scholarly journals The Characterization of Microbial Communities Response to Shallow Groundwater Contamination in Typical Piedmont Region of Taihang Mountains in the North China Plain

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 736 ◽  
Author(s):  
He ◽  
Ning ◽  
Yang ◽  
Huang ◽  
Cui ◽  
...  
Keyword(s):  
Microbial Diversity ◽  
Microbial Communities ◽  
Dna Sequences ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Nitrate Contamination ◽  
Cumulative Probability ◽  
Organic Nitrate ◽  
Microbial Indicators ◽  
Piedmont Region

Regional-scale nitrate and organic contaminants in the shallow groundwater were investigated in the Piedmont region of Taihang Mountains (PRTM), but the information of the microbial communities is limited. However, microorganisms provide a dominated contribution to indicate and degrade the contaminants in the aquifer. Therefore, this study investigates the microbial diversity and contamination microbial indicators of groundwater samples with different contaminated types to better understand the contamination in the PRTM. Seventy-six samples were collected between two rivers in the Tang-Dasha River Basin covering 4000 km2 in the PRTM. High-throughput sequencing was employed to determine the samples’ DNA sequences. The samples were divided into four groups: background (B), nitrate contamination (N), organic contamination (O) and organic-nitrate contamination (O_N) based on the cumulative probability distribution and the Chinese groundwater standard levels of NO3−, COD and DO concentrations. Then, the microbial diversity and contamination microbial indicators were studied in the four groups. The results showed that the O group exhibited lower diversity than other groups. Bacteria detected in these four groups covered 531 families, 987 genera, and 1881 species. Taxonomic assignment analysis indicated that Rhodobacter, Vogesella, Sphingobium dominated in the O_N group, N group, and O group, and accounted for 18.05%, 17.74%, 16.45% in each group at genus level, respectively. Furthermore, these three genera were identified as contamination microbial indicators to the three types of contamination, respectively. The results provide a potential molecular microbiological method to identity contamination in shallow groundwater, and established a strong foundation for further investigation and remediation in the PRTM.

scholarly journals A meta-analysis and statistical modelling of nitrates in groundwater at the African scale

2016 ◽  
Vol 20 (6) ◽  
pp. 2353-2381 ◽  
Author(s):  
Issoufou Ouedraogo ◽  
Marnik Vanclooster
Keyword(s):  
Statistical Model ◽  
Nitrate Concentration ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Statistical Modelling ◽  
Nitrate Pollution ◽  
Nitrate Contamination ◽  
Data Set ◽  
Nitrate Concentrations ◽  
Groundwater Systems

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space–time distribution of this contamination, as well as understanding the factors that explain this contamination, is important for managing sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the African scale by statistical modelling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology, and anthropogenic data for statistical model development. The maximum, medium, and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, four variables are retained in the statistical explanatory model: (1) depth to groundwater (shallow groundwater, typically < 50 m); (2) recharge rate; (3) aquifer type; and (4) population density. The first three variables represent intrinsic vulnerability of groundwater systems to pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the African scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class, and region type improve the prediction of maximum nitrate concentrations at the African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the data set, we do not develop a statistical model for these data. The data-based statistical model presented here represents an important step towards developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data become available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.

scholarly journals A meta-analysis of groundwater contamination by nitrates at the African scale

2016 ◽  
Author(s):  
Issoufou Ouedraogo ◽  
Marnik Vanclooster
Keyword(s):  
Statistical Model ◽  
Nitrate Concentration ◽  
Regional Scale ◽  
Model Development ◽  
Shallow Groundwater ◽  
Nitrate Pollution ◽  
Nitrate Contamination ◽  
Pan African ◽  
Nitrate Concentrations ◽  
Groundwater Systems

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space-time distribution of this contamination, as well as understanding the factors that explain this contamination is important to manage sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the pan-African scale by statistical modeling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology and anthropogenic data for statistical model development. The maximum, medium and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, 4 variables are retained in the statistical explanatory model: (1) Depth to groundwater (shallow groundwater, typically < 50 m); (2) Recharge rate; (3) Aquifer type; and (4) Population density. The former three variables represent intrinsic vulnerability of groundwater systems towards pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the pan-Africa scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class and region type improve the prediction of maximum nitrate concentrations at the pan-African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the dataset, we do not develop a statistical model for these data. The data based statistical model presented here represents an important step toward developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data becomes available and/or combined with more conceptual descriptions of the fate of nutrients in the hydro system.

scholarly journals CaptureSeq: Hybridization-Based Enrichment of cpn60 Gene Fragments Reveals the Community Structures of Synthetic and Natural Microbial Ecosystems

2021 ◽  
Vol 9 (4) ◽  
pp. 816
Author(s):  
Matthew G. Links ◽  
Tim J. Dumonceaux ◽  
E. Luke McCarthy ◽  
Sean M. Hemmingsen ◽  
Edward Topp ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Dna Sequences ◽  
Pcr Amplification ◽  
Shotgun Sequencing ◽  
Natural Ecosystems ◽  
Gene Markers ◽  
Microbial Profile ◽  
Microbial Ecosystems ◽  
Pcr Targeting

Background. The molecular profiling of complex microbial communities has become the basis for examining the relationship between the microbiome composition, structure and metabolic functions of those communities. Microbial community structure can be partially assessed with “universal” PCR targeting taxonomic or functional gene markers. Increasingly, shotgun metagenomic DNA sequencing is providing more quantitative insight into microbiomes. However, both amplicon-based and shotgun sequencing approaches have shortcomings that limit the ability to study microbiome dynamics. Methods. We present a novel, amplicon-free, hybridization-based method (CaptureSeq) for profiling complex microbial communities using probes based on the chaperonin-60 gene. Molecular profiles of a commercially available synthetic microbial community standard were compared using CaptureSeq, whole metagenome sequencing, and 16S universal target amplification. Profiles were also generated for natural ecosystems including antibiotic-amended soils, manure storage tanks, and an agricultural reservoir. Results. The CaptureSeq method generated a microbial profile that encompassed all of the bacteria and eukaryotes in the panel with greater reproducibility and more accurate representation of high G/C content microorganisms compared to 16S amplification. In the natural ecosystems, CaptureSeq provided a much greater depth of coverage and sensitivity of detection compared to shotgun sequencing without prior selection. The resulting community profiles provided quantitatively reliable information about all three domains of life (Bacteria, Archaea, and Eukarya) in the different ecosystems. The applications of CaptureSeq will facilitate accurate studies of host-microbiome interactions for environmental, crop, animal and human health. Conclusions: cpn60-based hybridization enriched for taxonomically informative DNA sequences from complex mixtures. In synthetic and natural microbial ecosystems, CaptureSeq provided sequences from prokaryotes and eukaryotes simultaneously, with quantitatively reliable read abundances. CaptureSeq provides an alternative to PCR amplification of taxonomic markers with deep community coverage while minimizing amplification biases.

STUDY OF MICROBIAL COMMUNITIES IN MEAT PROCESSING ENTERPRISES

2021 ◽  
Author(s):  
Anastasia Arturovna Semenova ◽  
◽  
Yulia Konstantinovna Yushina ◽  
Maria Alexandrovna Grudistova ◽  
Elena Viktorovna Zaiko ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Microbial Diversity ◽  
Microbial Communities ◽  
Biofilm Formation ◽  
Pathogenic Microorganisms ◽  
Meat Processing ◽  
Production Environment

The article discusses the results of a study of the microbial diversity of objects in the production environment of two meat processing enterprises, including antibiotic resistance, isolated strains of pathogenic microorganisms and their ability to biofilm formation.

scholarly journals How, when, and where relic DNA biases estimates of microbial diversity

2017 ◽  
Author(s):  
JT Lennon ◽  
ME Muscarella ◽  
SA Muscarella ◽  
BK Lehmkuhl
Keyword(s):  
Microbial Diversity ◽  
Dna Sequences ◽  
Species Abundance ◽  
Rrna Gene ◽  
Bacterial Dna ◽  
Soil Sediment ◽  
Species Abundance Distributions ◽  
Abundance And Diversity ◽  
Dna Pool ◽  
Biased Estimates

Extracellular or “relic” DNA is one of the largest pools of nucleic acids in the mbiosphere1,2. Relic DNA can influence a number of important ecological and evolutionary processes, but it may also bias estimates of microbial abundance and diversity, which has implications for understanding environmental, engineered, and host-associated ecosystems. We developed models capturing the fundamental processes that regulate the size and composition of the relic DNA pools to identify scenarios leading to biased estimates of biodiversity. Our models predict that bias increases with relic DNA pool size, but only when the species abundance distributions (SAD) of relic and intact DNA are distinct from one another. We evaluated our model predictions by quantifying relic DNA and assessing its contribution to bacterial diversity using 16S rRNA gene sequences collected from different ecosystem types, including soil, sediment, water, and the mammalian gut. On average, relic DNA made up 33 % of the total bacterial DNA pool, but exceeded 80 % in some samples. Despite its abundance, relic DNA had no effect on estimates of taxonomic and phylogenetic diversity, even in ecosystems where processes such as the physical protection of relic DNA are common and predicted by our models to generate bias. Rather, our findings are consistent with the expectation that relic DNA sequences degrade in proportion to their abundance and therefore may contribute minimally to estimates of microbial diversity.

scholarly journals STRUKTUR KOMUNITAS MIKROBA TANAH DAN IMPLIKASINYA DALAM MEWUJUDKAN SISTEM PERTANIAN BERKELANJUTAN

el–Hayah ◽  
2012 ◽  
Vol 1 (4) ◽  
Author(s):  
Prihastuti Prihastuti
Keyword(s):  
Microbial Community ◽  
Microbial Diversity ◽  
Microbial Communities ◽  
Soil Microbial Community ◽  
Soil Microbial Communities ◽  
Organic Soil ◽  
Plant Type ◽  
Soil Microbial Community Structure ◽  
Soil Microbial ◽  
Wide Range

<p>Soils are made up of organic and an organic material. The organic soil component contains all the living creatures in the soil and the dead ones in various stages of decomposition.  Biological activity in soil helps to recycle nutrients, decompose organic matter making nutrient available for plant uptake, stabilize humus, and form soil particles.<br />The extent of the diversity of microbial in soil is seen to be critical to the maintenance of soil health and quality, as a wide range of microbial is involved in important soil functions.  That ecologically managed soils have a greater quantity and diversity of soil microbial. The two main drivers of soil microbial community structure, i.e., plant type and soil type, are thought to exert their function in a complex manner. The fact that in some situations the soil and in others the plant type is the key factor determining soil microbial diversity is related to their complexity of the microbial interactions in soil, including interactions between microbial and soil and microbial and plants. <br />The basic premise of organic soil stewardship is that all plant nutrients are present in the soil by maintaining a biologically active soil environment. The diversity of microbial communities has on ecological function and resilience to disturbances in soil ecosystems. Relationships are often observed between the extent of microbial diversity in soil, soil and plant quality and ecosystem sustainability. Agricultural management can be directed toward maximizing the quality of the soil microbial community in terms of disease suppression, if it is possible to shift soil microbial communities.</p><p>Keywords: structure, microbial, implication, sustainable agriculture<br /><br /></p>

scholarly journals Metagenomic Analysis of Microbial Diversity in Landfill Lysimeter Soil of Ghazipur Landfill Site, New Delhi, India

2017 ◽  
Vol 5 (42) ◽  
Author(s):  
Juhi Gupta ◽  
Rashmi Rathour ◽  
Madan Kumar ◽  
Indu Shekhar Thakur
Keyword(s):  
Microbial Diversity ◽  
Microbial Communities ◽  
Landfill Site ◽  
Metagenomic Analysis ◽  
New Delhi ◽  
Soil Microbial Diversity ◽  
Soil Microbial ◽  
Functional Aspects ◽  
Waste Degradation ◽  
Metagenomic Approach

ABSTRACT We report the soil microbial diversity and functional aspects related to degradation of recalcitrant compounds, determined using a metagenomic approach, in a landfill lysimeter prepared with soil from Ghazipur landfill site, New Delhi, India. Metagenomic analysis revealed the presence and functional diversity of complex microbial communities responsible for waste degradation.

Revealing the spatial pattern of subsurface soil salinity over the Argentinean Dry Chaco

2021 ◽  
Author(s):  
Michiel Maertens ◽  
Veerle Vanacker ◽  
Gabriëlle De Lannoy ◽  
Frederike Vincent ◽  
Raul Giménez ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Soil Salinization ◽  
Dry Forest ◽  
Surface Model ◽  
Subsurface Soil

&lt;p&gt;The South-American Dry Chaco is a unique ecoregion as it is one of the largest sedimentary plains in the world hosting the planet&amp;#8217;s largest dry forest. The 787.000 km&amp;#178; region covers parts of Argentina, Paraguay, and Bolivia and is characterized by a negative climatic water balance as a consequence of limited rainfall inputs (800 mm/year) and high temperatures (21&amp;#176;C). In combination with the region&amp;#8217;s extreme flat topography (slopes &lt; 0.1%) and shallow groundwater tables, saline soils are expected in substantial parts of the region. In addition, it is expected that large-scale deforestation processes disrupt the hydrological cycle resulting in rising groundwater tables and further increase the risk for soil salinization.&lt;/p&gt;&lt;p&gt;In this study, we identified the regional-scale patterns of subsurface soil salinity in the Dry Chaco. &amp;#160;Field data were obtained during a two-month field campaign in the dry season of 2019. A total of 492 surface- and 142 subsurface-samples were collected along East-West transects to determine soil electric conductivity, pH, bulk density and humidity. Spatial regression techniques were used to reveal the topographic and ecohydrological variables that are associated with subsurface soil salinity over the Dry Chaco. The hydrological information was obtained from a state-of-the-art land surface model with an improved set of satellite-derived vegetation and land cover parameters.&lt;/p&gt;&lt;p&gt;In the presentation, we will present a subsurface soil salinity map for a part of the Argentinean Dry Chaco and provide relevant insights into the driving mechanisms behind it.&lt;/p&gt;

scholarly journals Functional metagenomic analysis of dust-associated microbiomes above the Red Sea

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nojood A. Aalismail ◽  
David K. Ngugi ◽  
Rubén Díaz-Rúa ◽  
Intikhab Alam ◽  
Michael Cusack ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Dna Sequences ◽  
Biofilm Formation ◽  
Atmospheric Transport ◽  
Taxonomic Diversity ◽  
Functional Metagenomics ◽  
Genetic Traits ◽  
Gene Catalogue ◽  
Range Transport ◽  
Genes Encoding

Abstract Atmospheric transport is a major vector for the long-range transport of microbial communities, maintaining connectivity among them and delivering functionally important microbes, such as pathogens. Though the taxonomic diversity of aeolian microorganisms is well characterized, the genomic functional traits underpinning their survival during atmospheric transport are poorly characterized. Here we use functional metagenomics of dust samples collected on the Global Dust Belt to initiate a Gene Catalogue of Aeolian Microbiome (GCAM) and explore microbial genetic traits enabling a successful aeolian lifestyle in Aeolian microbial communities. The GCAM reported here, derived from ten aeolian microbial metagenomes, includes a total of 2,370,956 non-redundant coding DNA sequences, corresponding to a yield of ~31 × 106 predicted genes per Tera base-pair of DNA sequenced for the aeolian samples sequenced. Two-thirds of the cataloged genes were assigned to bacteria, followed by eukaryotes (5.4%), archaea (1.1%), and viruses (0.69%). Genes encoding proteins involved in repairing UV-induced DNA damage and aerosolization of cells were ubiquitous across samples, and appear as fundamental requirements for the aeolian lifestyle, while genes coding for other important functions supporting the aeolian lifestyle (chemotaxis, aerotaxis, germination, thermal resistance, sporulation, and biofilm formation) varied among the communities sampled.

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