scholarly journals A comparison of rumen microbial profiles in dairy cows as retrieved by 454 Roche and Ion Torrent (PGM) sequencing platforms

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1599 ◽  
Author(s):  
Nagaraju Indugu ◽  
Kyle Bittinger ◽  
Sanjay Kumar ◽  
Bonnie Vecchiarelli ◽  
Dipti Pitta
Keyword(s):  
Microbial Community ◽  
Dairy Cows ◽  
Community Composition ◽  
Transition Period ◽  
Microbial Community Composition ◽  
Procrustes Analysis ◽  
Ion Torrent ◽  
Sequencing Data ◽  
Bacterial Phyla ◽  
Ion Torrent Pgm

Next generation sequencing (NGS) technology is a widely accepted tool used by microbial ecologists to explore complex microbial communities in different ecosystems. As new NGS platforms continue to become available, it becomes imperative to compare data obtained from different platforms and analyze their effect on microbial community structure. In the present study, we compared sequencing data from both the 454 and Ion Torrent (PGM) platforms on the same DNA samples obtained from the rumen of dairy cows during their transition period. Despite the substantial difference in the number of reads, error rate and length of reads among both platforms, we identified similar community composition between the two data sets. Procrustes analysis revealed similar correlations (M2= 0.319;P= 0.001) in the microbial community composition between the two platforms. Both platforms revealed the abundance of the same bacterial phyla which were Bacteroidetes and Firmicutes; however, PGM recovered an additional four phyla. Comparisons made at the genus level by each platforms revealed differences in only a few genera such asPrevotella,Ruminococcus,SucciniclasticumandTreponema(p< 0.05; chi square test). Collectively, we conclude that the output generated from PGM and 454 yielded concurrent results, provided stringent bioinformatics pipelines are employed.

scholarly journals Soil microbial community composition in a paddy field with different fertilization managements

2021 ◽  
pp. 1-11
Author(s):  
Limin Wang ◽  
Dongfeng Huang
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Soil Properties ◽  
Microbial Communities ◽  
Community Composition ◽  
Microbial Community Composition ◽  
Alpha Diversity ◽  
Phosphorus Fertilization ◽  
Bacterial Phyla ◽  
P Fertilizer

Microbes play vital roles in soil quality; however, their response to N (nitrogen) and P (phosphorus) fertilization in acidic paddy soils of subtropical China remains poorly understood. Here, a 10-year field experiment was conducted to evaluate the effects of different fertilization treatments on microbial communities by Illumina MiSeq sequencing. The results showed that different fertilization treatments did not exert a significant effect on microbial alpha diversity, but altered soil properties, and thus affected microbial community composition. The microbial communities in the T1 (optimized N and P fertilizer) and T2 (excessive N fertilizer) treated soils differed from those in the T0 (no N and P fertilizer) and T3 (excessive P fertilizer) treated soils. In addition, the bacterial phyla Proteobacteria, Chloroflexi, and Acidobacteria, and the fungal phyla Ascomycota and Basidiomycota dominated all the fertilized treatments. Soil total potassium (TK) concentration was the most important factor driving the variation in bacterial community structure under different fertilization regimes, while the major factors shaping fungal community structure were soil TN and NO3–-N (nitrate N). These findings indicate that optimization of N and P application rates might result in variations in soil properties, which changed the microbial community structure in the present study.

scholarly journals Analysis of Microbial Communities in Aged Refuse Based on 16S Sequencing

2021 ◽  
Vol 13 (8) ◽  
pp. 4111
Author(s):  
Fen Hou ◽  
Junjie Du ◽  
Ye Yuan ◽  
Xihui Wu ◽  
Sai Zhao
Keyword(s):  
Microbial Community ◽  
Soil Fertility ◽  
Community Composition ◽  
Microbial Community Composition ◽  
Alpha Diversity ◽  
Amplicon Sequencing ◽  
Undisturbed Soil ◽  
16S Sequencing ◽  
Bacterial Phyla ◽  
Aged Refuse

Aged refuse is widely considered to have certain soil fertility. 16S rRNA amplicon sequencing is used to investigate the microbial community of aged refuse. The aged refuse is found to contain higher soil fertility elements (total nitrogen, total phosphorus, total potassium, etc.) and higher concentrations of heavy metals (Pb, Cd, Zn, and Hg). Taxonomy based on operational taxonomic units (OTUs) shows that Actinobacteria, Proteobacteria, Chloroflexi, Acidobacteria, and Gemmatimonadetes are the main bacterial phyla in the two soils and there is a palpable difference in the microbial community composition between the two groups of samples. The genera Paramaledivibacter, Limnochorda, Marinobacter, Pseudaminobacter, Kocuria, Bdellovibrio, Halomonas, Gillisia, and Membranicola are enriched in the aged refuse. Functional predictive analysis shows that both the control soil and aged refuse have a high abundance of “carbohydrate metabolism” and “amino acid metabolism”, and show differences in the abundance of several metabolism pathways, such as “xenobiotics biodegradation and metabolism” and “lipid metabolism”. Aged refuse and undisturbed soil show significant differences in alpha diversity and microbial community composition. Multiple environmental factors (Hg, TN, Cr, Cd, etc.) significantly impact microorganisms’ abundance (Marinobacter, Halomonas, Blastococcus, etc.). Our study provides valuable knowledge for the ecological restoration of closed landfills.

scholarly journals Development and validation of the Simulator of the Canine Intestinal Microbial Ecosystem (SCIME)1

2019 ◽  
Vol 98 (1) ◽  
Author(s):  
Cindy Duysburgh ◽  
Wendy P Ossieur ◽  
Kim De Paepe ◽  
Pieter Van den Abbeele ◽  
Ramiro Vichez-Vargas ◽  
...  
Keyword(s):  
Microbial Community ◽  
Community Composition ◽  
In Vitro Model ◽  
Microbial Community Composition ◽  
Companion Animals ◽  
Distal Colon ◽  
Bacterial Phyla ◽  
Vitro Model

Abstract Whereas a wide variety of in vitro models have been developed and validated to assess the effect of specific food ingredients on the human gut microbiome, such models have only been developed and applied to a limited extent for companion animals. Since the use of pre- and probiotics to improve gut health is an emerging research topic in the field of companion animals and as dogs are often used as laboratory animals in developing and testing of pharmaceuticals, the current study aimed to establish an adequate canine in vitro model. This consisted of a four-stage reactor composed of a stomach and small intestinal compartment followed by a proximal and distal colon. This semi-continuous gastrointestinal tract model allowed a long-term, region-dependent, and pH-controlled simulation of the colon-associated microbial community of dogs. Upon reaching a functional steady state, the simulated canine microbial community composition proved to be representative of the in vivo situation. Indeed, the predominant bacterial phyla present in the in vitro proximal and distal colon corresponded with the main bacterial phyla detected in the fecal material of the dogs, resulting in an average community composition along the simulated canine gastrointestinal tract of 50.5% Firmicutes, 34.5% Bacteroidetes, 7.4% Fusobacteria, 4.9% Actinobacteria, and 2.7% Proteobacteria. A parallel in vivo–in vitro comparison assessing the effects of fructooligosaccharides (FOS) on the canine microbial community composition showed a consistent stimulation of Lactobacillus concentrations in the in vivo fecal samples as well as in the in vitro canine gut model. Furthermore, the in vitro platform provided additional insights about the prebiotic effect of FOS supplementation of dogs, such as a reduced abundance of Megamonas spp. which are only present in very low abundance in in vivo fecal samples, indicating an interesting application potential of the developed canine in vitro model in research related to gastrointestinal health of dogs.

Microbial Community Composition During Artificial Frosting of Dried Persimmon Fruits

LWT ◽  
2021 ◽  
pp. 111694
Author(s):  
Xiaoxi Chen ◽  
Qin Chen ◽  
Yaxin Liu ◽  
Bin Liu ◽  
Xubo Zhao ◽  
...  
Keyword(s):  
Microbial Community ◽  
Community Composition ◽  
Microbial Community Composition
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