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1. Microbiome. 2015 Oct 5;3:48. doi: 10.1186/s40168-015-0110-9.

 

An accurate and efficient experimental approach for characterization of the

complex oral microbiota.

 

Zheng W(1), Tsompana M(2,)(3), Ruscitto A(4), Sharma A(5), Genco R(6,)(7), Sun

Y(8,)(9), Buck MJ(10,)(11).

 

Author information:

(1)Department of Computer Science and Engineering, State University of New York

at Buffalo, Buffalo, NY, 14203, USA. wzheng4@buffalo.edu. (2)Center of Excellence

in Bioinformatics and Life Sciences, State University of New York at Buffalo,

Buffalo, NY, 14203, USA. tsompana@buffalo.edu. (3)Department of Biochemistry,

State University of New York at Buffalo, Buffalo, NY, 14203, USA.

tsompana@buffalo.edu. (4)Department of Oral Biology, State University of New York

at Buffalo, Buffalo, NY, 14203, USA. aruscitt@buffalo.edu. (5)Department of Oral

Biology, State University of New York at Buffalo, Buffalo, NY, 14203, USA.

sharmaa@buffalo.edu. (6)Department of Oral Biology, State University of New York

at Buffalo, Buffalo, NY, 14203, USA. rjgenco@buffalo.edu. (7)Department of

Microbiology and Immunology, State University of New York at Buffalo, Buffalo,

NY, 14203, USA. rjgenco@buffalo.edu. (8)Department of Computer Science and

Engineering, State University of New York at Buffalo, Buffalo, NY, 14203, USA.

yijunsun@buffalo.edu. (9)Department of Microbiology and Immunology, State

University of New York at Buffalo, Buffalo, NY, 14203, USA. yijunsun@buffalo.edu.

(10)Center of Excellence in Bioinformatics and Life Sciences, State University of

New York at Buffalo, Buffalo, NY, 14203, USA. mjbuck@buffalo.edu. (11)Department

of Biochemistry, State University of New York at Buffalo, Buffalo, NY, 14203,

USA. mjbuck@buffalo.edu.

 

BACKGROUND: Currently, taxonomic interrogation of microbiota is based on

amplification of 16S rRNA gene sequences in clinical and scientific settings.

Accurate evaluation of the microbiota depends heavily on the primers used, and

genus/species resolution bias can arise with amplification of non-representative

genomic regions. The latest Illumina MiSeq sequencing chemistry has extended the

read length to 300 bp, enabling deep profiling of large number of samples in a

single paired-end reaction at a fraction of the cost. An increasingly large

number of researchers have adopted this technology for various microbiome studies

targeting the 16S rRNA V3-V4 hypervariable region.

RESULTS: To expand the applicability of this powerful platform for further

descriptive and functional microbiome studies, we standardized and tested an

efficient, reliable, and straightforward workflow for the amplification, library

construction, and sequencing of the 16S V1-V3 hypervariable region using the new

2 × 300 MiSeq platform. Our analysis involved 11 subgingival plaque samples from

diabetic and non-diabetic human subjects suffering from periodontitis. The

efficiency and reliability of our experimental protocol was compared to 16S V3-V4

sequencing data from the same samples. Comparisons were based on measures of

observed taxonomic richness and species evenness, along with Procrustes analyses

using beta(β)-diversity distance metrics. As an experimental control, we also

analyzed a total of eight technical replicates for the V1-V3 and V3-V4 regions

from a synthetic community with known bacterial species operon counts. We show

that our experimental protocol accurately measures true bacterial community

composition. Procrustes analyses based on unweighted UniFrac β-diversity metrics

depicted significant correlation between oral bacterial composition for the V1-V3

and V3-V4 regions. However, measures of phylotype richness were higher for the

V1-V3 region, suggesting that V1-V3 offers a deeper assessment of population

diversity and community ecology for the complex oral microbiota.

CONCLUSION: This study provides researchers with valuable experimental evidence

for the selection of appropriate 16S amplicons for future human oral microbiome

studies. We expect that the tested 16S V1-V3 framework will be widely applicable

to other types of microbiota, allowing robust, time-efficient, and inexpensive

examination of thousands of samples for population, phylogenetic, and functional

crossectional and longitutidal studies.

 

DOI: 10.1186/s40168-015-0110-9

PMCID: PMC4593206

PMID: 26437933  [PubMed - indexed for MEDLINE]

 

 

2. Appl Environ Microbiol. 2015 Nov;81(22):7893-904. doi: 10.1128/AEM.02294-15. Epub

2015 Sep 4.

 

Coexistence of Lactic Acid Bacteria and Potential Spoilage Microbiota in a Dairy

Processing Environment.

 

Stellato G(1), De Filippis F(1), La Storia A(1), Ercolini D(2).

 

Author information:

(1)Department of Agricultural Sciences, Division of Microbiology, University of

Naples Federico II, Portici, Italy. (2)Department of Agricultural Sciences,

Division of Microbiology, University of Naples Federico II, Portici, Italy

ercolini@unina.it.

 

Microbial contamination in food processing plants can play a fundamental role in

food quality and safety. In this study, the microbiota in a dairy plant was

studied by both 16S rRNA- and 26S rRNA-based culture-independent high-throughput

amplicon sequencing. Environmental samples from surfaces and tools were studied

along with the different types of cheese produced in the same plant. The

microbiota of environmental swabs was very complex, including more than 200

operational taxonomic units with extremely variable relative abundances (0.01 to

99%) depending on the species and sample. A core microbiota shared by 70% of the

samples indicated a coexistence of lactic acid bacteria with a remarkable level

of Streptococcus thermophilus and possible spoilage-associated bacteria,

including Pseudomonas, Acinetobacter, and Psychrobacter, with a relative

abundance above 50%. The most abundant yeasts were Kluyveromyces marxianus,

Yamadazyma triangularis, Trichosporon faecale, and Debaryomyces hansenii.

Beta-diversity analyses showed a clear separation of environmental and cheese

samples based on both yeast and bacterial community structure. In addition,

predicted metagenomes also indicated differential distribution of metabolic

pathways between the two categories of samples. Cooccurrence and coexclusion

pattern analyses indicated that the occurrence of potential spoilers was excluded

by lactic acid bacteria. In addition, their persistence in the environment can be

helpful to counter the development of potential spoilers that may contaminate the

cheeses, with possible negative effects on their microbiological quality.

 

Copyright © 2015, American Society for Microbiology. All Rights Reserved.

 

DOI: 10.1128/AEM.02294-15

PMCID: PMC4616952

PMID: 26341209  [PubMed - indexed for MEDLINE]

 

 

3. J Allergy Clin Immunol. 2015 Aug;136(2):334-42.e1. doi:

10.1016/j.jaci.2015.02.008. Epub 2015 Mar 26.

 

Sinus microbiota varies among chronic rhinosinusitis phenotypes and predicts

surgical outcome.

 

Ramakrishnan VR(1), Hauser LJ(2), Feazel LM(3), Ir D(3), Robertson CE(4), Frank

DN(5).

 

Author information:

(1)Department of Otolaryngology-Head and Neck Surgery, University of Colorado

School of Medicine, Aurora, Colo. Electronic address:

vijay.ramakrishnan@ucdenver.edu. (2)Department of Otolaryngology-Head and Neck

Surgery, University of Colorado School of Medicine, Aurora, Colo. (3)Division of

Infectious Diseases, University of Colorado School of Medicine, Aurora, Colo.

(4)Microbiome Research Consortium, University of Colorado School of Medicine,

Aurora, Colo; Department of Molecular, Cellular, and Developmental Biology,

University of Colorado, Boulder, Colo. (5)Division of Infectious Diseases,

University of Colorado School of Medicine, Aurora, Colo; Microbiome Research

Consortium, University of Colorado School of Medicine, Aurora, Colo.

 

BACKGROUND: Chronic rhinosinusitis (CRS) is a prevalent multifactorial disease

process in which bacteria are believed to play a role in the propagation of

inflammation. Multiple subtypes of CRS have been described based on clinical and

pathologic features, but a detailed examination of the sinus microbiota in

patients with CRS and its clinical subtypes has yet to be performed.

OBJECTIVE: We sought to examine the resident microbiota of CRS subtypes and

determine whether bacterial diversity is a predictor of disease outcomes.

METHODS: Sinus swabs from patients with CRS and healthy subjects collected during

endoscopic sinus surgery were analyzed by means of molecular phylogenetic

analysis of 16S rDNA pyrosequences.

RESULTS: Fifty-six patients with CRS and 26 control subjects were studied.

Biodiversity was similar between the CRS and control groups. Among the CRS

subtypes examined, only 2 conditions (presence of purulence and comorbid

condition of asthma) were associated with significant alterations in microbial

community composition. In 27 patients with CRS who were followed postoperatively,

those with better outcomes had more diverse bacterial communities present at the

time of surgery, along with higher relative abundances of Actinobacteria.

CONCLUSION: Analysis of microbiota in a large cohort reveals that particular CRS

phenotypes (asthma and purulence) are characterized by distinct compositions of

resident bacterial communities. We found that bacterial diversity and composition

are predictors of surgical outcome, promoting the concept of community ecology in

patients with CRS.

 

Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by

Elsevier Inc. All rights reserved.

 

DOI: 10.1016/j.jaci.2015.02.008

PMID: 25819063  [PubMed - indexed for MEDLINE]

 

 

4. BMC Microbiol. 2015 Aug 12;15:160. doi: 10.1186/s12866-015-0497-2.

 

Cilantro microbiome before and after nonselective pre-enrichment for Salmonella

using 16S rRNA and metagenomic sequencing.

 

Jarvis KG(1), White JR(2), Grim CJ(3,)(4), Ewing L(5), Ottesen AR(6), Beaubrun

JJ(7), Pettengill JB(8), Brown E(9), Hanes DE(10).

 

Author information:

(1)U. S. Food and Drug Administration, Center for Food Safety and Applied

Nutrition, OARSA, Laurel, MD, USA. karen.jarvis@fda.hhs.gov. (2)Oak Ridge

Institute for Science and Technology, Oak Ridge, TN, USA.

james.dna.white@gmail.com. (3)U. S. Food and Drug Administration, Center for Food

Safety and Applied Nutrition, OARSA, Laurel, MD, USA.

Christopher.Grim@fda.hhs.gov. (4)Oak Ridge Institute for Science and Technology,

Oak Ridge, TN, USA. Christopher.Grim@fda.hhs.gov. (5)U. S. Food and Drug

Administration, Center for Food Safety and Applied Nutrition, OARSA, Laurel, MD,

USA. Laura.Ewing-Peeples@fda.hhs.gov. (6)U. S. Food and Drug Administration,

Center for Food Safety and Applied Nutrition, ORS, College Park, MD, USA.

Andrea.Ottesen@fda.hhs.gov. (7)U. S. Food and Drug Administration, Center for

Food Safety and Applied Nutrition, OARSA, Laurel, MD, USA.

Junia.Jean-GillesBeaubrun@fda.hhs.gov. (8)U. S. Food and Drug Administration,

Center for Food Safety and Applied Nutrition, ORS, College Park, MD, USA.

James.Pettengill@fda.hhs.gov. (9)U. S. Food and Drug Administration, Center for

Food Safety and Applied Nutrition, ORS, College Park, MD, USA.

Eric.Brown@fda.hhs.gov. (10)U. S. Food and Drug Administration, Center for Food

Safety and Applied Nutrition, OARSA, Laurel, MD, USA. Darcy.Hanes@fda.hhs.gov.

 

BACKGROUND: Salmonella enterica is a common cause of foodborne gastroenteritis in

the United States and is associated with outbreaks in fresh produce such as

cilantro. Salmonella culture-based detection methods are complex and time

consuming, and improvments to increase detection sensitivity will benefit

consumers. In this study, we used 16S rRNA sequencing to determine the microbiome

of cilantro. We also investigated changes to the microbial community prior to and

after a 24-hour nonselective pre-enrichment culture step commonly used by

laboratory analysts to resuscitate microorganisms in foods suspected of

contamination with pathogens. Cilantro samples were processed for Salmonella

detection according to the method in the United States Food and Drug

Administration Bacteriological Analytical Manual. Genomic DNA was extracted from

culture supernatants prior to and after a 24-hour nonselective pre-enrichment

step and 454 pyrosequencing was performed on 16S rRNA amplicon libraries. A

database of Enterobacteriaceae 16S rRNA sequences was created, and used to screen

the libraries for Salmonella, as some samples were known to be culture positive.

Additionally, culture positive cilantro samples were examined for the presence of

Salmonella using shotgun metagenomics on the Illumina MiSeq.

RESULTS: Time zero uncultured samples had an abundance of Proteobacteria while

the 24-hour enriched samples were composed mostly of Gram-positive Firmicutes.

Shotgun metagenomic sequencing of Salmonella culture positive cilantro samples

revealed variable degrees of Salmonella contamination among the sequenced

samples.

CONCLUSIONS: Our cilantro study demonstrates the use of high-throughput

sequencing to reveal the microbiome of cilantro, and how the microbiome changes

during the culture-based protocols employed by food safety laboratories to detect

foodborne pathogens. Finding that culturing the cilantro shifts the microbiome to

a predominance of Firmicutes suggests that changing our culture-based methods

will improve detection sensitivity for foodborne enteric pathogens.

 

DOI: 10.1186/s12866-015-0497-2

PMCID: PMC4534111

PMID: 26264042  [PubMed - indexed for MEDLINE]

 

MR DNA is a low cost DNA sequencing service provider specializing in Genome sequencing, 16s sequencing, microbiome, metagenome, bacterial transcriptomes, RNAseq and more

 

VISIT MR DNA for your sequencing

5. Toxicol Lett. 2016 Feb 3;242:60-7. doi: 10.1016/j.toxlet.2015.11.022. Epub 2015

Nov 25.

 

The role of gut microbiota in fetal methylmercury exposure: Insights from a pilot

study.

 

Rothenberg SE(1), Keiser S(2), Ajami NJ(3), Wong MC(4), Gesell J(5), Petrosino

JF(6), Johs A(7).

 

Author information:

(1)Department of Environmental Health Sciences, University of South Carolina, 921

Assembly Street Room 401, Columbia, SC, USA. Electronic address:

rothenbs@mailbox.sc.edu. (2)Greenville Health System, Maternal Fetal Medicine,

890 W. Faris Road, Suite 470, Greenville, SC 29605, USA. Electronic address:

skeiser@ghs.org. (3)The Alkek Center for Metagenomics and Microbiome Research

(CMMR), Department of Molecular Virology and Microbiology, Baylor College of

Medicine, Houston, TX, USA. Electronic address: Nadim.Ajami@bcm.edu. (4)The Alkek

Center for Metagenomics and Microbiome Research (CMMR), Department of Molecular

Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.

Electronic address: matthew.wong@bcm.edu. (5)The Alkek Center for Metagenomics

and Microbiome Research (CMMR), Department of Molecular Virology and

Microbiology, Baylor College of Medicine, Houston, TX, USA. Electronic address:

Jonathan.Gesell@bcm.edu. (6)The Alkek Center for Metagenomics and Microbiome

Research (CMMR), Department of Molecular Virology and Microbiology, Baylor

College of Medicine, Houston, TX, USA. Electronic address: jpetrosi@bcm.edu.

(7)Environmental Sciences Division, Oak Ridge National Laboratory, 1 Bethel

Valley Road, P.O. Box 2008, MS-6038 Oak Ridge, TN, USA. Electronic address:

johsa@ornl.gov.

 

PURPOSE: The mechanisms by which gut microbiota contribute to methylmercury

metabolism remain unclear. Among a cohort of pregnant mothers, the objectives of

our pilot study were to determine (1) associations between gut microbiota and

mercury concentrations in biomarkers (stool, hair and cord blood) and (2) the

contributions of gut microbial mercury methylation/demethylation to stool

methylmercury.

METHODS: Pregnant women (36-39 weeks gestation, n=17) donated hair and stool

specimens, and cord blood was collected for a subset (n=7). The diversity of gut

microbiota was determined using 16S rRNA gene profiling (n=17). For 6 stool

samples with highest/lowest methylmercury concentrations, metagenomic whole

genome shotgun sequencing was employed to search for the mercury methylation gene

(hgcA), and two mer operon genes involved in methylmercury detoxification (merA

and merB).

RESULTS: Seventeen bacterial genera were significantly correlated (increasing or

decreasing) with stool methylmercury, stool inorganic mercury, or hair total

mercury; however, aside from one genus, there was no overlap between biomarkers.

There were no definitive matches for hgcA or merB, while merA was detected at low

concentrations in all six samples.

MAJOR CONCLUSIONS: Proportional differences in stool methylmercury were not

likely attributed to gut microbiota through methylation/demethylation. Gut

microbiota potentially altered methylmercury metabolism using indirect pathways.

 

Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

 

DOI: 10.1016/j.toxlet.2015.11.022

PMCID: PMC4707065 [Available on 2017-02-03]

PMID: 26626101  [PubMed - indexed for MEDLINE]

 

 

6. J Clin Microbiol. 2015 Sep;53(9):2900-7. doi: 10.1128/JCM.01094-15. Epub 2015 Jul

1.

 

Cohort Study of Airway Mycobiome in Adult Cystic Fibrosis Patients: Differences

in Community Structure between Fungi and Bacteria Reveal Predominance of

Transient Fungal Elements.

 

Kramer R(1), Sauer-Heilborn A(2), Welte T(3), Guzman CA(4), Abraham WR(4), Höfle

MG(4).

 

Author information:

(1)Department of Vaccinology and Applied Microbiology, Helmholtz Centre for

Infection Research, Braunschweig, Germany Rolf.Kramer@up.ac.za. (2)Department of

Pneumology, Hanover Medical School, Hannover, Germany. (3)Department of

Pneumology, Hanover Medical School, Hannover, Germany German Centre for Infection

Research and German Centre for Lung Research, Hannover, Germany. (4)Department of

Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research,

Braunschweig, Germany.

 

The respiratory mycobiome is an important but understudied component of the human

microbiota. Like bacteria, fungi can cause severe lung diseases, but their

infection rates are much lower. This study compared the bacterial and fungal

communities of sputum samples from a large cohort of 56 adult patients with

cystic fibrosis (CF) during nonexacerbation periods and under continuous

antibiotic treatment. Molecular fingerprinting based on single-strand

conformation polymorphism (SSCP) analysis revealed fundamental differences

between bacterial and fungal communities. Both groups of microorganisms were

taxonomically classified by identification of gene sequences (16S rRNA and

internal transcript spacer), and prevalences of single taxa were determined for

the entire cohort. Major bacterial pathogens were frequently observed, whereas

fungi of known pathogenicity in CF were detected only in low numbers. Fungal

species richness increased without reaching a constant level (saturation),

whereas bacterial richness showed saturation after 50 patients were analyzed. In

contrast to bacteria, a large number of fungal species were observed together

with high fluctuations over time and among patients. These findings demonstrated

that the mycobiome was dominated by transient species, which strongly suggested

that the main driving force was their presence in inhaled air rather than

colonization. Considering the high exposure of human airways to fungal spores, we

concluded that fungi have low colonization abilities in CF, and colonization by

pathogenic fungal species may be considered a rare event. A comprehensive

understanding of the conditions promoting fungal colonization may offer the

opportunity to prevent colonization and substantially reduce or even eliminate

fungus-related disease progression in CF.

 

Copyright © 2015, American Society for Microbiology. All Rights Reserved.

 

DOI: 10.1128/JCM.01094-15

PMCID: PMC4540938

PMID: 26135861  [PubMed - indexed for MEDLINE]

 

 

7. J Immunol Methods. 2015 Jun;421:112-21. doi: 10.1016/j.jim.2015.04.004. Epub 2015

Apr 17.

 

Mycobiome: Approaches to analysis of intestinal fungi.

 

Tang J(1), Iliev ID(2), Brown J(3), Underhill DM(4), Funari VA(5).

 

Author information:

(1)Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;

Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA

90048, USA. (2)Department of Biomedical Sciences, Cedars-Sinai Medical Center,

Los Angeles, CA 90048, USA; F. Widjaja Foundation Inflammatory Bowel and

Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA

90048, USA. Electronic address: Iliyan.Iliev@cshs.org. (3)Genomics Core,

Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. (4)Department of

Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; F.

Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute,

Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. (5)Genomics Core,

Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Biomedical

Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. Electronic

address: Vincent.Funari@cshs.org.

 

Massively parallel sequencing (MPSS) of bacterial 16S rDNA has been widely used

to characterize the microbial makeup of the human and mouse gastrointestinal

tract. However, techniques for fungal microbiota (mycobiota) profiling remain

relatively under-developed. Compared to 16S profiling, the size and sequence

context of the fungal Internal Transcribed Spacer 1 (ITS1), the most common

target for mycobiota profiling, are highly variable. Using representative

gastrointestinal tract fungi to build a known "mock" library, we examine how this

sequence variability affects data quality derived from Illumina Miseq and Ion

Torrent PGM sequencing pipelines. Also, while analysis of bacterial 16S profiles

is facilitated by the presence of high-quality well-accepted databases of

bacterial 16S sequences, such an accepted database has not yet emerged to

facilitate fungal ITS sequence characterization, and we observe that redundant

and inconsistent ITS1 sequence representation in publically available fungal

reference databases affect quantitation and annotation of species in the gut. To

address this problem, we have constructed a manually curated reference database

optimized for annotation of gastrointestinal fungi. This targeted host-associated

fungi (THF) database contains 1817 ITS1 sequences representing sequence diversity

in genera previously identified in human and mouse gut. We observe that this

database consistently outperforms three common ITS database alternatives on

comprehensiveness, taxonomy assignment accuracy and computational efficiency in

analyzing sequencing data from the mouse gastrointestinal tract.

 

Copyright © 2015 Elsevier B.V. All rights reserved.

 

DOI: 10.1016/j.jim.2015.04.004

PMCID: PMC4451377

PMID: 25891793  [PubMed - indexed for MEDLINE]

 

MR DNA is a low cost DNA sequencing service provider specializing in Genome sequencing, 16s sequencing, microbiome, metagenome, bacterial transcriptomes, RNAseq and more

 

VISIT MR DNA for your sequencing