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Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types.

ABSTRACT: Characterization of poultry microbiota is becoming increasingly important due to the growing need for microbiome-based interventions to improve poultry health and production performance. However, the lack of standardized protocols for sampling, sample processing, DNA extraction, sequencing, and bioinformatic analysis can hinder data comparison between studies. Here, we investigated how the DNA extraction process affects microbial community compositions and diversity metrics in different chicken respiratory sample types including choanal and tracheal swabs, nasal cavity and tracheal washes, and lower respiratory lavage. We did a side-by-side comparison of the performances of Qiagen DNeasy blood and tissue (BT) and ZymoBIOMICS DNA Miniprep (ZB) kits. In general, samples extracted with the BT kit yielded higher concentrations of total DNA while those extracted with the ZB kit contained higher numbers of bacterial 16S rRNA gene copies per unit volume. Therefore, the samples were normalized to equal amounts of 16S rRNA gene copies prior to sequencing. For each sample type, all predominant bacterial taxa detected in samples extracted with one kit were present in replicate samples extracted with the other kit and did not show significant differences at the class level. However, a few differentially abundant shared taxa were observed at family and genus levels. Furthermore, between-kit differences in alpha and beta diversity metrics at the amplicon sequence variant level were statistically indistinguishable. Therefore, both kits perform similarly in terms of 16S rRNA gene-based poultry microbiome analysis for the sample types analyzed in this study.

SUBMITTER: Abundo MEC

PROVIDER: S-EPMC7787465 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types.

Abundo Michael E C MEC Ngunjiri John M JM Taylor Kara J M KJM Ji Hana H Ghorbani Amir A K C Mahesh M Weber Bonnie P BP Johnson Timothy J TJ Lee Chang-Won CW

PloS one 20210106 1

Characterization of poultry microbiota is becoming increasingly important due to the growing need for microbiome-based interventions to improve poultry health and production performance. However, the lack of standardized protocols for sampling, sample processing, DNA extraction, sequencing, and bioinformatic analysis can hinder data comparison between studies. Here, we investigated how the DNA extraction process affects microbial community compositions and diversity metrics in different chicken ...[more]

PMID: 33406075

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Project description:IntroductionDetermining bacterial community structure in fecal samples through DNA sequencing is an important facet of intestinal health research. The impact of different commercially available DNA extraction kits upon bacterial community structures has received relatively little attention. The aim of this study was to analyze bacterial communities in volunteer and inflammatory bowel disease (IBD) patient fecal samples extracted using widely used DNA extraction kits in established gastrointestinal research laboratories.MethodsFecal samples from two healthy volunteers (H3 and H4) and two relapsing IBD patients (I1 and I2) were investigated. DNA extraction was undertaken using MoBio Powersoil and MP Biomedicals FastDNA SPIN Kit for Soil DNA extraction kits. PCR amplification for pyrosequencing of bacterial 16S rRNA genes was performed in both laboratories on all samples. Hierarchical clustering of sequencing data was done using the Yue and Clayton similarity coefficient.ResultsDNA extracted using the FastDNA kit and the MoBio kit gave median DNA concentrations of 475 (interquartile range 228-561) and 22 (IQR 9-36) ng/µL respectively (p<0.0001). Hierarchical clustering of sequence data by Yue and Clayton coefficient revealed four clusters. Samples from individuals H3 and I2 clustered by patient; however, samples from patient I1 extracted with the MoBio kit clustered with samples from patient H4 rather than the other I1 samples. Linear modelling on relative abundance of common bacterial families revealed significant differences between kits; samples extracted with MoBio Powersoil showed significantly increased Bacteroidaceae, Ruminococcaceae and Porphyromonadaceae, and lower Enterobacteriaceae, Lachnospiraceae, Clostridiaceae, and Erysipelotrichaceae (p<0.05).ConclusionThis study demonstrates significant differences in DNA yield and bacterial DNA composition when comparing DNA extracted from the same fecal sample with different extraction kits. This highlights the importance of ensuring that samples in a study are prepared with the same method, and the need for caution when cross-comparing studies that use different methods.

Project description:Rapid urbanization has led to a growing sanitation crisis in urban areas of Bangladesh and potential exposure to fecal contamination in the urban environment due to inadequate sanitation and poor fecal sludge management. Limited data are available on environmental fecal contamination associated with different exposure pathways in urban Dhaka. We conducted a cross-sectional study to explore the magnitude of fecal contamination in the environment in low-income, high-income, and transient/floating neighborhoods in urban Dhaka. Ten samples were collected from each of 10 environmental compartments in 10 different neighborhoods (4 low-income, 4 high-income and 2 transient/floating neighborhoods). These 1,000 samples were analyzed with the IDEXX-Quanti-Tray technique to determine most-probable-number (MPN) of E. coli. Samples of open drains (6.91 log10 MPN/100 mL), surface water (5.28 log10 MPN/100 mL), floodwater (4.60 log10 MPN/100 mL), produce (3.19 log10 MPN/serving), soil (2.29 log10 MPN/gram), and street food (1.79 log10 MPN/gram) had the highest mean log10 E. coli contamination compared to other samples. The contamination concentrations did not differ between low-income and high-income neighborhoods for shared latrine swabs, open drains, municipal water, produce, and street foodsamples. E. coli contamination levels were significantly higher (p <0.05) in low-income neighborhoods compared to high-income for soil (0.91 log10 MPN/gram, 95% CI, 0.39, 1.43), bathing water (0.98 log10 MPN/100 mL, 95% CI, 0.41, 1.54), non-municipal water (0.64 log10 MPN/100 mL, 95% CI, 0.24, 1.04), surface water (1.92 log10 MPN/100 mL, 95% CI, 1.44, 2.40), and floodwater (0.48 log10 MPN/100 mL, 95% CI, 0.03, 0.92) samples. E. coli contamination were significantly higher (p<0.05) in low-income neighborhoods compared to transient/floating neighborhoods for drain water, bathing water, non-municipal water and surface water. Future studies should examine behavior that brings people into contact with the environment and assess the extent of exposure to fecal contamination in the environment through multiple pathways and associated risks.

Project description:Background:The poultry industry in Pakistan has flourished since the 1960s; however, there are scarce data regarding the impact of occupational exposure on the pulmonary health of farm workers in terms of years working in the industry. The objective of the present study was to assess the effect of poultry environment on the health of occupationally exposed poultry farmers in countries of warm climatic regions, such as Pakistan. This study will also show the effect of exposure to poultry facilities on the health of poultry farmers in the context of low-income countries with a relatively inadequate occupational exposure risk management. Materials and methods:The lung function capacity of 79 poultry workers was measured using a spirometer. Along with spirometry, a structured questionnaire was also administrated to obtain information about age, height, weight, smokers/nonsmokers, years of working experience, and pulmonary health of farm workers. The workers who were directly involved in the care and handling of birds in these intensive facilities were considered and divided into four groups based on their years of working experience: Group I (3-10 months), Group II (1-5 years), Group III (6-10 years), and Group IV (more than 11 years). The forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and the FEV1/FVC ratio were considered to identify lung function abnormalities. Statistical analysis was carried out using independent sample t test, Chi-square test, Pearson's correlation, and linear regression. Results:Based on the performed spirometry, 68 (86 %) of workers were found normal and healthy, whereas 11 (14 %) had a mild obstruction. Of the 11 workers with mild obstruction, the highest number with respect to the total was in Group IV (more than 11 years of working experience) followed by Group III and Group II. Most of the workers were found healthy, which seems to be because of the healthy survivor effect. For the independent sample t test, a significant difference was noticed between healthy and nonhealthy farmers, whereas Chi-square test showed a significant association with height, drugs, and working experience. Linear regression that was stratified by respiratory symptoms showed for workers with symptoms, regression models for all spirometric parameters (FVC, FEV1, and FEV1/FVC) have better predictive power or R square value than those of workers without symptoms. Conclusion:These findings suggest that lung function capacity was directly related to years of working experience. With increasing number of working years, symptoms of various respiratory problems enhanced in the poultry workers. It should be noted that most of the poultry workers were healthy and young, the rationale being that there is a high turnover rate in this profession. The mobility in this job and our finding of 86% of the healthy workers in the present study also proposed healthy worker survivor effect.

Dataset Information

Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types.

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Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types.

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