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41. Microbiologyopen. 2014 Dec;3(6):910-21. doi: 10.1002/mbo3.216. Epub 2014 Sep 26.

 

DNA extraction protocols cause differences in 16S rRNA amplicon sequencing

efficiency but not in community profile composition or structure.

 

Rubin BE(1), Sanders JG, Hampton-Marcell J, Owens SM, Gilbert JA, Moreau CS.

 

Author information:

(1)Committee on Evolutionary Biology, University of Chicago, Chicago, Illinois;

Department of Science and Education, Field Museum of Natural History, Chicago,

Illinois.

 

The recent development of methods applying next-generation sequencing to

microbial community characterization has led to the proliferation of these

studies in a wide variety of sample types. Yet, variation in the physical

properties of environmental samples demands that optimal DNA extraction

techniques be explored for each new environment. The microbiota associated with

many species of insects offer an extraction challenge as they are frequently

surrounded by an armored exoskeleton, inhibiting disruption of the tissues

within. In this study, we examine the efficacy of several commonly used protocols

for extracting bacterial DNA from ants. While bacterial community composition

recovered using Illumina 16S rRNA amplicon sequencing was not detectably biased

by any method, the quantity of bacterial DNA varied drastically, reducing the

number of samples that could be amplified and sequenced. These results indicate

that the concentration necessary for dependable sequencing is around 10,000

copies of target DNA per microliter. Exoskeletal pulverization and tissue

digestion increased the reliability of extractions, suggesting that these steps

should be included in any study of insect-associated microorganisms that relies

on obtaining microbial DNA from intact body segments. Although laboratory and

analysis techniques should be standardized across diverse sample types as much as

possible, minimal modifications such as these will increase the number of

environments in which bacterial communities can be successfully studied.

 

© 2014 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

 

DOI: 10.1002/mbo3.216

PMCID: PMC4263514

PMID: 25257543  [PubMed - indexed for MEDLINE]

 

 

42. Anaerobe. 2015 Aug;34:74-9. doi: 10.1016/j.anaerobe.2015.04.010. Epub 2015 Apr

21.

 

Evaluation of composition and individual variability of rumen microbiota in yaks

by 16S rRNA high-throughput sequencing technology.

 

Guo W(1), Li Y(2), Wang L(3), Wang J(1), Xu Q(1), Yan T(4), Xue B(1).

 

Author information:

(1)Institute of Animal Nutrition, Sichuan Agricultural University, Yaan, Sichuan,

625014, China. (2)Institute of Animal Genetics and Breeding, Sichuan Agricultural

University, Chengdu, Sichuan, 611130, China. (3)Institute of Animal Nutrition,

Sichuan Agricultural University, Yaan, Sichuan, 625014, China; Agri-Food and

Biosciences Institute, Hillsborough, Co Down, BT26 6DR, UK. Electronic address:

wanglizhi08@aliyun.com. (4)Agri-Food and Biosciences Institute, Hillsborough, Co

Down, BT26 6DR, UK.

 

The Yak (Bos grunniens) is a unique species of ruminant animals that is important

to agriculture of the Tibetan plateau, and has a complex intestinal microbial

community. The objective of the present study was to characterize the composition

and individual variability of microbiota in the rumen of yaks using 16S rRNA gene

high-throughput sequencing technique. Rumen samples used in the present study

were obtained from grazing adult male yaks (n = 6) in a commercial farm in Ganzi

Autonomous Prefecture of Sichuan Province, China. Universal prokaryote primers

were used to target the V4-V5 hypervariable region of 16S rRNA gene. A total of

7200 operational taxonomic units (OTUs) were obtained after sequence filtering

and chimera removal. Within these OTUs, 0.56% belonged to Archaea (40 OTUs),

7.19% to unassigned species (518 OTUs), and the remaining OTUs (6642) in all

samples were of bacterial origin. When examining the community structure of

bacteria, we identified 23 phyla within 159 families after taxonomic

summarization. Bacteroidetes and Firmicutes were the predominant phyla accounting

for 39.68% (SD = 0.05) and 45.90% (SD = 0.06), respectively. Moreover, 3764 OTUs

were identified as shared OTUs (i.e. represented in all yaks) and belonged to 35

genera, exhibiting highly variable abundance across individual samples.

Phylogenetic placement of these genera across individual samples was examined. In

addition, we evaluated the distance among the 6 rumen samples by adding taxon

phylogeny using UniFrac, representing 24.1% of average distance. In summary, the

current study reveals a shared rumen microbiome and phylogenetic lineage and

presents novel information on composition and individual variability of the

bacterial community in the rumen of yaks.

 

Copyright © 2015. Published by Elsevier Ltd.

 

DOI: 10.1016/j.anaerobe.2015.04.010

PMID: 25911445  [PubMed - indexed for MEDLINE]

 

 

43. Water Res. 2015 Mar 1;70:471-84. doi: 10.1016/j.watres.2014.12.013. Epub 2014 Dec

16.

 

A framework for establishing predictive relationships between specific bacterial

16S rRNA sequence abundances and biotransformation rates.

 

Helbling DE(1), Johnson DR(2), Lee TK(3), Scheidegger A(4), Fenner K(2).

 

Author information:

(1)School of Civil and Environmental Engineering, Cornell University, Ithaca, NY,

USA. Electronic address: damian.helbling@cornell.edu. (2)Eawag, Swiss Federal

Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland;

Department of Environmental Systems Science, ETH Zurich, 8092 Zurich,

Switzerland. (3)School of Civil and Environmental Engineering, Yonsei University,

Seoul, Republic of Korea. (4)Eawag, Swiss Federal Institute of Aquatic Science

and Technology, 8600 Dübendorf, Switzerland.

 

The rates at which wastewater treatment plant (WWTP) microbial communities

biotransform specific substrates can differ by orders of magnitude among WWTP

communities. Differences in taxonomic compositions among WWTP communities may

predict differences in the rates of some types of biotransformations. In this

work, we present a novel framework for establishing predictive relationships

between specific bacterial 16S rRNA sequence abundances and biotransformation

rates. We selected ten WWTPs with substantial variation in their environmental

and operational metrics and measured the in situ ammonia biotransformation rate

constants in nine of them. We isolated total RNA from samples from each WWTP and

analyzed 16S rRNA sequence reads. We then developed multivariate models between

the measured abundances of specific bacterial 16S rRNA sequence reads and the

ammonia biotransformation rate constants. We constructed model scenarios that

systematically explored the effects of model regularization, model linearity and

non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic

units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large

percentage (greater than 80%) of model scenarios resulted in well-performing and

significant models at intermediate SDTs of 0.13-0.14 and 0.26. The 16S rRNA

sequences consistently selected into the well-performing and significant models

at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then

extend the framework by applying it to the biotransformation rate constants of

ten micropollutants measured in batch reactors seeded with the ten WWTP

communities. We identified phylogenetic groups that were robustly selected into

all well-performing and significant models constructed with biotransformation

rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic

groups can be used as predictive biomarkers of WWTP microbial community activity

towards these specific micropollutants. This work is an important step towards

developing tools to predict biotransformation rates in WWTPs based on taxonomic

composition.

 

Copyright © 2014 Elsevier Ltd. All rights reserved.

 

DOI: 10.1016/j.watres.2014.12.013

PMID: 25594727  [PubMed - indexed for MEDLINE]

 

 

44. BMC Res Notes. 2016 Aug 2;9:380. doi: 10.1186/s13104-016-2172-6.

 

Pipeline for amplifying and analyzing amplicons of the V1-V3 region of the 16S

rRNA gene.

 

Allen HK(1), Bayles DO(2), Looft T(3), Trachsel J(3,)(4), Bass BE(3,)(5), Alt

DP(2), Bearson SM(3), Nicholson T(6), Casey TA(3).

 

Author information:

(1)Food Safety and Enteric Pathogens Research Unit, National Animal Disease

Center, Agricultural Research Service, Ames, IA, 50010, USA.

heather.allen@ars.usda.gov. (2)Infectious Bacterial Diseases Research Unit,

National Animal Disease Center, Agricultural Research Service, Ames, IA, 50010,

USA. (3)Food Safety and Enteric Pathogens Research Unit, National Animal Disease

Center, Agricultural Research Service, Ames, IA, 50010, USA. (4)Interdepartmental

Microbiology Graduate Program, Iowa State University, Ames, IA, 50010, USA.

(5)Diamond V, Cedar Rapids, IA, 52404, USA. (6)Virus and Prion Research Unit,

National Animal Disease Center, Agricultural Research Service, USDA, Ames, IA,

50010, USA.

 

BACKGROUND: Profiling of 16S rRNA gene sequences is an important tool for testing

hypotheses in complex microbial communities, and analysis methods must be updated

and validated as sequencing technologies advance. In host-associated bacterial

communities, the V1-V3 region of the 16S rRNA gene is a valuable region to

profile because it provides a useful level of taxonomic resolution; however, use

of Illumina MiSeq data for experiments targeting this region needs validation.

RESULTS: Using a MiSeq machine and the version 3 (300 × 2) chemistry, we

sequenced the V1-V3 region of the 16S rRNA gene within a mock community. Nineteen

bacteria and one archaeon comprised the mock community, and 12 replicate

amplifications of the community were performed and sequenced. Sequencing the

large fragment (490 bp) that encompasses V1-V3 yielded a higher error rate

(3.6 %) than has been reported when using smaller fragment sizes. This higher

error rate was due to a large number of sequences that occurred only one or two

times among all mock community samples. Removing sequences that occurred one time

among all samples (singletons) reduced the error rate to 1.4 %. Diversity

estimates of the mock community containing all sequences were inflated, whereas

estimates following singleton removal more closely reflected the actual mock

community membership. A higher percentage of the sequences could be taxonomically

assigned after singleton and doubleton sequences were removed, and the

assignments reflected the membership of the input DNA.

CONCLUSIONS: Sequencing the V1-V3 region of the 16S rRNA gene on the MiSeq

platform may require additional sequence curation in silico, and improved error

rates and diversity estimates show that removing low-frequency sequences is

reasonable. When datasets have a high number of singletons, these singletons can

be removed from the analysis without losing statistical power while reducing

error and improving microbiota assessment.

 

DOI: 10.1186/s13104-016-2172-6

PMCID: PMC4970291

PMID: 27485508  [PubMed - in process]

 

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