Short-term structure and functional changes in bacterial community composition following amendment with biosolids compost

2011 ◽  
Vol 48 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Eddie Cytryn ◽  
Larissa Kautsky ◽  
Maya Ofek ◽  
Raphi T. Mandelbaum ◽  
Dror Minz
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Term Structure ◽  
Bacterial Community Composition ◽  
Short Term ◽  
Functional Changes ◽  
Biosolids Compost

Cow responses and evolution of the rumen bacterial and methanogen community following a complete rumen content transfer

2018 ◽  
Vol 156 (8) ◽  
pp. 1047-1058
Author(s):  
T. De Mulder ◽  
L. Vandaele ◽  
N. Peiren ◽  
A. Haegeman ◽  
T. Ruttink ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Bacterial Community Composition ◽  
Rumen Content ◽  
Short Term ◽  
Microbiome Composition ◽  
Nutritional Conditions ◽  
Microbiome Diversity ◽  
Methanogen Community ◽  
New Community

AbstractUnderstanding the rumen microbial ecosystem requires the identification of factors that influence the community structure, such as nutrition, physiological condition of the host and host–microbiome interactions. The objective of the current study was to describe the rumen microbial communities before, during and after a complete rumen content transfer. The rumen contents of one donor cow were removed completely and used as inoculum for the emptied rumen of the donor itself and three acceptor cows under identical physiological and nutritional conditions. Temporal changes in microbiome composition and rumen function were analysed for each of four cows over a period of 6 weeks. Shortly after transfer, the cows showed different responses to perturbation of their rumen content. Feed intake depression in the first 2 weeks after transfer resulted in short-term changes in milk production, methane emission, fatty acid composition and rumen bacterial community composition. These effects were more pronounced in two cows, whose microbiome composition showed reduced diversity. The fermentation metrics and microbiome diversity of the other two cows were not affected. Their rumen bacterial community initially resembled the composition of the donor but evolved to a new community profile that resembled neither the donor nor their original composition. Descriptive data presented in the current paper show that the rumen bacterial community composition can quickly recover from a reduction in microbiome diversity after a severe perturbation. In contrast to the bacteria, methanogenic communities were more stable over time and unaffected by stress or host effects.

Assessment of Bacterial Community Composition of Anaerobic Granular Sludge in Response to Short-Term Uranium Exposure

2018 ◽  
Vol 76 (3) ◽  
pp. 648-659 ◽  
Author(s):  
Taotao Zeng ◽  
Shiqi Zhang ◽  
Xiang Gao ◽  
Guohua Wang ◽  
Piet N. L. Lens ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Bacterial Community Composition ◽  
Granular Sludge ◽  
Anaerobic Granular Sludge ◽  
Short Term

Short-term bacterial community composition dynamics in response to accumulation and breakdown of Microcystis blooms

2011 ◽  
Vol 45 (4) ◽  
pp. 1702-1710 ◽  
Author(s):  
Huabing Li ◽  
Peng Xing ◽  
Meijun Chen ◽  
Yuanqi Bian ◽  
Qinglong L. Wu
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Bacterial Community Composition ◽  
Short Term ◽  
Microcystis Blooms

scholarly journals Collection of Environmental Variables and Bacterial Community Compositions in Marian Cove, Antarctica, during Summer 2018

Data ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 27
Author(s):  
Hyo-Ryeon Kim ◽  
Jae-Hyun Lim ◽  
Ju-Hyoung Kim ◽  
Il-Nam Kim
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Marine Bacteria ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Bacterial Community Composition ◽  
Composition Data ◽  
Decomposition Of Organic Matter ◽  
Class Level ◽  
Earth’S Climate

Marine bacteria, which are known as key drivers for marine biogeochemical cycles and Earth’s climate system, are mainly responsible for the decomposition of organic matter and production of climate-relevant gases (i.e., CO₂, N₂O, and CH₄). However, research is still required to fully understand the correlation between environmental variables and bacteria community composition. Marine bacteria living in the Marian Cove, where the inflow of freshwater has been rapidly increasing due to substantial glacial retreat, must be undergoing significant environmental changes. During the summer of 2018, we conducted a hydrographic survey to collect environmental variables and bacterial community composition data at three different layers (i.e., the seawater surface, middle, and bottom layers) from 15 stations. Of all the bacterial data, 17 different phylum level bacteria and 21 different class level bacteria were found and Proteobacteria occupy 50.3% at phylum level following Bacteroidetes. Gammaproteobacteria and Alphaproteobacteria, which belong to Proteobacteria, are the highest proportion at the class level. Gammaproteobacteria showed the highest relative abundance in all three seawater layers. The collection of environmental variables and bacterial composition data contributes to improving our understanding of the significant relationships between marine Antarctic regions and marine bacteria that lives in the Antarctic.

scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

scholarly journals Soil Bacterial Community Diversity and Composition as Affected by Tillage Intensity Treatments in Corn-Soybean Production Systems

2021 ◽  
Vol 12 (1) ◽  
pp. 157-172
Author(s):  
Shankar G. Shanmugam ◽  
Normie W. Buehring ◽  
Jon D. Prevost ◽  
William L. Kingery
Keyword(s):  
Microbial Community ◽  
Bacterial Community ◽  
Community Composition ◽  
Production System ◽  
Soil Microbial Community ◽  
Production Systems ◽  
Bacterial Community Composition ◽  
Soybean Production ◽  
Soil Microbial ◽  
Soil Bacterial

Our understanding on the effects of tillage intensity on the soil microbial community structure and composition in crop production systems are limited. This study evaluated the soil microbial community composition and diversity under different tillage management systems in an effort to identify management practices that effectively support sustainable agriculture. We report results from a three-year study to determine the effects on changes in soil microbial diversity and composition from four tillage intensity treatments and two residue management treatments in a corn-soybean production system using Illumina high-throughput sequencing of 16S rRNA genes. Soil samples were collected from tillage treatments at locations in the Southern Coastal Plain (Verona, Mississippi, USA) and Southern Mississippi River Alluvium (Stoneville, Mississippi, USA) for soil analysis and bacterial community characterization. Our results indicated that different tillage intensity treatments differentially changed the relative abundances of bacterial phyla. The Mantel test of correlations indicated that differences among bacterial community composition were significantly influenced by tillage regime (rM = 0.39, p ≤ 0.0001). Simpson’s reciprocal diversity index indicated greater bacterial diversity with reduction in tillage intensity for each year and study location. For both study sites, differences in tillage intensity had significant influence on the abundance of Proteobacteria. The shift in the soil bacterial community composition under different tillage systems was strongly correlated to changes in labile carbon pool in the system and how it affected the microbial metabolism. This study indicates that soil management through tillage intensity regime had a profound influence on diversity and composition of soil bacterial communities in a corn-soybean production system.

scholarly journals Longitudinal assessment of the bovine ocular bacterial community dynamics in calves

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Alison C. Bartenslager ◽  
Nirosh D. Althuge ◽  
John Dustin Loy ◽  
Matthew M. Hille ◽  
Matthew L. Spangler ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Community Dynamics ◽  
Bacterial Community Composition ◽  
Clinical Signs ◽  
Sampling Time ◽  
Experimental Population ◽  
Opportunistic Pathogens ◽  
Time Periods ◽  
Over Time

Abstract Background Infectious Bovine Keratoconjunctivitis (IBK), commonly known as pinkeye, is one of the most significant diseases of beef cattle. As such, IBK costs the US beef industry at least 150 million annually. However, strategies to prevent IBK are limited, with most cases resulting in treatment with antibiotics once the disease has developed. Longitudinal studies evaluating establishment of the ocular microbiota may identify critical risk periods for IBK outbreaks or changes in the microbiota that may predispose animals to IBK. Results In an attempt to characterize the establishment and colonization patterns of the bovine ocular microbiota, we conducted a longitudinal study consisting of 227 calves and evaluated the microbiota composition over time using amplicon sequence variants (ASVs) based on 16S rRNA sequencing data and culture-based approaches. Beef calves on trial consisted of both male (intact) and females. Breeds were composed of purebred Angus and composites with varying percentages of Simmental, Angus, and Red Angus breeds. Average age at the start of the trial was 65 days ±15.02 and all calves remained nursing on their dam until weaning (day 139 of the study). The trial consisted of 139 days with four sampling time points on day 0, 21, 41, and 139. The experimental population received three different vaccination treatments (autogenous, commercial (both inactivated bacteria), and adjuvant placebo), to assess the effectiveness of different vaccines for IBK prevention. A significant change in bacterial community composition was observed across time periods sampled compared to the baseline (p < 0.001). However, no treatment effect of vaccine was detected within the ocular bacterial community. The bacterial community composition with the greatest time span between sampling time periods (98d span) was most similar to the baseline sample collected, suggesting re-establishment of the ocular microbiota to baseline levels over time after perturbation. The effect of IgA levels on the microbial community was investigated in a subset of cattle within the study. However, no significant effect of IgA was observed. Significant changes in the ocular microbiota were identified when comparing communities pre- and post-clinical signs of IBK. Additionally, dynamic changes in opportunistic pathogens Moraxella spp. were observed and confirmed using culture based methods. Conclusions Our results indicate that the bovine ocular microbiota is well represented by opportunistic pathogens such as Moraxella and Mycoplasma. Furthermore, this study characterizes the diversity of the ocular microbiota in calves and demonstrates the plasticity of the ocular microbiota to change. Additionally, we demonstrate the ocular microbiome in calves is similar between the eyes and the perturbation of one eye results in similar changes in the other eye. We also demonstrate the bovine ocular microbiota is slow to recover post perturbation and as a result provide opportunistic pathogens a chance to establish within the eye leading to IBK and other diseases. Characterizing the dynamic nature of the ocular microbiota provides novel opportunities to develop potential probiotic intervention to reduce IBK outbreaks in cattle.

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