41. Microbiologyopen. 2014 Dec;3(6):910-21. doi: 10.1002/mbo3.216. Epub 2014 Sep 26.
Rubin BE(1), Sanders JG, Hampton-Marcell J, Owens SM, Gilbert JA, Moreau CS.
(1)Committee on Evolutionary Biology, University of Chicago, Chicago, Illinois;
Department of Science and Education, Field Museum of Natural History, Chicago,
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.
PMID: 25257543 [PubMed - indexed for MEDLINE]
42. Anaerobe. 2015 Aug;34:74-9. doi: 10.1016/j.anaerobe.2015.04.010. Epub 2015 Apr
Guo W(1), Li Y(2), Wang L(3), Wang J(1), Xu Q(1), Yan T(4), Xue B(1).
(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:
firstname.lastname@example.org. (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.
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
Helbling DE(1), Johnson DR(2), Lee TK(3), Scheidegger A(4), Fenner K(2).
(1)School of Civil and Environmental Engineering, Cornell University, Ithaca, NY,
USA. Electronic address: email@example.com. (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
Copyright © 2014 Elsevier Ltd. All rights reserved.
PMID: 25594727 [PubMed - indexed for MEDLINE]
44. BMC Res Notes. 2016 Aug 2;9:380. doi: 10.1186/s13104-016-2172-6.
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).
(1)Food Safety and Enteric Pathogens Research Unit, National Animal Disease
Center, Agricultural Research Service, Ames, IA, 50010, USA.
firstname.lastname@example.org. (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,
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.
PMID: 27485508 [PubMed - in process]