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      The gut-lung axis in critical illness: microbiome composition as a predictor of mortality at day 28 in mechanically ventilated patients

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          Abstract

          Background

          Microbial communities are of critical importance in the human host. The lung and gut microbial communities represent the most essential microbiota within the human body, collectively referred to as the gut-lung axis. However, the differentiation between these communities and their influence on clinical outcomes in critically ill patients remains uncertain.

          Methods

          An observational cohort study was obtained in the intensive care unit (ICU) of an affiliated university hospital. Sequential samples were procured from two distinct anatomical sites, namely the respiratory and intestinal tracts, at two precisely defined time intervals: within 48 h and on day 7 following intubation. Subsequently, these samples underwent a comprehensive analysis to characterize microbial communities using 16S ribosomal RNA (rRNA) gene sequencing and to quantify concentrations of fecal short-chain fatty acids (SCFAs). The primary predictors in this investigation included lung and gut microbial diversity, along with indicator species. The primary outcome of interest was the survival status at 28 days following mechanical ventilation.

          Results

          Sixty-two mechanically ventilated critically ill patients were included in this study. Compared to the survivors, the diversity of microorganisms was significantly lower in the deceased, with a significant contribution from the gut-originated fraction of lung microorganisms. Lower concentrations of fecal SCFAs were detected in the deceased. Multivariate Cox regression analysis revealed that not only lung microbial diversity but also the abundance of Enterococcaceae from the gut were correlated with day 28 mortality.

          Conclusion

          Critically ill patients exhibited lung and gut microbial dysbiosis after mechanical ventilation, as evidenced by a significant decrease in lung microbial diversity and the proliferation of Enterococcaceae in the gut. Levels of fecal SCFAs in the deceased served as a marker of imbalance between commensal and pathogenic flora in the gut. These findings emphasize the clinical significance of microbial profiling in predicting the prognosis of ICU patients.

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          Most cited references89

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            Metagenomic biomarker discovery and explanation

            This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                wengqinyon@sina.com
                jeromesuper@126.com
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central (London )
                1471-2180
                18 December 2023
                18 December 2023
                2023
                : 23
                : 399
                Affiliations
                Department of Critical Care Medicine, Fujian Medical University Union Hospital, ( https://ror.org/055gkcy74) Fuzhou, China
                Article
                3078
                10.1186/s12866-023-03078-3
                10726596
                38110878
                850667fb-6d9f-4b63-854e-f613cc18fff6
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 August 2023
                : 20 October 2023
                Funding
                Funded by: Talent start-up funding
                Funded by: Funding for Top Hospital and Specialty Excellence of Fujian Province
                Award ID: 2022 (884)
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Microbiology & Virology
                intestinal microbiota,lung microbiota,gut-lung axis,intensive care unit,longitudinal study

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