Original brief
Evolution of intestinal microbiome in a process of faecal microbiota transplantation in a patient with Clostridioides difficile infection: NGS analysis with different bioinformatics software programs
Evolución del microbioma intestinal en un proceso de transferencia de microbiota fecal (TMF) en un paciente con infección por Clostridioides difficile: análisis por NGS con diferentes programas bioinformáticos
María Paz Venteroa,
, Noelia Espinosaa, Rodrigo Joverb, Yolanda Guillenc, Esperanza Merinod, Juan Carlos Rodrígueza,e
Corresponding author
a Servicio de Microbiología. Hospital General Universitario de Alicante. Instituto de Investigación Sanitaria y Biomédica de Alicante-ISABIAL, Alicante, Spain
b Servicio Digestivo. Hospital General Universitario de Alicante. Instituto de Investigación Sanitaria y Biomédica de Alicante-ISABIAL, Alicante, Spain
c Universidad Oberta de Catalunya, Barcelona, Spain
d Unidad de Enfermedades infecciosas. Hospital General Universitario de Alicante. Instituto de Investigación Sanitaria y Biomédica de Alicante-ISABIAL, Alicante, Spain
e Departamento de Producción Vegetal y Microbiología. Universidad Miguel Hernández de Elche (Alicante), Elche, Spain
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Hospital General Universitario de Alicante. Instituto de Investigación Sanitaria y Biomédica de Alicante-ISABIAL, Alicante, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Servicio Digestivo. Hospital General Universitario de Alicante. Instituto de Investigación Sanitaria y Biomédica de Alicante-ISABIAL, Alicante, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Universidad Oberta de Catalunya, Barcelona, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Unidad de Enfermedades infecciosas. Hospital General Universitario de Alicante. Instituto de Investigación Sanitaria y Biomédica de Alicante-ISABIAL, Alicante, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Departamento de Producción Vegetal y Microbiología. Universidad Miguel Hernández de Elche (Alicante), Elche, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Evolución del microbioma intestinal en un proceso de transferencia de microbiota fecal (TMF) en un paciente con infección por <span class="elsevierStyleItalic">Clostridioides difficile</span>: análisis por NGS con diferentes programas bioinformáticos" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2530 "Ancho" => 3185 "Tamanyo" => 512987 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0045" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">(A) α diversity analysis. Chao1 (left) and Shannon (right) indexes obtained with the Bioconductor programme. (B) β diversity analysis. Representation of the result of the calculation of the Unifrac unweighted distance, and the subsequent analysis of principal components with QIIME 2. (C) Heatmap obtained with QIIME 2. The sample data are horizontal. The information referring to the genera is vertical. (D) Differentially abundant genera using the LefSe algorithm. The image on the left corresponds to the one made with data from QIIME 2, and the one on the right to the one made with data from Bioconductor. Donor: sample obtained from the donor; PAT: sample obtained from the pre-FMT patient; P7D, P30D, P90D and P180D: samples obtained from the post-FMT patient at 7, 30, 90 and 180 days.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">The incidence of <span class="elsevierStyleItalic">Clostridioides difficile</span> infection (CDI) has increased considerably over the last twenty years and thanks not only to the incidence, but also to its associated morbidity and mortality rates, CDI has become a global health problem. CDI is the main cause of nosocomial diarrhoea of infectious origin associated with the administration of antibiotics. It has led to an increase in in-hospital mortality, and generates significant healthcare costs.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The treatment of choice for CDI is antibiotic therapy, which is effective in approximately 85% of cases. However, the incidence of recurrence is high, at around 30%, with the rate increasing to 45% after the first episode, and up to 75% in patients with multiple episodes of recurrence.<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> A number of different factors have been associated with the increased risk of recurrence, including the need to remain on broad-spectrum antibiotic therapy, old age, or having suffered a previous episode of CDI.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> As all these factors affect the patient's own microbiota, there are theories that the underlying cause of relapses is dysbiosis due to colonisation by <span class="elsevierStyleItalic">Clostridioides difficile</span> (CD). Consequently, faecal microbiota transplantation (FMT) after the antibiotic treatment has been proposed as an effective method to prevent relapses in patients with this disorder.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Various different computer-based tools are available to carry out microbiota analyses, such as Mothur,<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> QIIME 2<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> and Bioconductor.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> Studies comparing the results obtained with Mothur and QIIME 2 have found no significant differences between them.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> However, as yet there are no published studies comparing these methods with the results obtained using Bioconductor.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><p id="par0020" class="elsevierStylePara elsevierViewall">Samples: the surplus of six stool samples were analysed, one from the recipient pre-transplant, considered pathological, and five non-pathological (from the donor, and from the recipient at 7, 30, 90 and 180 days after the procedure). The recipient was a patient who had already had six relapses of CDI. All the procedures were conducted following national ethical and legal standards, and in accordance with the guidelines established in the Declaration of Helsinki (2000).</p><p id="par0025" class="elsevierStylePara elsevierViewall">FMT procedure: a faecal microbiota concentrate from a healthy donor was introduced by endoscopy. The donor was selected after screening which included a clinical questionnaire and microbiological studies of serum samples, faeces and nasal exudate. Presence of multiple pathogens was ruled out by culture and molecular techniques, as proposed in various clinical guidelines.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Study of the microbiome by mass sequencing (next-generation sequencing [NGS]): the microbiota study followed the protocol recommended by Illumina.<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> The QIIME 2<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> and Bioconductor<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> programmes were used for the bioinformatic analysis.</p><p id="par0035" class="elsevierStylePara elsevierViewall">The α diversity was studied by calculating the Shannon and Chao1 indices, and the β diversity through estimation of the <span class="elsevierStyleItalic">unweighted Unifrac</span> distance. Taxonomy was assigned using the SILVA database (Release 132).<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> Differential taxa were identified through ANCOM-Composition and Gneiss in QIIME 2, DESeq2 and EdgeR in Bioconductor, and LefSe through the Galaxy server.</p><p id="par0040" class="elsevierStylePara elsevierViewall">The comparative analysis of the results with QIIME 2 and Bioconductor was carried out by comparing the total taxa detection number (operational taxonomic units [OTU]) detected by both programmes, and the number of unidentified sequences. Student's <span class="elsevierStyleItalic">t</span> test was used for parametric variables and the Mann–Whitney <span class="elsevierStyleItalic">U</span> test for non-parametric variables.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Diversity analysis</span><p id="par0045" class="elsevierStylePara elsevierViewall">The lowest Chao1 and Shannon indices were those obtained for the pre-FMT samples, and at seven days post-FMT with both QIIME 2 and Bioconductor, so we could say these were the samples with the lowest α diversity. The sample obtained at 30 days post-FMT was the one with the greatest α diversity (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>A).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0050" class="elsevierStylePara elsevierViewall">The results for β diversity grouped the samples into two large clusters, separating the non-pathological samples (donor and post-FMT samples) from the pathological sample (pre-FMT). The samples with the most similarity were those of the donor, and of the patient at the longest times after the transplant, with these being far apart from the pre-FMT sample and the sample obtained seven days post-FMT (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>B).</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Taxonomic composition analysis</span><p id="par0055" class="elsevierStylePara elsevierViewall">The taxonomic composition analysis revealed that the non-pathological samples were more similar to each other, and different from the pre-FMT patient sample. Groups of microorganisms that characterised both pathological and non-pathological samples were observed (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>C).</p><p id="par0060" class="elsevierStylePara elsevierViewall">The genera <span class="elsevierStyleItalic">Lachnospira, Butyricimonas, Paraprevotella, Odoribacter</span> and <span class="elsevierStyleItalic">Anaerostipes</span> were described as taxa associated with the non-pathological state by the DESeq algorithm. The ANCOM and Gneiss algorithms also identified some of these genera as associated with this state. Of the rest of the taxa identified as differentially abundant among the studied samples, seven were detected by at least three algorithms: <span class="elsevierStyleItalic">Blautia, Bacteroides</span> and <span class="elsevierStyleItalic">Alistipes</span> associated with the non-pathological samples; and <span class="elsevierStyleItalic">Klebsiella, Veillonella, Megasphaera</span> and <span class="elsevierStyleItalic">Lactobacillus</span> with the pathological state (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Comparative analysis of the different methods used</span><p id="par0065" class="elsevierStylePara elsevierViewall">The results obtained in the diversity and taxonomy analysis with QIIME 2 and Bioconductor were similar. However, to check if there were differences in the OTUs, or in the taxonomic assignment, a comparison was carried out evaluating the difference between the total OTUs observed, and the number of sequences not identified by each method. The mean total OTUs observed in QIIME 2 was 140, and in Bioconductor this was 141 (<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.97), so we can state that there were no significant differences.</p><p id="par0070" class="elsevierStylePara elsevierViewall">The mean percentage of unidentified sequences for the QIIME 2 programme was 9.96%, and for Bioconductor this was 7.91%. In this case, the percentage was found to be slightly higher with the QIIME 2 programme, although a <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.197 was obtained, so it was established that there were no differences in the taxonomic assignment.</p><p id="par0075" class="elsevierStylePara elsevierViewall">Comparison of the algorithms used to identify the differentially abundant taxa revealed that the most restrictive was LefSe, which only detected one differential taxon (<span class="elsevierStyleItalic">Lactobacillus</span>) with the QIIME 2 data, and three (<span class="elsevierStyleItalic">Lactobacillus, Megasphaera</span> and <span class="elsevierStyleItalic">Klebsiella</span>) with the Bioconductor data (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>D). The least restrictive was DESeq, which identified a total of 34 genera associated with one or other state. The Gneiss algorithm was the one that detected a greater number of taxa which were also detected by other algorithms (7/11, 63.6%). In contrast, the DESeq algorithm was the one that indicated a greater number of taxa which did not coincide with other algorithms, a total of 28 out of 34 (82.3%) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Discussion</span><p id="par0080" class="elsevierStylePara elsevierViewall">The greatest risk factor in the development of CDI is the administration of antibiotics, which causes an alteration in the composition of the intestinal bacterial community, increasing the host's susceptibility to the pathogen.<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> Regardless of the analysis system, our results showed that the patient's microbiota before the microbiota transplant process was different from that of the donor, considered as a healthy microbiota, both in its diversity and in its taxonomic composition.</p><p id="par0085" class="elsevierStylePara elsevierViewall">Healthy microbiota contain trillions of bacteria, primarily <span class="elsevierStyleItalic">Bacteroidetes, Firmicutes, Actinobacteria</span> and <span class="elsevierStyleItalic">Proteobacteria</span>,<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> and act as a natural barrier that prevents CDI. Changes in the bacterial composition of the microbiota after FMT include the appearance of different genera, such as <span class="elsevierStyleItalic">Lachnospira, Butyricimonas, Paraprevotella, Odoribacter</span> and <span class="elsevierStyleItalic">Anaerostipes</span>, which are not found in the pre-transplant patient's microbiome due to the dysbiosis generated by massive colonisation of the colon by CD.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> The same changes were also found in the samples analysed in this study where, over time, these genera, which were absent in the sample taken from the patient before the FMT, gradually started to be identified.</p><p id="par0090" class="elsevierStylePara elsevierViewall">The results obtained with QIIME 2 and Bioconductor are similar, and both can therefore be used without waiting for variations in the detection of taxa associated with bioinformatic analysis. This type of study is essential to establish a common work flow in the analysis of these data, so that the results obtained are comparable regardless of where they are obtained. After using both programmes, QIIME 2 being a very powerful and complete tool, Bioconductor allowed for increased adaptability of the “<span class="elsevierStyleItalic">pipelines</span>” to different situations, as well as greater versatility, due to the existence of a multitude of packages. Therefore, although both options are equally valid, we might suggest the use of Bioconductor.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Funding</span><p id="par0095" class="elsevierStylePara elsevierViewall">This study received no specific funding from public, private or non-profit organisations.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conflicts of interest</span><p id="par0100" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:3 [ "identificador" => "xres1491880" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Material and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1354591" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1491881" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusión" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1354592" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Material and methods" ] 6 => array:3 [ "identificador" => "sec0015" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0020" "titulo" => "Diversity analysis" ] 1 => array:2 [ "identificador" => "sec0025" "titulo" => "Taxonomic composition analysis" ] 2 => array:2 [ "identificador" => "sec0030" "titulo" => "Comparative analysis of the different methods used" ] ] ] 7 => array:2 [ "identificador" => "sec0035" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0040" "titulo" => "Funding" ] 9 => array:2 [ "identificador" => "sec0045" "titulo" => "Conflicts of interest" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2020-02-26" "fechaAceptado" => "2020-05-16" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1354591" "palabras" => array:4 [ 0 => "Microbiota" 1 => "Faecal microbiota transfer" 2 => "NGS" 3 => "<span class="elsevierStyleItalic">Clostridioides difficile</span>" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1354592" "palabras" => array:4 [ 0 => "Microbiota" 1 => "Transferencia microbiota fecal" 2 => "Secuenciación masiva" 3 => "<span class="elsevierStyleItalic">Clostridioides difficile</span>" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Clostridioides difficile</span> infection (CDI) has become a global healthcare challenge due to increases in its incidence and mortality rates. Faecal microbiota transfer (FMT) is postulated as a protocol to prevent CDI recurrence.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Material and methods</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">A donor faecal sample and patient faecal samples (pre-FMT and post-FMT) were analysed. The r16S gene was amplified and sequenced by NGS, and its diversity and taxonomy composition were examined.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Microbial richness increased in post-FMT samples, and the β diversity studies grouped the samples into two clusters. One included the non-pathological samples (donor and pre-FMT samples), and the other included the pathological sample. The results obtained by Qiime2 and Bioconductor were similar.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The analysis showed an increase in taxonomic diversity after the FMT, which suggests its usefulness. Moreover, these results showed that standardisation of bioinformatics analysis is key.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Material and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducción</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">La diarrea por <span class="elsevierStyleItalic">Clostridioides difficile</span> es un importante problema de salud pública, cuyo tratamiento es complejo. La transferencia de microbiota fecal (TMF) se postula como una terapia útil para prevenir recidivas.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Material y métodos</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Se analizaron seis muestras fecales, una procedente del donante y cinco del paciente antes y después de la TMF. Se amplificó y secuenció el gen 16Sr mediante secuenciación masiva y se estudió la diversidad y composición taxonómica.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">La diversidad aumentó en las muestras post-TMF, y se identificaron dos clústeres, uno formado por las muestras no patológicas (donante y paciente post-TMF), y otro por la muestra patológica. Los resultados obtenidos a través Qiime2 y Bioconductor fueron similares.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusión</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">El análisis realizado demostró un incremento en la diversidad taxonómica del paciente tras la TMF, sugiriendo su utilidad. Además, los resultados obtenidos con Qiime2 y Bioconductor reflejaron la importancia de unificar los análisis bioinformáticos.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusión" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "⋆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as: Ventero MP, Espinosa N, Jover R, Guillen Y, Merino E, Rodríguez JC. Evolución del microbioma intestinal en un proceso de transferencia de microbiota fecal (TMF) en un paciente con infección por <span class="elsevierStyleItalic">Clostridioides difficile</span>: análisis por NGS con diferentes programas bioinformáticos. Enferm Infecc Microbiol Clin. 2021;39:184–187.</p>" ] ] "multimedia" => array:2 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2530 "Ancho" => 3185 "Tamanyo" => 512987 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0045" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">(A) α diversity analysis. Chao1 (left) and Shannon (right) indexes obtained with the Bioconductor programme. (B) β diversity analysis. Representation of the result of the calculation of the Unifrac unweighted distance, and the subsequent analysis of principal components with QIIME 2. (C) Heatmap obtained with QIIME 2. The sample data are horizontal. The information referring to the genera is vertical. (D) Differentially abundant genera using the LefSe algorithm. The image on the left corresponds to the one made with data from QIIME 2, and the one on the right to the one made with data from Bioconductor. Donor: sample obtained from the donor; PAT: sample obtained from the pre-FMT patient; P7D, P30D, P90D and P180D: samples obtained from the post-FMT patient at 7, 30, 90 and 180 days.</p>" ] ] 1 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0050" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Taxa \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">ANCOM \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Gneiss \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">DESeq \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">EdgeR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LEfSe \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Blautia</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Bacteroides</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">x \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 October | 3 | 1 | 4 |
| 2024 September | 28 | 10 | 38 |
| 2024 August | 20 | 10 | 30 |
| 2024 July | 10 | 4 | 14 |
| 2024 June | 14 | 5 | 19 |
| 2024 May | 20 | 7 | 27 |
| 2024 April | 22 | 4 | 26 |
| 2024 March | 11 | 7 | 18 |
| 2024 February | 24 | 6 | 30 |
| 2024 January | 24 | 6 | 30 |
| 2023 December | 21 | 5 | 26 |
| 2023 November | 12 | 6 | 18 |
| 2023 October | 13 | 8 | 21 |
| 2023 September | 9 | 3 | 12 |
| 2023 August | 9 | 2 | 11 |
| 2023 July | 11 | 3 | 14 |
| 2023 June | 11 | 3 | 14 |
| 2023 May | 14 | 4 | 18 |
| 2023 April | 11 | 1 | 12 |
| 2023 March | 12 | 3 | 15 |
| 2023 February | 12 | 1 | 13 |
| 2023 January | 17 | 2 | 19 |
| 2022 December | 13 | 7 | 20 |
| 2022 November | 9 | 5 | 14 |
| 2022 October | 14 | 3 | 17 |
| 2022 September | 17 | 25 | 42 |
| 2022 August | 26 | 7 | 33 |
| 2022 July | 7 | 7 | 14 |
| 2022 June | 8 | 3 | 11 |
| 2022 May | 11 | 4 | 15 |
| 2022 April | 14 | 9 | 23 |
| 2022 March | 10 | 10 | 20 |
| 2022 February | 8 | 4 | 12 |
| 2022 January | 20 | 6 | 26 |
| 2021 December | 9 | 8 | 17 |
| 2021 November | 8 | 6 | 14 |
| 2021 October | 10 | 3 | 13 |
| 2021 September | 2 | 0 | 2 |
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