scholarly journals 2-Way k-Means as a Model for Microbiome Samples

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Weston J. Jackson ◽  
Ipsita Agarwal ◽  
Itsik Pe’er
Keyword(s):  
Unsupervised Learning ◽  
16S Rdna ◽  
Mixture Model ◽  
Vaginal Microbiome ◽  
16S Rdna Sequencing ◽  
Sequencing Data ◽  
Cluster Assignment ◽  
Rdna Sequencing

Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.

scholarly journals 2-Way k-Means as a Model for Microbiome Samples

2017 ◽  
Author(s):  
Weston J. Jackson ◽  
Ipsita Agarwal ◽  
Itsik Pe’er
Keyword(s):  
Unsupervised Learning ◽  
16S Rdna ◽  
Mixture Model ◽  
Vaginal Microbiome ◽  
16S Rdna Sequencing ◽  
Sequencing Data ◽  
Cluster Assignment ◽  
Rdna Sequencing

ABSTRACTMotivationMicrobiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones, and therefore lie in-between them in cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome [email protected]

scholarly journals Foes or Friends? Bacteria Enriched in the Tumor Microenvironment of Colorectal Cancer

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 372 ◽  
Author(s):  
Xu ◽  
Yin ◽  
Zhang ◽  
Lv ◽  
Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Human Health ◽  
16S Rdna ◽  
Intestinal Microbiota ◽  
Intestinal Bacteria ◽  
16S Rdna Sequencing ◽  
Sequencing Data ◽  
Rdna Sequencing ◽  
Prevention And Treatment

Colorectal cancer (CRC) is the second most commonly diagnosed cancer and the third cause of cancer death in the world, while intestinal microbiota is a community of microbes living in human intestine that can potentially impact human health in many ways. Accumulating evidence suggests that intestinal microbiota, especially that from the intestinal bacteria, play a key role in the CRC development; therefore, identification of bacteria involved in CRC development can provide new targets for the CRC diagnosis, prevention, and treatment. Over the past decade, there have been considerable advances in applying 16S rDNA sequencing data to verify associated intestinal bacteria in CRC patients; however, due to variations of individual and environment factors, these results seem to be inconsistent. In this review, we scrutinized the previous 16S rDNA sequencing data of intestinal bacteria from CRC patients, and identified twelve genera that are specifically enriched in the tumor microenvironment. We have focused on their relationship with the CRC development, and shown that some bacteria could promote CRC development, acting as foes, while others could inhibit CRC development, serving as friends, for human health. Finally, we highlighted their potential applications for the CRC diagnosis, prevention, and treatment.

Metabolite fingerprinting of novel Streptomyces UK-238 from the Himalayan forest

2020 ◽  
Vol 16 ◽  
Author(s):  
Nidhi Srivastava ◽  
Indira P. Sarethy
Keyword(s):  
Ethyl Acetate ◽  
16S Rdna ◽  
Retention Index ◽  
Ethyl Acetate Extract ◽  
Similarity Index ◽  
Antimicrobial Drug ◽  
16S Rdna Sequencing ◽  
Metabolite Fingerprinting ◽  
Streptomyces Sp ◽  
Rdna Sequencing

Aims: Characterization of antimicrobial metabolites of novel Streptomyces sp. UK-238. Background: Novel antimicrobial drug discovery is urgently needed due to emerging multi antimicrobial drug resistance among pathogens. Since many years, natural products have provided the basic skeletons for many therapeutic compounds including antibiotics. Bioprospection of un/under explored habitats and focussing on selective isolation of actinobacteria as major reservoir of bio and chemodiversity has yielded good results. Objective: The main objectives of the study were the identification of UK-238 by 16S rDNA sequencing and antimicrobial metabolite fingerprinting of culture extracts. Method: In the present study, a promising isolate, UK-238, has been screened for antimicrobial activity and metabolite fingerprinting from the Himalayan Thano Reserve forest. It was identified by 16S rDNA sequencing. Ethyl acetate extract was partially purified by column chromatography. The pooled active fractions were fingerprinted by GC-MS and compounds were tentatively identified by collated data analysis based on Similarity Index, observed Retention Index from Databases and calculated Retention Index. Results: UK-238 was identified as Streptomyces sp. with 98.4% similarity to S. niveiscabiei. It exhibited broad-spectrum antibacterial and antifungal activity. GC-MS analysis of active fractions of ethyl acetate extract showed the presence of eighteen novel antimicrobial compounds belonging to four major categories- alcohols, alkaloid, esters and peptide. Conclusion: The study confirms that bioprospection of underexplored habitats can elaborate novel bio and chemodiversity.

PSVII-9 Influence of lactose and milk oligosaccharides in whey permeate on jejunal mucosa-associated microbiota in nursery pigs during 7 to 11 kg BW

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 407-407
Author(s):  
Ki Beom Jang ◽  
Sung Woo Kim
Keyword(s):  
Alpha Diversity ◽  
Jejunal Mucosa ◽  
16S Rdna Sequencing ◽  
Sequencing Data ◽  
Milk Oligosaccharides ◽  
Whey Permeate ◽  
Nursery Pigs ◽  
Rdna Sequencing ◽  
Acid Producing Bacteria ◽  
Diversity Estimates

Abstract This study aimed to evaluate supplemental effects of milk carbohydrates in whey permeate on jejunal mucosa-associated microbiota in nursery pigs during 7 to 11 kg BW. A total of 720 pigs at 7.5 kg BW were allotted to 6 treatments (6 pens/treatment and 20 pigs/pen). Treatments were 6 levels of whey permeate supplementation (0, 3.75, 7.50, 11.25, 15.00, and 18.75%) and fed to pigs for 11 d. On d 11, 36 pigs representing median BW of each pen were euthanized to collect the jejunal mucosa to evaluate microbiota in the jejunum by 16S rDNA sequencing. Data were analyzed using contrasts in MIXED procedure of SAS. Whey permeate contained 76.3% lactose and 0.4% milk oligosaccharides. Increasing whey permeate supplementation from 0 to 18.75% did not affect the alpha-diversity estimates of microbiota. Whey permeate supplementation tended to decrease (P = 0.073, 1.59 to 1.22) Firmicutes:Bacteroidetes compared with no addition of whey permeate. Increasing whey permeate supplementation tended to linearly increase Bifidobacteriaceae (P = 0.089, 0.73 to 1.11), decrease Enterobacteriaceae (P = 0.091, 1.04 to 0.52), decrease Stretococcaceae (P = 0.094, 1.50 to 0.71), and caused quadratic changes (P < 0.05) on Lactobacillaceae (maximum: 9.14% at 12.91% whey permeate). Increasing whey permeate supplementation caused a quadratic change (P < 0.05) on Lactobacillus_Salivarius (maximum: 0.92% at 7.35% whey permeate) and tended to cause quadratic changes on Lactobacillus_Rogosae (P = 0.083; maximum: 0.53% at 8.45% whey permeate) and Lactobacillus_Mucosae (P = 0.092; maximum: 0.70% at 6.98% whey permeate). In conclusion, supplementation of whey permeate as sources of lactose and milk oligosaccharides at a range from 7 to 13% seems to be beneficial to nursery pigs by increasing the abundance of lactic acid-producing bacteria in the jejunal mucosa.

Analysis of the bacterial floral structure and diversity of Xuanwei ham by 16S rDNA sequencing

2020 ◽  
Vol 40 (4) ◽  
Author(s):  
Luying Shan ◽  
Yinjiao Li ◽  
Shi Zheng ◽  
Yuanmiao Wei ◽  
Ying Shang
Keyword(s):  
16S Rdna ◽  
16S Rdna Sequencing ◽  
Floral Structure ◽  
Rdna Sequencing

scholarly journals ISOLATION, PURIFICATION, AND CHARACTERIZATION OF LIPASE FROM BACILLUS SP. FROM KITCHEN GREASE

2018 ◽  
Vol 11 (6) ◽  
pp. 224
Author(s):  
Jaiganesh R ◽  
Jaganathan Mk
Keyword(s):  
16S Rdna ◽  
Solvent Tolerance ◽  
16S Rdna Sequencing ◽  
Bacillus Sp ◽  
Purification And Characterization ◽  
Lipase Enzyme ◽  
Sequencing Method ◽  
Rdna Sequencing ◽  
Solvent Tolerant

Objective: The objective of this work was to isolation, purification and characterization of solvent tolerant lipase from Bacillus sp. The objective of this work was to isolation, purification and characterization of solvent tolerant lipase from Bacillus sp. from kitchen grease for a variety of applications including organic synthetic reactions and preparation of enantiomerically pure pharmaceuticals.Methods: Lipase producing isolates were screened from kitchen grease on a selective medium rhodamine B olive oil agar, and tributyrin agar was used to screen the lipase and esterase producing an organism, respectively. The isolate identified using 16S rDNA sequencing method and enzyme activity was quantitatively assayed. Lipase production was characterized in different conditions.Results: The isolate showed highest lipase activity was which later was identified as Bacillus sp. using 16S rDNA sequencing method. The lipase was purified using ammonium sulfate precipitation. The isolate showed excellent tolerance to methanol, ethanol, acetonitrile, and moderate tolerance to butanol. The increased biomass concentration, maximum production, and activity were achieved at 37°C in 24 h incubation, then gradual reduction in production was observed. The maximum activity of lipase enzyme was obtained at pH between 6 and 9.Conclusion: The isolate produce solvent tolerance lipase enzyme and it can be a promising candidate of solvent tolerance lipase enzyme for variety of industrial applications.

Gut microbiota community adaption during young children fecal microbiota transplantation by 16s rDNA sequencing

2016 ◽  
Vol 206 ◽  
pp. 66-72 ◽  
Author(s):  
Jian-Lei Gu ◽  
Yi-Zhong Wang ◽  
Shi-Yi Liu ◽  
Guang-Jun Yu ◽  
Ting Zhang ◽  
...  
Keyword(s):  
Young Children ◽  
Gut Microbiota ◽  
16S Rdna ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
16S Rdna Sequencing ◽  
Rdna Sequencing

scholarly journals Determination of uterine bacterial community in postpartum dairy cows with metritis based on 16S rDNA sequencing

2020 ◽  
Vol 10 ◽  
pp. 100102
Author(s):  
Hao Chen ◽  
Kaiqiang Fu ◽  
Binbin Pang ◽  
Jifang Wang ◽  
Huatao Li ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Dairy Cows ◽  
16S Rdna ◽  
16S Rdna Sequencing ◽  
Rdna Sequencing
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