Citizen-science based study of the oral microbiome in Cystic fibrosis and matched controls reveals major differences in diversity and abundance of bacterial and fungal species.
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ABSTRACT:
SUBMITTER: Willis JR
PROVIDER: S-EPMC8143623 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe prevalence of fungal disease in cystic fibrosis (CF) and non-CF bronchiectasis is increasing and the clinical spectrum is widening. Poor sensitivity and a lack of standard diagnostic criteria renders interpretation of culture results challenging. In order to develop effective management strategies, a more accurate and comprehensive understanding of the airways fungal microbiome is required. The study aimed to use DNA sequences from sputum to assess the load and diversity of fungi in adults with CF and non-CF bronchiectasis.MethodsNext generation sequencing of the ITS2 region was used to examine fungal community composition (n = 176) by disease and underlying clinical subgroups including allergic bronchopulmonary aspergillosis, chronic necrotizing pulmonary aspergillosis, non-tuberculous mycobacteria, and fungal bronchitis. Patients with no known active fungal disease were included as disease controls.ResultsITS2 sequencing greatly increased the detection of fungi from sputum. In patients with CF fungal diversity was lower, while burden was higher than those with non-CF bronchiectasis. The most common operational taxonomic unit (OTU) in patients with CF was Candida parapsilosis (20.4%), whereas in non-CF bronchiectasis sputum Candida albicans (21.8%) was most common. CF patients with overt fungal bronchitis were dominated by Aspergillus spp., Exophiala spp., Candida parapsilosis or Scedosporium spp.ConclusionThis study provides a framework to more accurately characterize the extended spectrum of fungal airways diseases in adult suppurative lung diseases.
| S-EPMC8048771 | biostudies-literature
Project description:Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.
| S-EPMC5954204 | biostudies-literature
Project description:Pelagic seabirds are elusive species which are difficult to observe, thus determining their spatial distribution during the migration period is a difficult task. Here we undertook the first long-term study on the distribution of migrating shearwaters from data gathered within the framework of citizen science projects. Specifically, we collected daily abundance (only abundance given presence) of Balearic shearwaters from 2005 to 2017 from the online databases Trektellen and eBird. We applied machine-learning techniques, specifically Random Forest regression models, to predict shearwater abundance during migration using 15 environmental predictors. We built separated models for pre-breeding and post-breeding migration. When evaluated for the total data sample, the models explained more than 52% of the variation in shearwater abundance. The models also showed good ability to predict shearwater distributions for both migration periods (correlation between observed and predicted abundance was about 70%). However, relative variable importance and variation among the models built with different training data subsamples differed between migration periods. Our results showed that data gathered in citizen science initiatives together with recently available high-resolution satellite imagery, can be successfully applied to describe the migratory spatio-temporal patterns of seabird species accurately. We show that a predictive modelling approach may offer a powerful and cost-effective tool for the long-term monitoring of the migratory patterns in sensitive marine species, as well as to identify at sea areas relevant for their protection. Modelling approaches can also be essential tools to detect the impacts of climate and other global changes in this and other species within the range of the training data.
| S-EPMC7428152 | biostudies-literature
Project description:Human-associated microbial communities include fungi, but we understand little about which fungal species are present, their relative and absolute abundances, and how antimicrobial therapy impacts fungal communities. The disease cystic fibrosis (CF) often involves chronic airway colonization by bacteria and fungi, and these infections cause irreversible lung damage. Fungi are detected more frequently in CF sputum samples upon initiation of antimicrobial therapy, and several studies have implicated the detection of fungi in sputum with worse outcomes. Thus, a more complete understanding of fungi in CF is required.We characterized the fungi and bacteria in expectorated sputa from six CF subjects. Samples were collected upon admission for systemic antibacterial therapy and upon the completion of treatment and analyzed using a pyrosequencing-based analysis of fungal internal transcribed spacer 1 (ITS1) and bacterial 16S rDNA sequences. A mixture of Candida species and Malassezia dominated the mycobiome in all samples (74%-99% of fungal reads). There was not a striking trend correlating fungal and bacterial richness, and richness showed a decline after antibiotic therapy particularly for the bacteria. The fungal communities within a sputum sample resembled other samples from that subject despite the aggressive antibacterial therapy. Quantitative PCR analysis of fungal 18S rDNA sequences to assess fungal burden showed variation in fungal density in sputum before and after antibacterial therapy but no consistent directional trend. Analysis of Candida ITS1 sequences amplified from sputum or pure culture-derived genomic DNA from individual Candida species found little (<0.5%) or no variation in ITS1 sequences within or between strains, thereby validating this locus for the purpose of Candida species identification. We also report the enhancement of the publically available Visualization and Analysis of Microbial Population Structures (VAMPS) tool for the analysis of fungal communities in clinical samples.Fungi are present in CF respiratory sputum. In CF, the use of intravenous antibiotic therapy often does not profoundly impact bacterial community structure, and we observed a similar stability in fungal species composition. Further studies are required to predict the effects of antibacterials on fungal burden in CF and fungal community stability in non-CF populations.
| S-EPMC4236224 | biostudies-literature
Project description:The history of fungal diversity of the Northeastern United States is currently fragmentary and restricted to particular functional groups or limited geospatial scales. Here, we describe a unique by its size, lifespan and data originators dataset, to improve our understanding of species occurrence and distribution across the state and time. Between the years 2007 to 2019, over 30 parks and nature preserves were sampled during forays conducted by members of the New Jersey Mycological Association (USA), a nonprofit organization of fungi enthusiasts. The dataset contains over 400 000 occurrences of over 1400 species across the state, made up mostly of the phylum Basidiomycota (89%) and Ascomycota (11%), with most observations resolved at the species level (>99%). The database is georeferenced and openly accessible through the Global Biodiversity Information Facility (GBIF) repository. This dataset marks a productive endeavor to contribute to our knowledge of the biodiversity of fungi in the Northeastern United States leveraging citizen science to better resolve biodiversity of this critical and understudied kingdom.
| S-EPMC9811868 | biostudies-literature
Project description:BackgroundThe oral cavity comprises a rich and diverse microbiome, which plays important roles in health and disease. Previous studies have mostly focused on adult populations or in very young children, whereas the adolescent oral microbiome remains poorly studied. Here, we used a citizen science approach and 16S profiling to assess the oral microbiome of 1500 adolescents around Spain and its relationships with lifestyle, diet, hygiene, and socioeconomic and environmental parameters.ResultsOur results provide a detailed snapshot of the adolescent oral microbiome and how it varies with lifestyle and other factors. In addition to hygiene and dietary habits, we found that the composition of tap water was related to important changes in the abundance of several bacterial genera. This points to an important role of drinking water in shaping the oral microbiota, which has been so far poorly explored. Overall, the microbiome samples of our study can be clustered into two broad compositional patterns (stomatotypes), driven mostly by Neisseria and Prevotella, respectively. These patterns show striking similarities with those found in unrelated populations.ConclusionsWe hypothesize that these stomatotypes represent two possible global optimal equilibria in the oral microbiome that reflect underlying constraints of the human oral niche. As such, they should be found across a variety of geographical regions, lifestyles, and ages.
| S-EPMC6284318 | biostudies-literature
Project description:Cystic fibrosis (CF) is a genetic disease with mutational changes leading to profound dysbiosis, both pulmonary and intestinal, from a very young age. This dysbiosis plays an important role in clinical manifestations, particularly in the lungs, affected by chronic infection. The range of microbiological tools has recently been enriched by metagenomics based on next-generation sequencing (NGS). Currently applied essentially in a gene-targeted manner, metagenomics has enabled very exhaustive description of bacterial communities in the CF lung niche and, to a lesser extent, the fungi. Aided by progress in bioinformatics, this now makes it possible to envisage shotgun sequencing and opens the door to other areas of the microbial world, the virome, and the archaeome, for which almost everything remains to be described in cystic fibrosis. Paradoxically, applying NGS in microbiology has seen a rebirth of bacterial culture, but in an extended manner (culturomics), which has proved to be a perfectly complementary approach to NGS. Animal models have also proved indispensable for validating microbiome pathophysiological hypotheses. Description of pathological microbiomes and correlation with clinical status and therapeutics (antibiotic therapy, cystic fibrosis transmembrane conductance regulator (CFTR) modulators) revealed the richness of microbiome data, enabling description of predictive and follow-up biomarkers. Although monogenic, CF is a multifactorial disease, and both genotype and microbiome profiles are crucial interconnected factors in disease progression. Microbiome-genome interactions are thus important to decipher.
| S-EPMC7288443 | biostudies-literature
Project description:The microbiome of the respiratory tract, including the nasopharyngeal and oropharyngeal microbiota, is a dynamic community of microorganisms that is highly diverse. The cystic fibrosis (CF) airway microbiome refers to the polymicrobial communities present in the lower airways of CF patients. It is comprised of chronic opportunistic pathogens (such as Pseudomonas aeruginosa) and a variety of organisms derived mostly from the normal microbiota of the upper respiratory tract. The complexity of these communities has been inferred primarily from culture independent molecular profiling. As with most microbial communities it is generally assumed that most of the organisms present are not readily cultured. Our culture collection generated using more extensive cultivation approaches, reveals a more complex microbial community than that obtained by conventional CF culture methods. To directly evaluate the cultivability of the airway microbiome, we examined six samples in depth using culture-enriched molecular profiling which combines culture-based methods with the molecular profiling methods of terminal restriction fragment length polymorphisms and 16S rRNA gene sequencing. We demonstrate that combining culture-dependent and culture-independent approaches enhances the sensitivity of either approach alone. Our techniques were able to cultivate 43 of the 48 families detected by deep sequencing; the five families recovered solely by culture-independent approaches were all present at very low abundance (<0.002% total reads). 46% of the molecular signatures detected by culture from the six patients were only identified in an anaerobic environment, suggesting that a large proportion of the cultured airway community is composed of obligate anaerobes. Most significantly, using 20 growth conditions per specimen, half of which included anaerobic cultivation and extended incubation times we demonstrate that the majority of bacteria present can be cultured.
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Project description:To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models-especially N-mixtures with beta-binomial detection submodels-were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.
| S-EPMC9296480 | biostudies-literature
Project description:This multi-center study will compare multi-target DNA and quantitative FIT stool-based testing to colonoscopy in individuals with Cystic Fibrosis (CF) undergoing colon cancer screening with colonoscopy. The primary endpoint is detection of any adenomas, including advanced adenomas and colorectal cancer (CRC).
| 2726863 | ecrin-mdr-crc