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16s rRNA Sequencing with MR DNA

16S ribosomal  (rRNA) sequencing using next generation sequencing is a method used to identify and compare bacteria and archaea present within almost any type of sample. 16S rRNA gene sequencing is a well-established method for studying phylogeny and taxonomy of samples from complex microbiomes or environments that are difficult or impossible to study.

 

 

 

 

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16. Proteomics. 2015 Oct;15(20):3418-23. doi: 10.1002/pmic.201500104. Epub 2015 Sep

10.

 

Improving the quality of genome, protein sequence, and taxonomy databases: a

prerequisite for microbiome meta-omics 2.0.

 

Pible O(1), Armengaud J(1).

 

Author information:

(1)CEA-Marcoule, DSV/IBITEC-S/SPI/Li2D, Laboratory "Innovative technologies for

Detection and Diagnostics", Bagnols-sur-Cèze, France.

 

High-throughput shotgun metaproteomic approaches on environmental or medical

microbiomes are producing huge amounts of tandem mass spectrometry data. These

can be interpreted either with a general protein sequence database comprising

tens of thousands of sequenced genomes or with a more customized database such as

those obtained after metagenome sequencing of the DNA extracted from the same

sample. However, not all entries in a nucleotide or protein sequence database are

of equal quality and this can critically impact metaproteomic data

interpretation. In this viewpoint article, we exemplify several key issues.

First, either genome or transcriptome data interpretation due to inaccurate

contig assembly and gene prediction may be erroneous, for its mitigation the

metaproteogenomic strategies could have an interesting perspective. Errors in

sample handling and taxonomical characterization may also be problematic.

Cross-contamination of genome sequences is also underestimated while frequent. As

a consequence of these structural errors regarding protein sequences and

additional problems due to homology-based functional annotation of proteins,

specific efforts for better interpretation of metaproteomic data are required. We

propose the development of new bioinformatic pipelines devoted to detection and

correction of errors and contaminations to improve the overall quality of

sequence and taxonomy databases for metaproteomics.

 

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

 

DOI: 10.1002/pmic.201500104

PMID: 26038180  [PubMed - indexed for MEDLINE]

 

 

17. Diabetes Obes Metab. 2013 Sep;15 Suppl 3:61-70. doi: 10.1111/dom.12157.

 

Metagenome and metabolism: the tissue microbiota hypothesis.

 

Burcelin R(1), Serino M, Chabo C, Garidou L, Pomié C, Courtney M, Amar J,

Bouloumié A.

 

Author information:

(1)Institut National de Santé et de Recherche Médicale (INSERM), U1048, Toulouse,

France. remy.burcelin@inserm.fr

 

Over the last decade, the research community has revealed the role of a new

organ: the intestinal microbiota. It is considered as a symbiont that is part of

our organism since, at birth, it educates the immune system and contributes to

the development of the intestinal vasculature and most probably the nervous

system. With the advent of new generation sequencing techniques, a catalogue of

genes that belong to this microbiome has been established that lists more than 5

million non-redundant genes called the metagenome. Using germ free mice colonized

with the microbiota from different origins, it has been formally demonstrated

that the intestinal microbiota causes the onset of metabolic diseases. Further to

the role of point mutations in our genome, the microbiota can explain the

on-going worldwide pandemic of obesity and diabetes, its dissemination and family

inheritance, as well as the diversity of the associated metabolic phenotypes.

More recently, the discovery of bacterial DNA within host tissues, such as the

liver, the adipose tissue and the blood, which establishes a tissue microbiota,

introduces new opportunities to identify targets and predictive biomarkers based

on the host to microbiota interaction, as well as to define new strategies for

pharmacological, immunomodulatory vaccines and nutritional applications.

 

© 2013 John Wiley & Sons Ltd.

 

DOI: 10.1111/dom.12157

PMID: 24003922  [PubMed - indexed for MEDLINE]

 

 

18. Nan Fang Yi Ke Da Xue Xue Bao. 2015 Jul;35(7):931-4.

 

[Methods, challenges and opportunities for big data analyses of microbiome].

 

[Article in Chinese]

 

Sheng HF(1), Zhou HW.

 

Author information:

(1)Department of Environmental Health, School of Public Health and Tropical

Medicine, Southern Medical University, 510515

China.E-mail:shenghuafang0727@163.com.

 

Microbiome is a novel research field related with a variety of chronic

inflamatory diseases. Technically, there are two major approaches to analysis of

microbiome: metataxonome by sequencing the 16S rRNA variable tags, and metagenome

by shot-gun sequencing of the total microbial (mainly bacterial) genome mixture.

The 16S rRNA sequencing analyses pipeline includes sequence quality control,

diversity analyses, taxonomy and statistics; metagenome analyses further includes

gene annotation and functional analyses. With the development of the sequencing

techniques, the cost of sequencing will decrease, and big data analyses will

become the central task. Data standardization, accumulation, modeling and disease

prediction are crucial for future exploit of these data. Meanwhile, the

information property in these data, and the functional verification with

culture-dependent and culture-independent experiments remain the focus in future

research. Studies of human microbiome will bring a better understanding of the

relations between the human body and the microbiome, especially in the context of

disease diagnosis and therapy, which promise rich research opportunities.

 

 

PMID: 26198938  [PubMed - indexed for MEDLINE]

 

 

19. Microbiome. 2015 Sep 3;3:37. doi: 10.1186/s40168-015-0102-9.

 

The effect of dietary resistant starch type 2 on the microbiota and markers of

gut inflammation in rural Malawi children.

 

Ordiz MI(1), May TD(2), Mihindukulasuriya K(3), Martin J(3), Crowley J(4), Tarr

PI(1), Ryan K(1), Mortimer E(5), Gopalsamy G(5), Maleta K(6), Mitreva M(3,)(7),

Young G(5), Manary MJ(8,)(9,)(10,)(11).

 

Author information:

(1)Department of Pediatrics, Washington University, St. Louis, MO, 63110, USA.

(2)Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA.

(3)The Genome Institute, Washington University, St. Louis, MO, 63110, USA.

(4)NIH/NIGMS Biomedical Mass Spectrometry, Washington University, St. Louis, MO,

63110, USA. (5)Flinders Centre for Innovation in Cancer, Adelaide, Australia.

(6)Department of Community Health, College of Medicine, Blantyre, Malawi.

(7)Department of Medicine, Washington University, St. Louis, MO, 63110, USA.

(8)Department of Pediatrics, Washington University, St. Louis, MO, 63110, USA.

manary@kids.wustl.edu. (9)Department of Pediatrics, Baylor College of Medicine,

Houston, TX, 77030, USA. manary@kids.wustl.edu. (10)Department of Community

Health, College of Medicine, Blantyre, Malawi. manary@kids.wustl.edu.

(11)Washington University, School of Medicine, St. Louis, MO, 63110, USA.

manary@kids.wustl.edu.

 

BACKGROUND: Resistant starch (RS) decreases intestinal inflammation in some

settings. We tested the hypothesis that gut inflammation will be reduced with

dietary supplementation with RS in rural Malawian children. Eighteen stunted

3-5-year-old children were supplemented with 8.5 g/day of RS type 2 for 4 weeks.

The fecal samples were analyzed for the microbiota, the microbiome, short chain

fatty acids, metabolome, and proteins indicative of inflammation before and after

the intervention. Subjects served as their own controls.

RESULTS: The consumption of RS changed the composition of the microbiota; at the

phylum level Actinobacteria increased, while Firmicutes decreased. Among the most

prevalent genera, Lactobacillus was increased and Roseburia, Blautia, and

Lachnospiracea incertae sedis were decreased. The Shannon H index at the genus

level decreased from 2.02 on the habitual diet and 1.76 after the introduction of

RS (P < 0.01). Fecal acetate concentration decreased, and fecal propionate

concentration increased after RS administration (-5.2 and 2.0 μmol/g,

respectively). Fecal calprotectin increased from 29 ± 69 to 89 ± 49 μg/g (P =

0.003) after RS was given. The lipopolysaccharide biosynthesis pathway was

upregulated.

CONCLUSIONS: Our findings do not support the hypothesis that RS reduces gut

inflammation in rural Malawian children.

 

DOI: 10.1186/s40168-015-0102-9

PMCID: PMC4558878

PMID: 26334878  [PubMed - indexed for MEDLINE]

 

 

20. Nature. 2016 Jun 8;534(7606):191-9. doi: 10.1038/nature18285.

 

Accounting for reciprocal host-microbiome interactions in experimental science.

 

Stappenbeck TS(1), Virgin HW(1).

 

Author information:

(1)Department of Pathology and Immunology, Washington University School of

Medicine, Campus Box 8118, 660 South Euclid Avenue, St Louis, Missouri 63110,

USA.

 

Mammals are defined by their metagenome, a combination of host and microbiome

genes. This knowledge presents opportunities to further basic biology with

translation to human diseases. However, the now-documented influence of the

metagenome on experimental results and the reproducibility of in vivo mammalian

models present new challenges. Here we provide the scientific basis for calling

on all investigators, editors and funding agencies to embrace changes that will

enhance reproducible and interpretable experiments by accounting for metagenomic

effects. Implementation of new reporting and experimental design principles will

improve experimental work, speed discovery and translation, and properly use

substantial investments in biomedical research.

 

DOI: 10.1038/nature18285

PMID: 27279212  [PubMed - indexed for MEDLINE]

 

 

 

76. J Microbiol Methods. 2009 Dec;79(3):266-71. doi: 10.1016/j.mimet.2009.09.012.

Epub 2009 Sep 29.

 

Metagenomic study of the oral microbiota by Illumina high-throughput sequencing.

 

Lazarevic V(1), Whiteson K, Huse S, Hernandez D, Farinelli L, Osterås M,

Schrenzel J, François P.

 

Author information:

(1)Genomic Research Laboratory, Geneva University Hospitals, Rue

Gabrielle-Perret-Gentil 4, CH-1211 Geneva 14, Switzerland.

vladimir.lazarevic@genomic.ch

 

To date, metagenomic studies have relied on the utilization and analysis of reads

obtained using 454 pyrosequencing to replace conventional Sanger sequencing.

After extensively scanning the 16S ribosomal RNA (rRNA) gene, we identified the

V5 hypervariable region as a short region providing reliable identification of

bacterial sequences available in public databases such as the Human Oral

Microbiome Database. We amplified samples from the oral cavity of three healthy

individuals using primers covering an approximately 82-base segment of the V5

loop, and sequenced using the Illumina technology in a single orientation. We

identified 135 genera or higher taxonomic ranks from the resulting 1,373,824

sequences. While the abundances of the most common phyla (Firmicutes,

Proteobacteria, Actinobacteria, Fusobacteria and TM7) are largely comparable to

previous studies, Bacteroidetes were less present. Potential sources for this

difference include classification bias in this region of the 16S rRNA gene, human

sample variation, sample preparation and primer bias. Using an Illumina

sequencing approach, we achieved a much greater depth of coverage than previous

oral microbiota studies, allowing us to identify several taxa not yet discovered

in these types of samples, and to assess that at least 30,000 additional reads

would be required to identify only one additional phylotype. The evolution of

high-throughput sequencing technologies, and their subsequent improvements in

read length enable the utilization of different platforms for studying

communities of complex flora. Access to large amounts of data is already leading

to a better representation of sample diversity at a reasonable cost.

 

DOI: 10.1016/j.mimet.2009.09.012

PMCID: PMC3568755

PMID: 19796657  [PubMed - indexed for MEDLINE]

 

 

16s rRNA Sequencing with MR DNA

16S ribosomal  (rRNA) sequencing using next generation sequencing is a method used to identify and compare bacteria and archaea present within almost any type of sample. 16S rRNA gene sequencing is a well-established method for studying phylogeny and taxonomy of samples from complex microbiomes or environments that are difficult or impossible to study.

 

 

 

 

16s sequencing illumina or PGM low cost prices with MR DNA

MR DNA is a next generation sequencing provider with low cost 16s sequencing services.

 

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