scholarly journals Identification and Metabolism of Naturally Prevailing Microorganisms in Zinc and Copper Mineral Processing

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 156
Author(s):  
Hanna Miettinen ◽  
Malin Bomberg ◽  
Thi Minh Khanh Le ◽  
Päivi Kinnunen
Keyword(s):  
Microbial Communities ◽  
Amplicon Sequencing ◽  
Heavy Metal Resistance ◽  
Extraction Methods ◽  
Metal Resistance ◽  
Marker Genes ◽  
Mineral Particle ◽  
Core Microbiome ◽  
Copper Mineral ◽  
Zinc Plant

It has only recently been discovered that naturally prevailing microorganisms have a notable role in flotation in addition to chemical process parameters and overall water quality. This study’s aim was to assess the prevailing microbial communities in relation to process chemistry in a zinc and copper mineral flotation plant. Due to the limitations of cultivation-based microbial methods that detect only a fraction of the total microbial diversity, DNA-based methods were utilised. However, it was discovered that the DNA extraction methods need to be improved for these environments with high mineral particle content. Microbial communities and metabolism were studied with quantitative PCR and amplicon sequencing of bacterial, archaeal and fungal marker genes and shotgun sequencing. Bacteria dominated the microbial communities, but in addition, both archaea and fungi were present. The predominant bacterial metabolism included versatile sulfur compound oxidation. Putative Thiovirga sp. dominated in the zinc plant and the water circuit samples, whereas Thiobacillus spp. dominated the copper plant. Halothiobacillus spp. were also an apparent part of the community in all samples. Nitrogen metabolism was more related to assimilatory than dissimilatory nitrate and nitrite oxidation/reduction reactions. Abundance of heavy metal resistance genes emphasized the adaptation and competitive edge of the core microbiome in these extreme conditions compared to microorganisms freshly entering the process.

scholarly journals Hill-based Dissimilarity Indices and Null Models for Analysis of Microbial Community Assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liebana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Sasso Pisano Geothermal Field Environment Harbours Diverse Ktedonobacteria Representatives and Illustrates Habitat-Specific Adaptations

2021 ◽  
Vol 9 (7) ◽  
pp. 1402
Author(s):  
Sania Arif ◽  
Corinna Willenberg ◽  
Annika Dreyer ◽  
Heiko Nacke ◽  
Michael Hoppert
Keyword(s):  
Hot Springs ◽  
Hot Spring ◽  
Amplicon Sequencing ◽  
Heavy Metal Resistance ◽  
18S Rrna Gene ◽  
Metal Resistance ◽  
Geothermal Field ◽  
Rrna Gene ◽  
Metabolic Potential ◽  
Eukaryotic Diversity

The hydrothermal steam environment of Sasso Pisano (Italy) was selected to investigate the associated microbial community and its metabolic potential. In this context, 16S and 18S rRNA gene partial sequences of thermophilic prokaryotes and eukaryotes inhabiting hot springs and fumaroles as well as mesophilic microbes colonising soil and water were analysed by high-throughput amplicon sequencing. The eukaryotic and prokaryotic communities from hot environments clearly differ from reference microbial communities of colder soil sites, though Ktedonobacteria showed high abundances in various hot spring samples and a few soil samples. This indicates that the hydrothermal steam environments of Sasso Pisano represent not only a vast reservoir of thermophilic but also mesophilic members of this Chloroflexi class. Metabolic functional profiling revealed that the hot spring microbiome exhibits a higher capability to utilise methane and aromatic compounds and is more diverse in its sulphur and nitrogen metabolism than the mesophilic soil microbial consortium. In addition, heavy metal resistance-conferring genes were significantly more abundant in the hot spring microbiome. The eukaryotic diversity at a fumarole indicated high abundances of primary producers (unicellular red algae: Cyanidiales), consumers (Arthropoda: Collembola sp.), and endoparasite Apicomplexa (Gregarina sp.), which helps to hypothesise a simplified food web at this hot and extremely nutrient-deprived acidic environment.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Analysis of soil microbial communities based on amplicon sequencing of marker genes

2017 ◽  
Vol 53 (5) ◽  
pp. 485-489 ◽  
Author(s):  
Anne Schöler ◽  
Samuel Jacquiod ◽  
Gisle Vestergaard ◽  
Stefanie Schulz ◽  
Michael Schloter
Keyword(s):  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Amplicon Sequencing ◽  
Marker Genes ◽  
Soil Microbial

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

Microbiome ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models. Results Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems. Conclusions Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Sabeh-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Comparative Genomic Analysis of 450 Strains of Salmonella enterica Isolated from Diseased Animals

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1025
Author(s):  
Shaohua Zhao ◽  
Cong Li ◽  
Chih-Hao Hsu ◽  
Gregory H. Tyson ◽  
Errol Strain ◽  
...  
Keyword(s):  
Resistance Genes ◽  
Bacterial Infections ◽  
Genomic Analysis ◽  
Heavy Metal Resistance ◽  
Metal Resistance ◽  
Comparative Genomic ◽  
Salmonella Infections ◽  
Antimicrobial Resistance Genes ◽  
Salmonella Pathogenicity Islands ◽  
Shed Light

Salmonella is a leading cause of bacterial infections in animals and humans. We sequenced a collection of 450 Salmonella strains from diseased animals to better understand the genetic makeup of their virulence and resistance features. The presence of Salmonella pathogenicity islands (SPIs) varied by serotype. S. Enteritidis carried the most SPIs (n = 15), while S. Mbandaka, S. Cerro, S. Meleagridis, and S. Havana carried the least (n = 10). S. Typhimurium, S. Choleraesuis, S. I 4,5,12:i:-, and S. Enteritidis each contained the spv operon on IncFII or IncFII-IncFIB hybrid plasmids. Two S. IIIa carried a spv operon with spvD deletion on the chromosome. Twelve plasmid types including 24 hybrid plasmids were identified. IncA/C was frequently associated with S. Newport (83%) and S. Agona (100%) from bovine, whereas IncFII (100%), IncFIB (100%), and IncQ1 (94%) were seen in S. Choleraesuis from swine. IncX (100%) was detected in all S. Kentucky from chicken. A total of 60 antimicrobial resistance genes (ARGs), four disinfectant resistances genes (DRGs) and 33 heavy metal resistance genes (HMRGs) were identified. The Salmonella strains from sick animals contained various SPIs, resistance genes and plasmid types based on the serotype and source of the isolates. Such complicated genomic structures shed light on the strain characteristics contributing to the severity of disease and treatment failures in Salmonella infections, including those causing illnesses in animals.

scholarly journals Heavy Metal Tolerance Trend in Extended-Spectrum β-Lactamase Encoding Strains Recovered from Food Samples

2021 ◽  
Vol 18 (9) ◽  
pp. 4718
Author(s):  
Kashaf Junaid ◽  
Hasan Ejaz ◽  
Iram Asim ◽  
Sonia Younas ◽  
Humaira Yasmeen ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Tolerance ◽  
Heavy Metal Tolerance ◽  
Heavy Metal Resistance ◽  
Metal Resistance ◽  
Food Samples ◽  
Esbl Production ◽  
Extended Spectrum ◽  
Retail Food ◽  
Esbl Producers

This study evaluates bacteriological profiles in ready-to-eat (RTE) foods and assesses antibiotic resistance, extended-spectrum β-lactamase (ESBL) production by gram-negative bacteria, and heavy metal tolerance. In total, 436 retail food samples were collected and cultured. The isolates were screened for ESBL production and molecular detection of ESBL-encoding genes. Furthermore, all isolates were evaluated for heavy metal tolerance. From 352 culture-positive samples, 406 g-negative bacteria were identified. Raw food samples were more often contaminated than refined food (84.71% vs. 76.32%). The predominant isolates were Klebsiella pneumoniae (n = 76), Enterobacter cloacae (n = 58), and Escherichia coli (n = 56). Overall, the percentage of ESBL producers was higher in raw food samples, although higher occurrences of ESBL-producing E. coli (p = 0.01) and Pseudomonas aeruginosa (p = 0.02) were observed in processed food samples. However, the prevalence of ESBL-producing Citrobacter freundii in raw food samples was high (p = 0.03). Among the isolates, 55% were blaCTX-M, 26% were blaSHV, and 19% were blaTEM. Notably, heavy metal resistance was highly prevalent in ESBL producers. These findings demonstrate that retail food samples are exposed to contaminants including antibiotics and heavy metals, endangering consumers.

scholarly journals Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kazutoshi Yoshitake ◽  
Gaku Kimura ◽  
Tomoko Sakami ◽  
Tsuyoshi Watanabe ◽  
Yukiko Taniuchi ◽  
...  
Keyword(s):  
Time Series ◽  
Microbial Communities ◽  
Pacific Coast ◽  
Three Dimensional ◽  
Amplicon Sequencing ◽  
Metagenomic Data ◽  
Shotgun Metagenomics ◽  
Functional Features ◽  
Pacific Coast Of Japan ◽  
Marine Microbial Communities

AbstractAlthough numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data (http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

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