003cem\u003eRothia\u003c/em\u003e in their lung samples were more likely to die.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between metataxonomy and serum cytokine levels\u003c/h2\u003e \u003cp\u003eOnly three of the 8 evaluated cytokines were correlated with any microbial genus. Supplementary Table\u0026nbsp;1. Human granulocyte and macrophage colony-stimulating factor (GM-CSF) has a strong negative correlation with several microorganisms (\u003cem\u003eCatellicoccus\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eAgathobacter\u003c/em\u003e); that is, the lower the concentration of GM-CSF is, the greater the abundance of these bacterial genera. Interleukin 17 (IL-17A) has a strong positive correlation with the genera \u003cem\u003eAcidibacillus\u003c/em\u003e and \u003cem\u003eAnaerovorax\u003c/em\u003e, and interferon gamma (IFN-γ) has a moderate positive correlation with the genera \u003cem\u003eCupriavidus\u003c/em\u003e, \u003cem\u003ePsychrobacillus\u003c/em\u003e and \u003cem\u003eErysipelatoclostridium\u003c/em\u003e. None of the 7 most frequently isolated microorganisms according to sequencing were correlated with cytokines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe most important findings of the present study are as follows: first, the genus \u003cem\u003eStreptococcus spp.\u003c/em\u003e has the highest overall abundance; second, there are differences in bacterial composition between samples that had positive culture results and negative cultures; third, the presence of \u003cem\u003eVeillonella\u003c/em\u003e sp., \u003cem\u003eGranulicatella\u003c/em\u003e sp., \u003cem\u003eEnterococcus\u003c/em\u003e sp. and \u003cem\u003eLactiplantibacillus\u003c/em\u003e sp. are biomarkers of negative cultures; and fourth, the presence of \u003cem\u003eRothia\u003c/em\u003e is a biomarker of mortality.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRothia sp.\u003c/em\u003e is associated with the development of ARDS in patients with COVID-19 pneumonia (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Han Y et al. performed a metatranscriptomic study in BAL fluid from 19 patients with COVID-19 and 23 healthy controls and found a correlation between \u003cem\u003eRothia mucilaginosa\u003c/em\u003e and SARS-CoV-2, suggesting that it plays a role in the host's inflammatory response. We did not find a correlation between \u003cem\u003eRothia\u003c/em\u003e and IL-1β; we previously described that patients with IL-1β levels\u0026thinsp;\u0026lt;\u0026thinsp;1,365 pg/mL had increased mortality(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The levels of only three cytokines, IFN-γ, GM-CSF and IL-17A, were correlated with the levels of some genera; however, we did not find studies on COVID-19 with similar findings.\u003c/p\u003e \u003cp\u003e \u003cem\u003eVeillonella\u003c/em\u003e sp., \u003cem\u003eGranulicatella\u003c/em\u003e sp. and other opportunistic oral pathogens have been detected in the BAL fluid of patients with COVID-19(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Kumar D et al. reported that \u003cem\u003eVeillonella\u003c/em\u003e was a biomarker for survival in COVID-19 pneumonia patients and improved the inflammatory response in these patients(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Meng H et al. performed metatranscriptomic sequencing in 72 patients with severe COVID-19 pneumonia and 57 patients who had already recovered, finding that \u003cem\u003eVeillonella rodentium\u003c/em\u003e was a marker of recovery. We found that the presence of \u003cem\u003eVeillonella\u003c/em\u003e in the lung samples of patients was a biomarker of negative cultures. Whether this lower probability of bacterial coinfection is a possible explanation for the higher probability of recovery is a possible hypothesis. We did not find similar studies in severe COVID-19 pneumonia that correlated the genera \u003cem\u003eVeillonella\u003c/em\u003e sp., \u003cem\u003eGranulicatella\u003c/em\u003e sp., \u003cem\u003eEnterococcus\u003c/em\u003e sp. and \u003cem\u003eLactiplantibacillus\u003c/em\u003e sp. by metataxonomy with negative cultures.\u003c/p\u003e \u003cp\u003eThe main difficulty we had was the low concentration of the isolated DNA. The lung samples from these patients with severe COVID-19 pneumonia in the IMV were scant, bloody, and contained a large amount of mucus, so we performed pretreatment to break up and inactivate the mucus. We suggest conducting studies on how to optimize DNA and RNA extraction techniques in these patients.\u003c/p\u003e \u003cp\u003eOther limitations were as follows: first, we did not have control samples from ventilated patients without pneumonia or from patients with pneumonia not related to COVID-9, which could serve as a reference for the community microbiota and be able to determine whether there was dysbiosis. Second, there was variability in the number of reads obtained from the samples, some with few reads and others with many reads. Third, metataxonomy does not provide species-level resolution for comparisons with cultures and the FA-PNEU.\u003c/p\u003e \u003cp\u003eThe strength of the study is the scarcity of literature on patients with severe pneumonia in the ICU at VMI; with less than 48 hours of hospitalization, the isolation of bacteria reflects possible bacterial coinfection. Thomsen K et al. evaluated the diagnosis of respiratory coinfections with the analysis of 16S/18S rRNA gene amplicons in 34 patients with severe COVID-19. They detected potential pathogens in four patients (12%) with the 16S gene, seven patients (21%) using conventional cultures, and one patient (3%) using a molecular respiratory panel. Fourteen patients had not received antibiotics, and four microorganisms (3 \u003cem\u003eHaemophilus influenzae\u003c/em\u003e and 1 \u003cem\u003eFusobacterium necrophorum\u003c/em\u003e) were detected in the 16S gene. They concluded that metataxonomy complements conventional microbial diagnosis(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLlor\u0026eacute;ns-Rico V et al. studied 21 patients with V4 amplification of the 16S gene in BAL fluid samples and demonstrated that the length of stay in the ICU, IMV and the use of antibiotics explained the greatest variation within the lung microbiota(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). We did not find differences in the composition of the bacterial communities between the samples of patients who were or were not on IMV for more than 7 days or in mortality. Merenstein C et al analyzed V1-V2 of the 16S rRNA gene in endotracheal aspirates from 24 patients with COVID-19 on IMV, with a median of 4 days of hospitalization. Six patients had taxa dominated by \u003cem\u003eStaphylococcus\u003c/em\u003e, and three were a prominent minority; only 3 patients had \u003cem\u003eStaphylococcus aureus\u003c/em\u003e identified by culture, suggesting that sequencing was more sensitive than culture(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In our study, \u003cem\u003eStaphylococcus\u003c/em\u003e was detected by taxonomy in 58 of the 67 lung samples (86.56%).\u003c/p\u003e \u003cp\u003eAnother strength is that the patients did not receive previous antibiotics, and their samples were taken in the first 12 hours of IMV, which reduces the probability of alteration of the bacterial composition. Castilhos et al. demonstrated that the use of antibiotics leads to greater dysbiosis in critically ill patients with COVID-19(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). IMV alters the microbiome, allowing the growth of opportunistic pathogens(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used Oxford Nanopore technology with a GridION (ONT) device, and the articles used Illumina MiSeq technology. Sequence reads are longer with Nanopore, and its accuracy, per base, is lower than that of MiSeq (95% vs. 99.9%)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), so we would be biased in performing comparisons. Heikema AP et al. compared both technologies in 59 nasal swab samples in which Nanopore presented problems in the detection of bacteria of the genus \u003cem\u003eCorynebacterium\u003c/em\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another difference is that we sequenced the complete 16S rRNA gene and not the individual hypervariable regions, which improved the precision of the measurements of bacterial diversity(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found that the \u003cem\u003eStreptococcus\u003c/em\u003e genus had the highest overall abundance. The presence of \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eGranulicatella\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eLactiplantibacillus\u003c/em\u003e are biomarkers of negative cultures, and the presence of \u003cem\u003eRothia\u003c/em\u003e is a biomarker of mortality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis was a case‒control study nested within a cohort of 139 patients included in the original study(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The patients were \u0026gt;\u0026thinsp;18 years of age and admitted to nine ICUs in Medell\u0026iacute;n, Colombia, with severe COVID-19 infection on mechanical ventilation. To meet the coinfection criteria, patients could not have been hospitalized for more than 48 h at the time of LRT sampling. Patients who had received any dose of empiric antimicrobial therapy were excluded. The study was conducted between March 1 and July 30, 2021. Respiratory therapists in each ICU collected samples from the lower respiratory tract on the first day of intubation with mini-bronchoalveolar lavage fluid [mini-BAL] or endotracheal aspirate [ETA]. Of the total volume of samples, 5\u0026ndash;10 ml (mL) was distributed for BioFire\u0026reg; FilmArray\u0026reg; Pneumonia Panel (FA-PNEU) testing, 5\u0026ndash;10 ml for conventional culturing, and another 5\u0026ndash;10 ml for lung microbiota analysis with the extraction of deoxyribonucleic acid (DNA). The samples were not processed when a quality level of 0 or 1 was detected by the Murray/Washington criteria, based on the number of squamous cells and neutrophils per field(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 139 samples were pretreated with 300 mL of isopropanol, 20 mL of 10 M NaOH, 179.1 mL of water and 0.9 mL of Tween-20 (volume of 500 mL) to break up mucus and inactivate respiratory samples. The protocol used was as follows: in a 15 ml conical tube, 400 \u0026micro;l of sample and 1,200 \u0026micro;l of pretreatment reagent were added, the tube was vortexed for 30 to 60 seconds, incubated at room temperature for a maximum of 2 hours and vortexed again. A total of 450 \u0026micro;l of the mixture was removed, placed in 1.5 ml vials and centrifuged at 13,000 rpm for 1 minute. Finally, 400 \u0026micro;l was removed without disturbing the generated pellet. The automated extraction protocol was then carried out with the MagMAX DNA Multi-Sample Ultra kit (Applied Biosystems, San Francisco, USA) using the KingFisher Flex System following the manufacturer's instructions for a final elution of 40 \u0026micro;l of genetic material. Qubit quantification was performed by fluorometric DNA quantification (Qubit dsDNA HS Assay kit, for Qubit 3.0, Life Technologies). Of the 139 samples, 89 had an optimal concentration (30 ng).\u003c/p\u003e \u003cp\u003eQuality filtering was performed with conventional PCR using the universal primers 27F and 1492R, with the objective of verifying the amplification of the band corresponding to the 16S rRNA gene. A total of 67 samples were considered optimal, and the 16S rRNA gene was sequenced using Oxford Nanopore technology on a GridION (ONT) device. The flow chart of the study is shown in Fig.\u0026nbsp;1. Four negative controls (no DNA template control) were included in the analyses to control for contamination of the reagents.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e16S DNA Sequencing\u003c/h2\u003e \u003cp\u003eDNA was amplified by PCR using specific 16S primers (27F and 1492R) containing 5' tags facilitating ligase-free ligation of rapid sequencing adapters (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://store.nanoporetech.com/16\u003c/span\u003e\u003cspan address=\"https://store.nanoporetech.com/16\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e s-barcoding-kit-1-24.html).\u003c/p\u003e \u003cp\u003eThe 16S Barcoding Kit 1\u0026ndash;24 SQK-16S024 (Oxford, Nanopore, Florida, USA) was used for library preparation and sequencing following the protocol recommended by the manufacturer. One microliter of the purified library was quantified using a Qubit 3.0 (Life Technologies, USA) following the manufacturer\u0026rsquo;s instructions. Subsequently, all the libraries were loaded into an R9.4 flow cell (Oxford Nanopore, UK) and run in the GrdION Mk1 system (Oxford Nanopore, UK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePredictor variables\u003c/h2\u003e \u003cp\u003eThe main objective was to determine whether the bacterial taxonomy was positively related to the FA-PNEU and conventional culture results. Other objectives were to determine the relationships of the metataxonomy with mortality and remaining on IMV on day 7 of the ICU stay. We hypothesize that bacterial DNA richness, α diversity, and bacterial community composition would serve as predictors of the outcomes defined by these four variables.\u003c/p\u003e \u003cp\u003eAdditionally, we examined the relationship of bacterial taxonomy with the serum levels of eight cytokines (pg/ml) taken at ICU admission: IL-1β, IL-2, IL-6, IL-10, IL-12p70, IL-18, IFN-γ and TNFα. The Human ProcartaPlex TM Multiplex Immunoassay Mix \u0026amp; Match of 10-Plex system based on magnetic beads was used to detect the serum biomarker PPX-10-MX323G4 (Invitrogen, Whatman, Massachusetts, United States). The 10 cytokines were analyzed using the LuminexR MAGPIX R System (Thermo Fisher Scientific, Luminex Corporation 12212 Technology Blvd. Austin, Texas 78727)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003eThe generation of raw data in fastq format was performed with MinKNOW software. The adapters and chimeras were removed (porechop v:0.2.4 and fastp v0.23.4), leaving only reads between 1000 and 2000 base pairs with quality scores\u0026thinsp;\u0026gt;\u0026thinsp;9. Taxonomic classification was carried out with Kraken v2.1.3 and the SILVA v138.1 database.\u003c/p\u003e \u003cp\u003eWe used the open access analysis platform MicrobiomeAnalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.microbiomeanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.microbiomeanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with 4 variables: mortality (alive-dead), mechanical ventilation on day 7 (yes-no), culture (positive-negative) and FA-PNEU (positive-negative). Filtering of the ASVs was performed, excluding those with a low count of 3 reads in less than 20% of the samples. Cumulative sum scaling (CSS) was used for data normalization.\u003c/p\u003e \u003cp\u003eWe performed a visual exploration by calculating the relative abundance of all samples and an overall abundance profile using a pie chart. A profile of the bacterial community was generated through the following steps: one, a heatmap of the core microbiota at the genus taxonomic level, with a minimum sample prevalence of 20% and a relative abundance of 0.01%; two, the calculation of microbial richness at the genus level, with the use of the Mann‒Whitney test as a statistical method to compare the richness between the four variables; three, the α diversity measured with the Shannon and Simpson indices, with the use of the Mann‒Whitney test as a statistical method to compare the indices between the four variables; and four, the differences in the composition of the bacterial community, with β diversity using the main coordinate analysis, with the Bray‒Curtis index at the genus level, with the PERMANOVA statistical method.\u003c/p\u003e \u003cp\u003eWe used the linear discriminant analysis effect size (LEfSe) algorithm to identify and interpret sex-related biomarkers for each of the four variables, with the Kruskal‒Wallis rank sum test (p value of 0.1) and univariate statistical comparisons with the t test, with a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe median and interquartile range were calculated for the continuous variables, and the categorical variables are presented as frequencies and percentages. To compare the concentrations of serum cytokines with those of microorganisms identified by metataxonomy, the Spearman correlation coefficient was used, with a predefined value greater or less than 0.6 and a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The data were analyzed using R version 4.2.3 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOVID-19: Coronavirus disease 2019.\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2: severe acute respiratory syndrome\u0026nbsp;coronavirus\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003eLDA: Linear discriminant analysis.\u003c/p\u003e\n\u003cp\u003eARDS: Acute respiratory distress syndrome.\u003c/p\u003e\n\u003cp\u003eIMV: invasive mechanical ventilation.\u003c/p\u003e\n\u003cp\u003eLRT: Lower respiratory tract.\u003c/p\u003e\n\u003cp\u003eICU: intensive care unit.\u003c/p\u003e\n\u003cp\u003eFA-PNEU: Biofire\u0026reg; FilmArray\u0026reg; pneumonia panel.\u003c/p\u003e\n\u003cp\u003eDNA: Deoxyribonucleic acid.\u003c/p\u003e\n\u003cp\u003eCSS: Cumulative sum scaling.\u003c/p\u003e\n\u003cp\u003eLEfSe: discriminant analysis effect size.\u003c/p\u003e\n\u003cp\u003eGM-CSF: Human granulocyte and macrophage colony-stimulating factor.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IFN-\u0026gamma;: Interferon gamma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003e This study was approved by the ethics committee of the Universidad Pontificia Bolivariana and by the committees of the clinics and hospitals that participated in the study. Written informed consent was obtained from the participants or their legal representatives. All experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eFJM, LEB, JPI, LEC, LL, LV and AT designed the study; collected, compiled, analyzed and interpreted the data; and wrote the manuscript. AJA, IM, LSP, JU, KC, and JPH and QM performed laboratory analyses, interpreted the data and wrote the manuscript. RLA and LF interpreted the data and wrote the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical approval and consent to participate\u003c/h2\u003e \u003cp\u003eThis study was approved by the ethics committee of the Universidad Pontificia Bolivariana and by the committees of the clinics and hospitals that participated in the study. Written informed consent was obtained from the participants or their legal representatives.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors have nothing to disclose and no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Minciencias, Colombia, 121084468048.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFJM, LEB, JPI, LEC, LL, LV and AT designed the study; collected, compiled, analyzed and interpreted the data; and wrote the manuscript. AJA, IM, LSP, JU, KC, and JPH and QM performed laboratory analyses, interpreted the data and wrote the manuscript. RLA and LF interpreted the data and wrote the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the institutions participating in the study with theRespective collaborators: Cl\u0026iacute;nica Universitaria Bolivariana, Francisco Molina;Cl\u0026iacute;nica El Rosario Tesoro, \u0026Aacute;lvaro Ochoa; Cl\u0026iacute;nica CardioVid, Juan David Uribe;Cl\u0026iacute;nica Sagrado Coraz\u0026oacute;n, Nelson Fonseca; Cl\u0026iacute;nica Las Am\u0026eacute;ricas Auna, BladimirGil; Cl\u0026iacute;nica Medell\u0026iacute;n, Juan Echeverry; Hospital La Mar\u0026iacute;a, Marco Gonz\u0026aacute;lez;Hospital Manuel Uribe \u0026Aacute;ngel, Victoria \u0026Aacute;ngel; and Hospital Pablo Tob\u0026oacute;n Uribe,Gisella de la Rosa.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and analysed during the current study are available in the National Center for Biotechnology Information Repository. The BioProject accession number is PRJNA1129550. The SRA records will be accessible with the following link: https://www.ncbi.nlm.nih.gov/sra/PRJNA1129550.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhatiwadaa S, Subedic A. Lung microbiome and coronavirus disease 2019 (COVID-19): Possible link and implications. Hum Microbiome J. 2020;17(August):1\u0026ndash;7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.humic.2020.100073\u003c/span\u003e\u003cspan address=\"10.1016/j.humic.2020.100073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMostafa HH, Fissel JA, Fanelli B, Bergman Y, Gniazdowski V, Dadlani M, et al. Metagenomic next-generation sequencing of nasopharyngeal specimens collected from confirmed and suspect covid-19 patients. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-36101-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-36101-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeikema AP, Horst-Kreft D, Boers SA, Jansen R, Hiltemann SD, de Koning W, et al. Comparison of illumina versus nanopore 16 s rRNA gene sequencing of the human nasal microbiota. Genes (Basel). 2020;11(9):1\u0026ndash;17. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes11091105\u003c/span\u003e\u003cspan address=\"10.3390/genes11091105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodicio MDR, Mendoza MDC. Identificaci\u0026oacute;n bacteriana mediante secuenciaci\u0026oacute;n del ARNr 16S: Fundamento, metodolog\u0026iacute;a y aplicaciones en microbiolog\u0026iacute;a cl\u0026iacute;nica. Enferm Infecc Microbiol Clin. 2004;22(4):238\u0026ndash;45. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1157/13059055\u003c/span\u003e\u003cspan address=\"10.1157/13059055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4659818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4659818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFew studies have analyzed the role of the lung microbiome in the diagnosis of pulmonary coinfection in ventilated ICU COVID-19 patients. We characterized the lung microbiota in COVID-19 patients with severe pneumonia on invasive mechanical ventilation using full-length 16S rRNA gene sequencing and established its relationship with coinfections, mortality, and the need for mechanical ventilation for more than 7 days. This study included 67 COVID-19 ICU patients. DNA extracted from mini-bronchoalveolar lavage fluid and endotracheal aspirates was amplified by PCR with specific 16S primers (27F and 1492R). General and relative bacterial abundance analysis was also conducted. Alpha diversity was measured by the Shannon and Simpson indices, and differences in the microbiota were established using beta diversity. A linear discriminant analysis (LDA) effect size algorithm was implemented to describe biomarkers. \u003cem\u003eStreptococcus\u003c/em\u003e spp. represented 51% of the overall microbial abundance. There were no differences in alpha diversity between the analyzed variables. There was variation in bacterial composition between samples that had positive and negative cultures. The genera \u003cem\u003eVeillonella\u003c/em\u003e sp., \u003cem\u003eGranulicatella\u003c/em\u003e sp., \u003cem\u003eEnterococcus\u003c/em\u003e sp. and \u003cem\u003eLactiplantibacillus\u003c/em\u003e sp., with LDA scores\u0026thinsp;\u0026gt;\u0026thinsp;2, were biomarkers associated with negative cultures. \u003cem\u003eRothia\u003c/em\u003e sp., with an LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2, was a biomarker associated with mortality.\u003c/p\u003e","manuscriptTitle":"Deciphering the Lung Microbiota in COVID-19 patients: Insights from Culture Analysis, FilmArray Pneumonia Panel, Ventilation Impact, and Mortality Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 13:40:37","doi":"10.21203/rs.3.rs-4659818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-09T13:53:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-07T07:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223964989174524192201184121345811881844","date":"2024-09-19T10:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294809027806652016803211276477690986959","date":"2024-08-22T01:23:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T03:56:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157921389623264584733704463637085344985","date":"2024-07-15T15:02:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-15T07:42:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T07:26:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-01T16:40:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T16:16:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-29T14:53:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e66a077e-3fac-4aed-bf97-78e105d03c07","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34877208,"name":"Biological sciences/Microbiology"},{"id":34877209,"name":"Health sciences/Diseases"},{"id":34877210,"name":"Health sciences/Medical research"},{"id":34877211,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2024-12-09T16:09:36+00:00","versionOfRecord":{"articleIdentity":"rs-4659818","link":"https://doi.org/10.1038/s41598-024-81738-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-03 15:57:31","publishedOnDateReadable":"December 3rd, 2024"},"versionCreatedAt":"2024-07-24 13:40:37","video":"","vorDoi":"10.1038/s41598-024-81738-8","vorDoiUrl":"https://doi.org/10.1038/s41598-024-81738-8","workflowStages":[]},"version":"v1","identity":"rs-4659818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4659818","identity":"rs-4659818","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}