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Dicker, James D. Chalmers" "autores" => array:2 [ 0 => array:2 [ "nombre" => "Alison J." "apellidos" => "Dicker" ] 1 => array:4 [ "nombre" => "James D." "apellidos" => "Chalmers" "email" => array:1 [ 0 => "jchalmers@dundee.ac.uk" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Scottish Centre for Respiratory Research, University of Dundee, Ninewells Hospital & Medical School, Dundee, United Kingdom" "identificador" => "aff0005" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Disbiosis microbiana en las bronquiectasias y la fibrosis quística" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Identification and treatment of bacterial infection is central to our management of bronchiectasis, whether caused by cystic fibrosis or by other underlying disorders.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">1</span></a> The pathophysiology of these diseases is understood to be a vicious cycle of infection, inflammation, and tissue damage which we attempt to break by eradicating or suppressing pathogenic bacteria using inhaled, oral, and intravenous antibiotics.<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">1,2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Our understanding of this pathophysiology is being changed, however, by developments in molecular microbiology and genomics. Non-culture based methods of identifying bacteria, viruses, and fungi in the lungs of CF and bronchiectasis patients is a rapidly expanding field of research. By utilising high-throughput sequencing platforms to target conserved regions of DNA, such as the bacterial 16S rRNA gene, identification of the bacteria present in sputum, bronchoalveolar lavage (BAL) or upper airway swabs is possible without the need for microbial culture.<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">3</span></a>There is potential to identify potential pathogens more sensitively, to identify non-culturable bacteria, to understand “phenotypes” of disease based on microbiota profiles, and to better understand the impact of treatment on microbes and the host response.</p><p id="par0015" class="elsevierStylePara elsevierViewall">Cystic fibrosis is perhaps the most intensively studied condition with respect to the lung microbiome. Recent data show that the upper airway microbiome starts to change in CF infants in the first year of life, with overgrowth of <span class="elsevierStyleItalic">Staphylococcus</span> even in children without antibiotic treatment.<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">4,5</span></a> As disease progresses, the proteobacteria increase in relative abundance, with the emergence of “traditional” CF pathogens such as <span class="elsevierStyleItalic">Pseudomonas spp.</span> Longitudinal studies have found that the lung microbiota is stable over time, at least in the short term, and does not vary significantly during exacerbations.<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">6</span></a> One of the largest studies in CF patients to date (<span class="elsevierStyleItalic">n</span>=269) confirmed patterns of microbiota diversity observed in other smaller-scale studies; dysbiosis (a reduction in microbiota diversity with dominance of one or a few genera) is associated with more severe disease, age, and increasing antibiotic use.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">7</span></a> A subset of genera, namely <span class="elsevierStyleItalic">Veillonella, Burkholderia, Prevotella, Streptococcus, Rothia</span>, and <span class="elsevierStyleItalic">Actinomyces</span>, appear to form a core CF microbiota, whilst genera like <span class="elsevierStyleItalic">Pseudomonas</span> and <span class="elsevierStyleItalic">Staphylococcus</span> become more dominant as disease progresses. Very few studies have extensive longitudinal follow up of patients or provide data on the impact of therapies such as inhaled antibiotics or CFTR modulators; this will need to be addressed before the utility of microbiota data in clinical settings is fully realised.</p><p id="par0020" class="elsevierStylePara elsevierViewall">The microbiota in bronchiectasis has been less well studied than in CF; small studies have shown that it is diverse and relatively stable over time, with a reduction in diversity identified at exacerbations and also associated with increasing disease severity.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">8</span></a> Like CF, there are common shared genera such as <span class="elsevierStyleItalic">Veillonella, Streptococcus</span>, and <span class="elsevierStyleItalic">Prevotella</span> with dominance of pathogens such as <span class="elsevierStyleItalic">Pseudomonas</span> or <span class="elsevierStyleItalic">Haemophilus</span> associated with increasing disease severity.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">9</span></a> Intriguingly, a randomised control trial (RCT) of erythromycin suggested that long term macrolide therapy, a common treatment strategy, may be associated with an increase in relative abundance of <span class="elsevierStyleItalic">Pseudomonas spp</span>.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">9</span></a> This is important because of the well-known association between <span class="elsevierStyleItalic">Pseudomonas aeruginosa</span> infection and poor clinical outcomes.<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">10</span></a> Dominance of <span class="elsevierStyleItalic">Pseudomonas spp</span>. by sequencing has also been associated with more exacerbations and increased levels of inflammation such as matrix metalloproteinases.<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">9,11</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">While there are increasing numbers of bronchiectasis microbiota studies, a significant percentage have arisen from a single RCT in Australia that included only patients with a history of 2 or more exacerbations in the previous year, among other exclusion criteria.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">9</span></a> It has been shown that 52%–93% of bronchiectasis patients in real-life are ineligible for RCTs.<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">12</span></a> Larger studies from diverse populations are now needed.</p><p id="par0030" class="elsevierStylePara elsevierViewall">Studies show that individuals microbiota profiles vary significantly from each other; what is normal for one patient during disease stability may not be for another. The key to being able to use microbiota data clinically will be a better understanding of how an individual patient's microbiota changes over time, and how this is associated with altered lung inflammatory profiles, clinical phenotypes and clinical outcomes. In both CF and bronchiectasis it appears that this change towards dysbiosis alongside changes in clinical and inflammatory profiles is clinically important and useful for stratifying patients. What is now needed is to determine if microbiota profiles can add to existing stratification methods, such as the Bronchiectasis Severity Index, by providing additional prediction of exacerbations or treatment response.<a class="elsevierStyleCrossRefs" href="#bib0140"><span class="elsevierStyleSup">13,14</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">A barrier to effective implantation of microbiota data into practice is a lack of agreed technical approaches. Sampling techniques and DNA extraction methods vary greatly. Different regions of the 16S rRNA gene have been sequenced, and numerous different sequence analysis pipelines exist. All of these can cause slight variation in the proportions of genera identified. Contamination of samples or laboratory reagents can produce misleading results if not identified and dealt with, particularly in low biomass samples. These technical and bioinformatic challenges need to be overcome and methods standardised if microbiota data is to become useful in clinical care.</p><p id="par0040" class="elsevierStylePara elsevierViewall">The bacterial microbiota alone will not explain all the phenotypic diversity seen in CF and bronchiectasis patients. Viruses, fungi, and mycobacteria also contribute to the lung microbiome in the healthy lung, in CF, and in bronchiectasis disease stability and exacerbation. Indeed, for bronchiectasis, no ‘virome’ or ‘mycobiota’ studies have been published to date, yet it is acknowledged that mycosis is becoming an increasing problem.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">15</span></a> In both CF and bronchiectasis, therapeutic and prophylactic use of antibiotics are key tools in the physician's arsenal, yet whilst the treatments may improve disease symptoms, the effect they have on the fungi, bacteria, and viruses inhabiting the airway is still unknown. A better understanding of how antibiotic usage in bronchiectasis and CF may contribute towards the development of a dysbiotic microbiome could lead towards better antibiotic stewardship in early disease to slow the development of dysbiosis and drug resistance.</p><p id="par0045" class="elsevierStylePara elsevierViewall">Microbial sequencing technologies are a powerful tool to understand these diseases as never before. Application of novel tools is not, however, an end in itself. The challenge for the pulmonary research community is to now apply this technology to answer clinically important questions and allow microbiome science to have a meaningful impact on patient care.</p></span>" "pdfFichero" => "main.pdf" "tienePdf" => true "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as: Dicker AJ, Chalmers JD. Disbiosis microbiana en las bronquiectasias y la fibrosis quística. 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Year/Month | Html | Total | |
---|---|---|---|
2024 November | 2 | 1 | 3 |
2024 October | 64 | 18 | 82 |
2024 September | 46 | 15 | 61 |
2024 August | 85 | 39 | 124 |
2024 July | 47 | 23 | 70 |
2024 June | 67 | 40 | 107 |
2024 May | 93 | 32 | 125 |
2024 April | 71 | 28 | 99 |
2024 March | 42 | 17 | 59 |
2024 February | 38 | 22 | 60 |
2023 March | 17 | 5 | 22 |
2023 February | 65 | 22 | 87 |
2023 January | 30 | 59 | 89 |
2022 December | 66 | 57 | 123 |
2022 November | 58 | 42 | 100 |
2022 October | 67 | 40 | 107 |
2022 September | 31 | 32 | 63 |
2022 August | 41 | 52 | 93 |
2022 July | 33 | 38 | 71 |
2022 June | 39 | 36 | 75 |
2022 May | 41 | 36 | 77 |
2022 April | 40 | 43 | 83 |
2022 March | 40 | 45 | 85 |
2022 February | 37 | 35 | 72 |
2022 January | 31 | 53 | 84 |
2021 December | 36 | 43 | 79 |
2021 November | 30 | 37 | 67 |
2021 October | 38 | 55 | 93 |
2021 September | 35 | 42 | 77 |
2021 August | 31 | 39 | 70 |
2021 July | 52 | 37 | 89 |
2021 June | 37 | 38 | 75 |
2021 May | 40 | 36 | 76 |
2021 April | 81 | 109 | 190 |
2021 March | 68 | 37 | 105 |
2021 February | 36 | 28 | 64 |
2021 January | 35 | 24 | 59 |
2020 December | 38 | 20 | 58 |
2020 November | 28 | 24 | 52 |
2020 October | 23 | 19 | 42 |
2020 September | 30 | 18 | 48 |
2020 August | 31 | 10 | 41 |
2020 July | 18 | 20 | 38 |
2020 June | 28 | 17 | 45 |
2020 May | 34 | 13 | 47 |
2020 April | 41 | 18 | 59 |
2020 March | 32 | 14 | 46 |
2020 February | 41 | 24 | 65 |
2020 January | 36 | 20 | 56 |
2019 December | 39 | 13 | 52 |
2019 November | 33 | 29 | 62 |
2019 October | 45 | 27 | 72 |
2019 September | 38 | 6 | 44 |
2019 August | 37 | 21 | 58 |
2019 July | 30 | 19 | 49 |
2019 June | 33 | 15 | 48 |
2019 May | 35 | 19 | 54 |
2019 April | 55 | 35 | 90 |
2019 March | 37 | 31 | 68 |
2019 February | 31 | 19 | 50 |
2019 January | 39 | 23 | 62 |
2018 December | 37 | 22 | 59 |
2018 November | 77 | 38 | 115 |
2018 October | 82 | 20 | 102 |
2018 September | 36 | 13 | 49 |
2018 May | 27 | 0 | 27 |
2018 April | 42 | 9 | 51 |
2018 March | 40 | 13 | 53 |
2018 February | 60 | 13 | 73 |
2018 January | 129 | 16 | 145 |
2017 December | 92 | 13 | 105 |
2017 October | 1 | 0 | 1 |
2017 August | 1 | 0 | 1 |
2017 March | 0 | 1 | 1 |