Short term dynamics of the sputum microbiome among COPD patients.

Chronic obstructive pulmonary disease (COPD) is an inflammatory disorder characterized by incompletely reversible airflow obstruction. The complexity of the lung microbial community in COPD patients has been highlighted in recent years. Evidence suggests that transplantation, medications, age, and disease severity influence microbial community membership. However, the dynamics of the lung microbiome in stable COPD patients remain poorly understood. In this study, we completed a longitudinal 16S ribosomal RNA survey of the lung microbiome on replicate sputum samples collected from 4 former smokers with COPD (Stage 2) within a 2-day time period. Samples from each individual over the two-day period were similar based on α-diversity, principle component analysis and taxonomy at the phyla and genera level. Sputum samples from COPD patients were also collected between 2-9 months of follow-up. Data suggest an increased variability of the sputum microbiota when comparing samples collected ≤ 3 months compared to those collected ≥ 4 months; however, no statistically significant shifts in the abundance (\u3e2-fold) of taxa between the two time points was observed. Bacterial composition and the number of operational taxonomic units (OTUs) remained similar over time. Results from this study suggest that the sputum microbiome is relatively stable in clinically stable COPD patients (Stage 2). This study furthers our understanding of the dynamics of the lung microbiome in COPD patients

A principal factor analysis to characterize agricultural exposures among Nebraska veterans

Agricultural workers are at an increased risk of developing chronic respiratory disorders. Accurate estimation of long-term agricultural exposures based on questionnaires has been used to improve the validity of epidemiologic investigations and subsequent evaluation of the association between agricultural exposures and chronic diseases. Our aim was to use principal factor analysis (PFA) to distill exposure data into essential variables characterizing long-term agricultural exposures. This is a crosssectional study of veterans between the ages of 40 and 80 years and who worked on a farm for ≥ 2 years. Participant characteristics were: 98.1% were white males with a mean age 65 ± 8 (SD) years and 39.8% had chronic obstructive pulmonary disease. The final model included four factors and explained 16.6% of the variance in the exposure data. Factor 1 was a heterogeneous factor; however, Factor 2 was exclusively composed of exposure to livestock such as hogs, dairy and poultry. Factor 3 included exposures from jobs on or off the farm such as wood dust, mineral dust, asbestos and spray paint. Crop exposure loaded exclusively in Factor 4 and included lifetime hours of exposure and maximum number of acres farmed in the participants’ lifetime. The factors in the final model were interpretable and consistent with farming practices

Short term dynamics of the sputum microbiome among COPD patients

Chronic obstructive pulmonary disease (COPD) is an inflammatory disorder characterized by incompletely reversible airflow obstruction. The complexity of the lung microbial community in COPD patients has been highlighted in recent years. Evidence suggests that transplantation, medications, age, and disease severity influence microbial community membership. However, the dynamics of the lung microbiome in stable COPD patients remain poorly understood. In this study, we completed a longitudinal 16S ribosomal RNA survey of the lung microbiome on replicate sputum samples collected from 4 former smokers with COPD (Stage 2) within a 2-day time period. Samples from each individual over the two-day period were similar based on α-diversity, principle component analysis and taxonomy at the phyla and genera level. Sputum samples from COPD patients were also collected between 2±9 months of follow-up. Data suggest an increased variability of the sputum microbiota when comparing samples collected ≤ 3 months compared to those collected ≥ 4 months; however, no statistically significant shifts in the abundance (\u3e2-fold) of taxa between the two time points was observed. Bacterial composition and the number of operational taxonomic units (OTUs) remained similar over time. Results from this study suggest that the sputum microbiome is relatively stable in clinically stable COPD patients (Stage 2). This study furthers our understanding of the dynamics of the lung microbiome in COPD patients

Shotgun Pyrosequencing Metagenomic Analyses of Dusts from Swine Confinement and Grain Facilities

<div><p>Inhalation of agricultural dusts causes inflammatory reactions and symptoms such as headache, fever, and malaise, which can progress to chronic airway inflammation and associated diseases, e.g. asthma, chronic bronchitis, chronic obstructive pulmonary disease, and hypersensitivity pneumonitis. Although in many agricultural environments feed particles are the major constituent of these dusts, the inflammatory responses that they provoke are likely attributable to particle-associated bacteria, archaebacteria, fungi, and viruses. In this study, we performed shotgun pyrosequencing metagenomic analyses of DNA from dusts from swine confinement facilities or grain elevators, with comparisons to dusts from pet-free households. DNA sequence alignment showed that 19% or 62% of shotgun pyrosequencing metagenomic DNA sequence reads from swine facility or household dusts, respectively, were of swine or human origin, respectively. In contrast only 2% of such reads from grain elevator dust were of mammalian origin. These metagenomic shotgun reads of mammalian origin were excluded from our analyses of agricultural dust microbiota. The ten most prevalent bacterial taxa identified in swine facility compared to grain elevator or household dust were comprised of 75%, 16%, and 42% gram-positive organisms, respectively. Four of the top five swine facility dust genera were assignable (<i>Clostridium, Lactobacillus, Ruminococcus,</i> and <i>Eubacterium</i>, ranging from 4% to 19% relative abundance). The relative abundances of these four genera were lower in dust from grain elevators or pet-free households. These analyses also highlighted the predominance in swine facility dust of <i>Firmicutes</i> (70%) at the phylum level, <i>Clostridia</i> (44%) at the Class level, and <i>Clostridiales</i> at the Order level (41%). In summary, shotgun pyrosequencing metagenomic analyses of agricultural dusts show that they differ qualitatively and quantitatively at the level of microbial taxa present, and that the bioinformatic analyses used for such studies must be carefully designed to avoid the potential contribution of non-microbial DNA, e.g. from resident mammals.</p></div

Taxonomic classification of metagenomic reads from swine confinement facility dust, grain elevator dust and household dust without pets.

<p><b><i>A.</i></b> Domain level; <b><i>B.</i></b> Phylum level. Each pie chart represents relative abundance values expressed as the total number domains (“pre-filtered” dataset) or phyla (“filtered” dataset) from swine confinement facility dust, grain elevator dust and household dust without pets. Other sequences equals reads that align significantly to the M5NR database that are derived from taxa not listed as descendants from one of the domains; Unassigned equal reads that do not align significantly to any M5NR database sequence.</p

Candidate biomarker analyses based on Genus abundance profiles from dust-derived shotgun metagenomic read datasets.

<p>STAMP Extended Error Graph of the top ranked genera identified in a two-group statistical test comparing MG-RAST Genus abundance profiles generated using the M5NR database for swine facility dust dataset (red) and both household and grain elevator dust datasets (black). Ranking of the genera is based on significance (q) values, which were corrected for multiple testing and show the indicated value for Storey’s false discovery rate. The unclassified genus shown is annotated by MG-RAST as derived from <i>Erysipelotrichaceae</i>.</p

Genus abundance ranking of swine confinement facility dust reads in comparison to grain elevator dust and household dust without pets.

<p><i>Relative</i> abundance values are expressed on the ordinate as a fraction of the total number of genera identified in swine dust. The 15 most abundant genera identified using the swine facility dust shotgun metagenomic reads and the M5NR database are shown. Relative abundance values were calculated for these same 15 genera for dust collected from grain elevators and households without pets. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095578#pone.0095578.s004" target="_blank">Figure S4</a> for comparisons at the Phylum, Class, Order, and Family taxonomic levels. Black = Swine dust; Gray = House dust; White = Grain dust.</p

BLASTn screening scheme used for alignment of shotgun metagenomic reads to swine and human genomes.

<p>Post-QC shotgun metagenomic reads from swine facility dust, swine feces, grain elevator dust and household dust without pets were aligned against the swine draft (ssc_ref_Sscrofa10) and human genome (hs_ref_GRCh37.p5) sequence. Reads that aligned against the swine draft and/or the human reference genome sequence with an expect value of less than 10⁻⁵ were subsequently aligned against all finished and draft bacterial genome sequence assemblies currently available at the NCBI FTP site on 11/16/2011. Except were indicated, filtered reads were used in all subsequent bioinformatics analyses. * From the NCBI Sequence Read Archive; E_S/H =  expect values for reads aligned to swine and/or human genomes; E_F =  expect values for reads with poor alignment with swine or human genomes (filtered reads).</p

Principal Component Analysis (PCA) of shotgun metagenomic reads from swine facility dust, grain elevator dust, household dust without pets and swine feces: Relative abundance of Genera.

<p>PCA was performed in STAMP using MG-RAST Genus-level organism abundance profiles that were derived from two swine facility dust samples, two grain elevator dust samples, two household dust samples without pets and three swine feces samples. <i>A.</i> The “filtered” reads from the swine confinement facility dust and the household dust datasets were used in the analyses. <i>B.</i> The “filtered” and “swine/human” reads from the swine confinement facility dust and the household dust datasets, respectively, were used in the analyses. Each symbol represents a sample. • Grain elevator dust (green); ▪ Household dust without pets (“filtered”, yellow); ▴ Swine confinement facility dust (“filtered”, red); ♦ Swine feces (blue).</p

Principal coordinate analysis of 9-month study samples.

<p>Principal coordinate analysis was performed using Qiime version 1.9.1-dev and Weighted UniFrac, and the results for principal coordinates 1 and 2 and 3 are shown. Square = T2; Circle = T1.</p