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etween the colorectal polyp and the control group. CP_S: salivary samples of colorectal polyp patients; HC_S: salivary samples of healthy controls; CP_F: fecal samples of colorectal polyp patients; HC_F: fecal samples of healthy controls."},{"height":677,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g005.jpg","width":1902,"caption":"(A) Unweighted and (B) weighted UniFrac-based PCoA of salivary microbiota shows significant distinction in β-diversity between the CP and HC groups. Each point in the plot represents a sample, and different colored points indicate different groups. The percentages in parentheses on the axes represent the fraction of the sample variance data (distance matrix) that can be explained by the corresponding axes. A 95% confidence ellipse is drawn. CP_S: salivary samples of colorectal polyp patients; HC_S: salivary samples of healthy controls."},{"height":677,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g006.jpg","width":1902,"caption":"(A) Unweighted and (B) weighted UniFrac-based PCoA of fecal microbiota shows significant distinction in β-diversity between the CP and HC groups. Each point in the plot represents a sample, and different colored points indicate different groups. The percentages in parentheses on the axes represent the fraction of the sample variance data (distance matrix) that can be explained by the corresponding axes. A 95% confidence ellipse is drawn. CP_F: fecal samples of colorectal polyp patients; HC_F: fecal samples of healthy controls."},{"height":1165,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g007.jpg","width":1902,"caption":"Correlation heatplots of (A) the top 20 taxa at genus level and (B) the top 20 taxa at species level detected in salivary and fecal samples from patients with colorectal polyps and healthy controls. CP_S: salivary samples of colorectal polyp patients; CP_F: fecal samples of colorectal polyp patients; HC_S: salivary samples of healthy controls; HC_F: fecal samples of healthy controls."},{"height":779,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g008.jpg","width":1902,"caption":"LEfSe biomarker analysis of salivary samples from patients with colorectal polyps shows taxonomic rank relationships of the main taxa from phylum to species (inner ring to outer ring). The taxonomic cladogram shows the taxonomic hierarchical relationships of the main taxa from phylum to genus, from inner to outer circles, in the sample community. Node size corresponds to the average relative abundance of that taxon; Hollow nodes represent taxa that do not differ significantly between groups, while nodes in other colors (e.g., blue and red) indicate that these taxa exhibit significant group differences and are more abundant in the group samples represented by that color. Letters identify the names of taxa that differ significantly between groups. CP_S: salivary samples of colorectal polyp patients; HC_S: salivary samples of healthy controls."},{"height":1019,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g009.jpg","width":1902,"caption":"LEfSe biomarker analysis of fecal samples from patients with colorectal polyps shows taxonomic rank relationships of the main taxa from phylum to species (inner ring to outer ring). The taxonomic cladogram shows the taxonomic hierarchical relationships of the main taxa from phylum to genus, from inner to outer circles, in the sample community. Node size corresponds to the average relative abundance of that taxon; Hollow nodes represent taxa that do not differ significantly between groups, while nodes in other colors (e.g., blue and red) indicate that these taxa exhibit significant group differences and are more abundant in the group samples represented by that color. Letters identify the names of taxa that differ significantly between groups. CP_F: fecal samples of colorectal polyp patients; HC_F: fecal samples of healthy controls."},{"height":849,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g010.jpg","width":851,"caption":"The ROC curve demonstrates the efficacy of using salivary and fecal microbiota as potential tool for detecting colorectal polyps, showing that the combination of both samples microbiota may result in more accurate detection. (AUC: area under the curve; FPR: false-positive rate; TPR: true-positive rate)."},{"height":1591,"url":"https://www.frontiersin.org/files/Articles/1182346/fmicb-14-1182346-HTML/image_m/fmicb-14-1182346-g011.jpg","width":1902,"caption":"Correlation plot of the top 30 taxa at species level in patients with colorectal polyps using Spearman’s correlation. Correlations with an adjusted p-value less than 0.05 are displayed with a dot inside the circle. Blue indicates a negative correlation, red indicates a positive correlation, with darker colors showing stronger correlations."}],"journal":{"guid":310,"name":"Frontiers in Microbiology","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fmicb.2023.1182346","pubDate":"2023-08-16","score":20.42527712598109,"title":"Salivary and fecal microbiota: potential new biomarkers for early screening of colorectal polyps","topics":["biomarker","Colorectal polyp","fecal microbiota","salivary microbiota","Full-length 16S rRNA sequencing"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fmicb.2023.1182346/pdf"},{"__typename":"Feed_Article","_id":"6859a5dbfd1016fa1b12e713","abstract":"","htmlAbstract":null,"authors":[{"fullName":"Paul Tetteh 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Pamer","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Duchossois Family Institute, Division of Infectious Diseases and Global Health, University of Chicago","address":null},"affiliations":[{"name":"Duchossois Family Institute, Division of Infectious Diseases and Global Health, University of Chicago","address":null}],"nessieId":null},{"fullName":"Adrian Egli","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Applied Microbiology Research, Department of Biomedicine, University of Basel","address":null},"affiliations":[{"name":"Applied Microbiology Research, Department of Biomedicine, University of Basel","address":null},{"name":"Clinical Bacteriology and Mycology, University Hospital of Basel","address":null}],"nessieId":null},{"fullName":"Lisa Maier","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2188041/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2188041/overview","affiliation":{"name":"Interfaculty Institute of Microbiology and Infection Medicine Tübingen, University of Tübingen","address":null},"affiliations":[{"name":"Interfaculty Institute of Microbiology and Infection Medicine Tübingen, University of Tübingen","address":null},{"name":"Cluster of Excellence ‘Controlling Microbes to Fight Infections', University of Tübingen","address":null}],"nessieId":null},{"fullName":"Pascale Vonaesch","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1702196/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1702196/overview","affiliation":{"name":"Department of Fundamental Microbiology, University of Lausanne","address":null},"affiliations":[{"name":"Department of Fundamental Microbiology, University of Lausanne","address":null},{"name":"Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute","address":null},{"name":"University of Basel","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2023-04-19","recentDate":"2023-05-22"},"doi":"10.3389/fmicb.2023.1208177","frontiersExtra":{"articleType":"Correction","impact":{"citations":4,"crossrefCitations":0,"downloads":574,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":2104},"isPartOfResearchTopic":true,"isPublished":true,"section":"Microorganisms in Vertebrate Digestive Systems"},"guid":1208177,"images":null,"journal":{"guid":310,"name":"Frontiers in Microbiology","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fmicb.2023.1208177","pubDate":"2023-05-22","score":7.908083178250676,"title":"Corrigendum: A MALDI-TOF MS library for rapid identification of human commensal gut bacteria from the class Clostridia","topics":["anaerobic","Clostridia","commensal bacteria","MALDI-TOF MS","culturomics","human gut microbiota","Bacterial identification","Next-generation probiotics"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fmicb.2023.1208177/pdf"},{"__typename":"Feed_Article","_id":"6859a5dbfd1016fa1b12e712","abstract":"This study aimed to evaluate the difference in gut microbiomes between preterm and term infants using third-generation long-read sequencing (Oxford Nanopore Technologies, ONT) compared with an established gold standard, Illumina (second-generation short-read sequencing). A total of 69 fecal samples from 51 term (T) and preterm (P) infants were collected at 7 and 28 days of life. Gut colonization profiling was performed by 16S rRNA gene sequencing using ONT. We used Illumina to validate and compare the patterns in 13 neonates. Using bioinformatic analysis, we identified features that differed between P and T. Both T1 and P1 microbiomes were dominated by Firmicutes (Staphylococcus and Enterococcus), whereas sequentially showed dominant transitions to Lactobacillus (p \u003c 0.001) and Streptococcus in T2 (p = 0.001), and pathogenic bacteria (Klebsiella) in P2 (p = 0.001). The abundance of beneficial bacteria (Bifidobacterium and Lactobacillus) increased in T2 (p = 0.026 and p \u003c 0.001, respectively). These assignments were correlated with the abundance at the species-level. Bacterial α-diversity increased in T (p = 0.005) but not in P (p = 0.156), and P2 showed distinct β-diversity clustering than T2 (p = 0.001). The ONT reliably identified pathogenic bacteria at the genus level, and taxonomic profiles were comparable to those identified by Illumina at the genus level. This study shows that ONT and Illumina are highly correlated. P and T had different microbiome profiles, and the α- and β-diversity varied. ONT sequencing has potential for pathogen detection in neonates in clinical settings.","htmlAbstract":"\u003cp\u003eThis study aimed to evaluate the difference in gut microbiomes between preterm and term infants using third-generation long-read sequencing (Oxford Nanopore Technologies, ONT) compared with an established gold standard, Illumina (second-generation short-read sequencing). A total of 69 fecal samples from 51 term (T) and preterm (P) infants were collected at 7 and 28 days of life. Gut colonization profiling was performed by 16S rRNA gene sequencing using ONT. We used Illumina to validate and compare the patterns in 13 neonates. Using bioinformatic analysis, we identified features that differed between P and T. Both T1 and P1 microbiomes were dominated by \u003ci\u003eFirmicutes\u003c/i\u003e (\u003ci\u003eStaphylococcus\u003c/i\u003e and \u003ci\u003eEnterococcus\u003c/i\u003e), whereas sequentially showed dominant transitions to \u003ci\u003eLactobacillus\u003c/i\u003e (\u003ci\u003ep\u003c/i\u003e \u0026lt; 0.001) and \u003ci\u003eStreptococcus\u003c/i\u003e in T2 (\u003ci\u003ep\u003c/i\u003e = 0.001), and pathogenic bacteria (\u003ci\u003eKlebsiella\u003c/i\u003e) in P2 (\u003ci\u003ep\u003c/i\u003e = 0.001). The abundance of beneficial bacteria (\u003ci\u003eBifidobacterium\u003c/i\u003e and \u003ci\u003eLactobacillus\u003c/i\u003e) increased in T2 (\u003ci\u003ep\u003c/i\u003e = 0.026 and \u003ci\u003ep\u003c/i\u003e \u0026lt; 0.001, respectively). These assignments were correlated with the abundance at the species-level. Bacterial α-diversity increased in T (\u003ci\u003ep\u003c/i\u003e = 0.005) but not in P (\u003ci\u003ep\u003c/i\u003e = 0.156), and P2 showed distinct β-diversity clustering than T2 (\u003ci\u003ep\u003c/i\u003e = 0.001). The ONT reliably identified pathogenic bacteria at the genus level, and taxonomic profiles were comparable to those identified by Illumina at the genus level. This study shows that ONT and Illumina are highly correlated. P and T had different microbiome profiles, and the α- and β-diversity varied. ONT sequencing has potential for pathogen detection in neonates in clinical settings.\u003c/p\u003e","authors":[{"fullName":"Teahyen Cha","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2178166/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2178166/overview","affiliation":{"name":"Department of Pediatrics, Hanyang University College of Medicine","address":null},"affiliations":[{"name":"Department of Pediatrics, Hanyang University College of Medicine","address":null}],"nessieId":null},{"fullName":"Hoo Hugo Kim","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2290722/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2290722/overview","affiliation":{"name":"Department of Earth Resources and Environmental Engineering, Hanyang University","address":null},"affiliations":[{"name":"Department of Earth Resources and Environmental Engineering, Hanyang University","address":null}],"nessieId":null},{"fullName":"Jihyun Keum","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2285699/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2285699/overview","affiliation":{"name":"Department of Obstetrics and Gynecology, Hanyang University College of Medicine","address":null},"affiliations":[{"name":"Department of Obstetrics and Gynecology, Hanyang University College of Medicine","address":null}],"nessieId":null},{"fullName":"Min-Jin Kwak","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/610929/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/610929/overview","affiliation":{"name":"Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University","address":null},"affiliations":[{"name":"Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University","address":null}],"nessieId":null},{"fullName":"Jae Yong Park","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Division of Microbiome, Int-Gen Company","address":null},"affiliations":[{"name":"Division of Microbiome, Int-Gen Company","address":null}],"nessieId":null},{"fullName":"Jeong Kyu Hoh","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2233850/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2233850/overview","affiliation":{"name":"Department of Obstetrics and Gynecology, Hanyang University College of Medicine","address":null},"affiliations":[{"name":"Department of Obstetrics and Gynecology, Hanyang University College of Medicine","address":null}],"nessieId":null},{"fullName":"Chang-Ryul Kim","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Department of Pediatrics, Hanyang University College of Medicine","address":null},"affiliations":[{"name":"Department of Pediatrics, Hanyang University College of Medicine","address":null}],"nessieId":null},{"fullName":"Byong-Hun Jeon","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/823709/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/823709/overview","affiliation":{"name":"Department of Earth Resources and Environmental Engineering, Hanyang University","address":null},"affiliations":[{"name":"Department of Earth Resources and Environmental Engineering, Hanyang University","address":null}],"nessieId":"420907446788"},{"fullName":"Hyun-Kyung Park","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1387049/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1387049/overview","affiliation":{"name":"Department of Pediatrics, Hanyang University College of Medicine","address":null},"affiliations":[{"name":"Department of Pediatrics, Hanyang University College of Medicine","address":null}],"nessieId":"274878558566"}],"dates":{"acceptedDate":"2023-04-17","recentDate":"2023-05-15"},"doi":"10.3389/fmicb.2023.1148466","frontiersExtra":{"articleType":"Original Research","impact":{"citations":15,"crossrefCitations":0,"downloads":5,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":11078},"isPartOfResearchTopic":true,"isPublished":true,"section":"Microorganisms in Vertebrate Digestive Systems"},"guid":1148466,"images":[{"height":286,"url":"https://www.frontiersin.org/files/myhome article library/1148466/1148466_Thumb_400.jpg","width":400,"caption":null},{"height":1305,"url":"https://www.frontiersin.org/files/Articles/1148466/fmicb-14-1148466-HTML/image_m/fmicb-14-1148466-g001.jpg","width":1831,"caption":"Sampling protocol and analysis method for participants. The subjects were grouped according to gestational age (37 weeks). Fecal samples were collected at 7 and 28 days after birth. ONT was performed on 38 fecal samples obtained from 34 infants. To compare the sequencing results, 15 overlapping samples from 13 infants were analyzed using Illumina. T, Term infants; P, Preterm infants; 7D, day 7 of life after birth; 28D, day 28 of life; DNA, Deoxyribonucleic Acid; rRNA, Ribosomal ribonucleic acid; ONT, Oxford Nanopore Technologies."},{"height":1289,"url":"https://www.frontiersin.org/files/Articles/1148466/fmicb-14-1148466-HTML/image_m/fmicb-14-1148466-g002.jpg","width":1901,"caption":"Relative abundance of the most dominant bacterial communities, analyzed by ONT. (A) Relative abundance at the phylum level. (B) Relative abundance at the genus level. (C) The specific bacterial population at the genus level (Bifidobacterium, Lactobacillus, Streptococcus, Klebsiella) (D) Relative abundance at the species-level. (E) The specific bacterial population at the species-level (Enterococcus faecalis, Staphylococcus epidermidis, Enterococcus faecium, Staphylococcus haemolyticus, Bifidobacterium longum, Lactobacillus gasseri, Streptococcus salivarius, Klebsiella pneumoniae). Treatment groups: T1, term group in period 1; T2, term group in period 2; P1, preterm group in period 1; P2, preterm group in period 2. *, ** indicate significant differences between groups (p \u003c 0.05, and p \u003c 0.01, respectively). a,b Mean values within a row having different superscript letters are significantly different (p \u003c 0.05)."},{"height":1466,"url":"https://www.frontiersin.org/files/Articles/1148466/fmicb-14-1148466-HTML/image_m/fmicb-14-1148466-g003.jpg","width":1654,"caption":"Difference in α- and β - diversity of gut microbiome between T and P. (A) Species evenness index (Pielou’s evenness index) and species richness index (Faith’s phylogenetic diversity). (B) Principal coordinate analysis (PCoA) ordination plots of microbial communities in T1, T2, P1, and P2 based on weighted UniFrac distance. p-value shows significant differences between groups. q-value shows FDR, adjusted p-value."},{"height":436,"url":"https://www.frontiersin.org/files/Articles/1148466/fmicb-14-1148466-HTML/image_m/fmicb-14-1148466-g004.jpg","width":1901,"caption":"Difference in relative abundance of the most dominant bacterial communities between VL and LP, as analyzed by ONT. (A) Relative abundance at the phylum level. (B) Relative abundance at the genus level. (C) Relative abundance at the species-level. Treatment groups: T, term group; VP, very preterm infants; LP, moderate to late preterm infants. *, ** indicate significant differences between groups (p \u003c 0.05, and p \u003c 0.01, respectively)."},{"height":823,"url":"https://www.frontiersin.org/files/Articles/1148466/fmicb-14-1148466-HTML/image_m/fmicb-14-1148466-g005.jpg","width":1901,"caption":"Correlation analysis between ONT and Illumina sequencing methods in phylum and genus level at D7 or D28. (A) Correlation plot of gut microbial abundance in phylum level of ONT (x-axis) and Illumina (y-axis) data at 7D or 28D (Pearson’s r2 = 0.905, Total; Pearson’s r2 = 0.961, D7; Pearson’s r2 = 0.819, 28D). The region on either side of the dotted line represents the 95% CIs. (B) Correlation plot of gut microbial abundance in genus level of ONT (x-axis) and Illumina (y-axis) data at 7D or 28D (Pearson’s r2 = 0.926, Total; Pearson’s r2 = 0.925, 7D; Pearson’s r2 = 0.904, 28D). The region on either side of the dotted line represents the 95% CIs. (C) Taxonomic profiles of gut microbiome abundance at 7D or 28D in phylum level. The top row corresponds to the results from ONT, and the bottom row shows the results from Illumina. (D) Taxonomic profiles of gut microbiome abundance at 7D or 28D in genus level. The top row corresponds to the results from ONT, and the bottom row shows the results from Illumina. 7D, day 7 of life after birth; 28D, day 28 of life; ONT, Oxford Nanopore Technologies."}],"journal":{"guid":310,"name":"Frontiers in Microbiology","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fmicb.2023.1148466","pubDate":"2023-05-15","score":35.58815438142249,"title":"Gut microbiome profiling of neonates using Nanopore MinION and Illumina MiSeq sequencing","topics":["gut microbiome","neonates","preterm infants","Nanopore MinION","Illumina MiSeq®"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fmicb.2023.1148466/pdf"},{"__typename":"Feed_Article","_id":"6859a5dafd1016fa1b12e711","abstract":"IntroductionMicrobial isolates from culture can be identified using 16S or whole-genome sequencing which generates substantial costs and requires time and expertise. Protein fingerprinting via Matrix-assisted Laser Desorption Ionization–time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid bacterial identification in routine diagnostics but shows a poor performance and resolution on commensal bacteria due to currently limited database entries. The aim of this study was to develop a MALDI-TOF MS plugin database (CLOSTRI-TOF) allowing for rapid identification of non-pathogenic human commensal gastrointestinal bacteria.\u003c/sec\u003eMethodsWe constructed a database containing mass spectral profiles (MSP) from 142 bacterial strains representing 47 species and 21 genera within the class Clostridia. Each strain-specific MSP was constructed using \u003e20 raw spectra measured on a microflex Biotyper system (Bruker-Daltonics) from two independent cultures.\u003c/sec\u003eResultsFor validation, we used 58 sequence-confirmed strains and the CLOSTRI-TOF database successfully identified 98 and 93% of the strains, respectively, in two independent laboratories. Next, we applied the database to 326 isolates from stool of healthy Swiss volunteers and identified 264 (82%) of all isolates (compared to 170 (52.1%) with the Bruker-Daltonics library alone), thus classifying 60% of the formerly unknown isolates.\u003c/sec\u003eDiscussionWe describe a new open-source MSP database for fast and accurate identification of the Clostridia class from the human gut microbiota. CLOSTRI-TOF expands the number of species which can be rapidly identified by MALDI-TOF MS.\u003c/sec\u003e","htmlAbstract":"\u003cp\u003e\u003cb\u003eIntroduction:\u003c/b\u003e Microbial isolates from culture can be identified using 16S or whole-genome sequencing which generates substantial costs and requires time and expertise. Protein fingerprinting \u003ci\u003evia\u003c/i\u003e Matrix-assisted Laser Desorption Ionization–time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid bacterial identification in routine diagnostics but shows a poor performance and resolution on commensal bacteria due to currently limited database entries. The aim of this study was to develop a MALDI-TOF MS plugin database (CLOSTRI-TOF) allowing for rapid identification of non-pathogenic human commensal gastrointestinal bacteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e We constructed a database containing mass spectral profiles (MSP) from 142 bacterial strains representing 47 species and 21 genera within the class \u003ci\u003eClostridia\u003c/i\u003e. Each strain-specific MSP was constructed using \u0026gt;20 raw spectra measured on a microflex Biotyper system (Bruker-Daltonics) from two independent cultures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e For validation, we used 58 sequence-confirmed strains and the CLOSTRI-TOF database successfully identified 98 and 93% of the strains, respectively, in two independent laboratories. Next, we applied the database to 326 isolates from stool of healthy Swiss volunteers and identified 264 (82%) of all isolates (compared to 170 (52.1%) with the Bruker-Daltonics library alone), thus classifying 60% of the formerly unknown isolates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscussion:\u003c/b\u003e We describe a new open-source MSP database for fast and accurate identification of the \u003ci\u003eClostridia\u003c/i\u003e class from the human gut microbiota. CLOSTRI-TOF expands the number of species which can be rapidly identified by MALDI-TOF MS.\u003c/p\u003e","authors":[{"fullName":"Paul Tetteh Asare","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/549048/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/549048/overview","affiliation":{"name":"Department of Fundamental Microbiology, University of Lausanne","address":null},"affiliations":[{"name":"Department of Fundamental Microbiology, University of Lausanne","address":null},{"name":"Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute","address":null},{"name":"University of Basel","address":null},{"name":"Applied Microbiology Research, Department of Biomedicine, University of Basel","address":null}],"nessieId":"120259714831"},{"fullName":"Chi-Hsien 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The plausible role of fungi in systemic lupus erythematosus (SLE) is far from clear and need to be explored.\u003c/sec\u003eMethodsA total of 64 subjects were recruited, including SLE, rheumatoid arthritis (RA), undifferentiated connective tissue diseases (UCTDs) patients and healthy controls (HCs). Fecal samples of subjects were collected. Gut fungi and bacteria were detected by ITS sequencing and 16S rRNA gene sequencing, respectively. Alpha and beta diversities of microbiota were analyzed. Linear discriminant analysis effect size analysis was performed to identify abundance of microbiota in different groups. The correlation network between bacterial and fungal microbiota was analyzed based on Spearman correlation.\u003c/sec\u003eResultsGut fungal diversity and community composition exhibited significant shifts in SLE compared with UCTDs, RA and HCs. Compared with HCs, the alpha and beta diversities of fungal microbiota decreased in SLE patients. According to principal coordinates analysis results, the constitution of fungal microbiota from SLE, RA, UCTDs patients and HCs exhibited distinct differences with a clear separation between fungal microbiota. There was dysbiosis in the compositions of fungal and bacterial microbiota in the SLE patients, compared to HCs. Pezizales, Cantharellales and Pseudaleuria were enriched in SLE compared with HCs, RA and UCTDs. There was a complex relationship network between bacterial and fungal microbiota, especially Candida which was related to a variety of bacteria.\u003c/sec\u003eConclusionThis study presents a pilot analysis of fungal microbiota with diversity and composition in SLE, and identifies several gut fungi with different abundance patterns taxa among SLE, RA, UCTDs and HCs. Furthermore, the gut bacterial-fungal association network in SLE patients was altered compared with HCs.\u003c/sec\u003e","htmlAbstract":"\u003cp\u003e\u003cb\u003eObjective:\u003c/b\u003e Gut fungi, as symbiosis with the human gastrointestinal tract, may regulate physiology \u003ci\u003evia\u003c/i\u003e multiple interactions with host cells. The plausible role of fungi in systemic lupus erythematosus (SLE) is far from clear and need to be explored.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e A total of 64 subjects were recruited, including SLE, rheumatoid arthritis (RA), undifferentiated connective tissue diseases (UCTDs) patients and healthy controls (HCs). Fecal samples of subjects were collected. Gut fungi and bacteria were detected by ITS sequencing and 16S rRNA gene sequencing, respectively. Alpha and beta diversities of microbiota were analyzed. Linear discriminant analysis effect size analysis was performed to identify abundance of microbiota in different groups. The correlation network between bacterial and fungal microbiota was analyzed based on Spearman correlation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e Gut fungal diversity and community composition exhibited significant shifts in SLE compared with UCTDs, RA and HCs. Compared with HCs, the alpha and beta diversities of fungal microbiota decreased in SLE patients. According to principal coordinates analysis results, the constitution of fungal microbiota from SLE, RA, UCTDs patients and HCs exhibited distinct differences with a clear separation between fungal microbiota. There was dysbiosis in the compositions of fungal and bacterial microbiota in the SLE patients, compared to HCs. Pezizales, Cantharellales and \u003ci\u003ePseudaleuria\u003c/i\u003e were enriched in SLE compared with HCs, RA and UCTDs. There was a complex relationship network between bacterial and fungal microbiota, especially \u003ci\u003eCandida\u003c/i\u003e which was related to a variety of bacteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion:\u003c/b\u003e This study presents a pilot analysis of fungal microbiota with diversity and composition in SLE, and identifies several gut fungi with different abundance patterns taxa among SLE, RA, UCTDs and HCs. Furthermore, the gut bacterial-fungal association network in SLE patients was altered compared with HCs.\u003c/p\u003e","authors":[{"fullName":"Bao-Zhu Li","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1187025/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1187025/overview","affiliation":{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University","address":null},"affiliations":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University","address":null},{"name":"Inflammatory and Immune Diseases Laboratory of Anhui Province","address":null}],"nessieId":"648973"},{"fullName":"Hua Wang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical 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