scholarly journals PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure

mSphere ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter Rubbens ◽  
Ruben Props ◽  
Frederiek-Maarten Kerckhof ◽  
Nico Boon ◽  
Willem Waegeman
Keyword(s):  
Flow Cytometry ◽  
Community Structure ◽  
Microbial Community ◽  
Microbial Community Structure ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Community Diversity ◽  
Data Sets ◽  
Natural Ecosystems ◽  
Flow Cytometry Data

ABSTRACT Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics. IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

scholarly journals PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

2019 ◽  
Author(s):  
Peter Rubbens ◽  
Ruben Props ◽  
Frederiek-Maarten Kerckhof ◽  
Nico Boon ◽  
Willem Waegeman
Keyword(s):  
Flow Cytometry ◽  
Microbial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Diversity ◽  
Amplicon Sequencing ◽  
Gaussian Mixture ◽  
Community Diversity ◽  
Rrna Gene ◽  
Flow Cytometry Data

AbstractMicrobial flow cytometry allows to rapidly characterize microbial communities. Recent research has demonstrated a moderate to strong connection between the cytometric diversity and taxonomic diversity based on 16S rRNA gene amplicon sequencing data. This creates the opportunity to integrate both types of data to study and predict the microbial community diversity in an automated and efficient way. However, microbial flow cytometry data results in a number of unique challenges that need to be addressed. The results of our work are threefold: i) We expand current microbial cytometry fingerprinting approaches by proposing and validating a model-based fingerprinting approach based upon Gaussian Mixture Models, which we called PhenoGMM. ii) We show that microbial diversity can be rapidly estimated by PhenoGMM. In combination with a supervised machine learning model, diversity estimations based on 16S rRNA gene amplicon sequencing data can be predicted. iii) We evaluate our method extensively by using multiple datasets from different ecosystems and compare its predictive power with a generic binning fingerprinting approach that is commonly used in microbial flow cytometry. These results demonstrate the strong connection between the genetic make-up of a microbial community and its phenotypic properties as measured by flow cytometry. Our workflow facilitates the study of microbial diversity and community dynamics using flow cytometry in a fast and quantitative way.ImportanceMicroorganisms are vital components in various ecoystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technique to characterize microbial community diversity and dynamics. It is an optical technique, able to rapidly characterize a number of phenotypic properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian Mixture Models. When samples have been analyzed by both flow cytometry and 16S rRNA gene amplicon sequencing, we show that supervised machine learning models can be used to find the relationship between the two types of data. We evaluate our workflow on datasets from different ecosystems, illustrating its general applicability for the analysisof microbial flow cytometry data. PhenoGMM facilitates the rapid characterization and predictive modelling of microbial diversity using flow cytometry.

scholarly journals Interaction between typical sulfonamides and bacterial diversity in drinking water

2018 ◽  
Vol 16 (6) ◽  
pp. 914-920 ◽  
Author(s):  
Qing Wu ◽  
Shuqun Li ◽  
Xiaofei Zhao ◽  
Xinhua Zhao
Keyword(s):  
Drinking Water ◽  
Community Structure ◽  
Microbial Community ◽  
Bacterial Community ◽  
Bacterial Diversity ◽  
Microbial Community Structure ◽  
Antibiotic Use ◽  
Community Diversity ◽  
Wide Range ◽  
Growth Of Bacteria

Abstract The abuse of antibiotics is becoming more serious as antibiotic use has increased. The sulfa antibiotics, sulfamerazine (SM1) and sulfamethoxazole (SMZ), are frequently detected in a wide range of environments. The interaction between SM1/SMZ and bacterial diversity in drinking water was investigated in this study. The results showed that after treatment with SM1 or SMZ at four different concentrations, the microbial community structure of the drinking water changed statistically significantly compared to the blank sample. At the genus level, the proportions of the different bacteria in drinking water may affect the degradation of the SM1/SMZ. The growth of bacteria in drinking water can be inhibited after the addition of SM1/SMZ, and bacterial community diversity in drinking water declined in this study. Furthermore, the resistance gene sul2 was induced by SM1 in the drinking water.

scholarly journals PLFA analysis of the microbial community structure at the points of the temporary localization of radioactive waste

2020 ◽  
Vol 26 ◽  
pp. 149-153
Author(s):  
Yu. V. Ruban ◽  
K. E. Shavanova ◽  
V. V. Illenko ◽  
K. D. Korepanova ◽  
D. O. Samofalova ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Radioactive Waste ◽  
Carbon Content ◽  
Microbial Community Structure ◽  
Lipid Fraction ◽  
Soil Microflora ◽  
Natural Ecosystems ◽  
Dominant Group ◽  
Erica Tool

Aim. PTLRW are the trenches and bursts for the localization of radioactive waste that were created during the first priority measures for elimination of the Chornobyl accident. The aim of the presented work was to characterize the microbial community structure on PTLRW. Methods. To describe the influence of environmental factors on the soil microflora, the agrochemical parameters of soil (pH, carbon, nitrogen, mobile potassium and phosphorus) were evaluated. Dose loading was calculated using the ERICA tool software package. The total lipid fraction was extracted with a modified Bligh-Dyer method. Results. The pH of the soil ranged from 3.0 to 3.9. The carbon content ranged from 0.95% to 2.11%. The exception was Red Forest from the trench/outside the trench where the carbon content was 2.52 and 1.98% and with a pH 4.5. Nitrogen content ranged from 33.6 mg / kg to 74.2 mg / kg. The PLFA content ranged from 15 μg / g to 18.9 μg / g, except Novoshepelychi and Zalissia (33.3 μg / g and 23 μg / g). Conclusions. In terms of the structural composition of the microorganisms, the PTLRW points were more homogeneous compared to the contaminated radionuclide ecosystems. In natural ecosystems, gram-positive bacteria were the main dominant group, unlike PTLRW where there were several groups. Keywords: PTLRW, microbial community structure, PLFA, biomarkers, ERICA tool.

scholarly journals Theoretical and Simulation-Based Investigation of the Relationship between Sequencing Effort, Microbial Community Richness, and Diversity in Binning Metagenome-Assembled Genomes

mSystems ◽  
2019 ◽  
Vol 4 (5) ◽  
Author(s):  
Taylor M. Royalty ◽  
Andrew D. Steen
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Genome Size ◽  
Relative Abundance ◽  
Microbial Community Structure ◽  
Data Sets ◽  
Short Read ◽  
Coupon Collector ◽  
Simulation Based ◽  
The Relationship

ABSTRACT We applied theoretical and simulation-based approaches to characterize how microbial community structure influences the amount of sequencing effort to reconstruct metagenomes that are assembled from short-read sequences. First, a coupon collector equation was proposed as an analytical model for predicting sequencing effort as a function of microbial community structure. Characterization was performed by varying community structure properties such as richness, evenness, and genome size. Simulations demonstrated that while community richness and evenness influenced the sequencing effort required to sequence a community metagenome to exhaustion, the effort necessary to sequence an individual genome to a target fraction of exhaustion depended only on the relative abundance of the genome and its genome size. A second analysis evaluated the quantity, completion, and contamination of metagenome-assembled genomes (MAGs) as a function of sequencing effort on four preexisting sequence read data sets from different environments. These data sets were subsampled to various degrees of completeness to simulate the effect of sequencing effort on MAG retrieval. Modeling suggested that sequencing efforts beyond what is typical in published experiments (1 to 10 Gbp) would generate diminishing returns in terms of MAG binning. A software tool, Genome Relative Abundance to Sequencing Effort (GRASE), was created to assist investigators to further explore this relationship. Reevaluation of the relationship between sequencing effort and binning success in the context of genome relative abundance, as opposed to base pairs, provides a constraint on sequencing experiments based on the relative abundance of microbes in an environment rather than arbitrary levels of sequencing effort. IMPORTANCE Short-read sequencing with Illumina sequencing technology provides an accurate, high-throughput method for characterizing the metabolic potential of microbial communities. Short-read sequences can be assembled and binned into metagenome-assembled genomes, thus shedding light on the function of microbial ecosystems that are important for health, agriculture, and Earth system processes. The work presented here provides an analytical framework for selecting sequencing effort as a function of genome relative abundance. As such, experimental goals in metagenome-assembled genome creation projects can select sequencing effort based on the rarest target genome as a constrained threshold. We hope that the results presented here, as well as GRASE, will be valuable to researchers planning sequencing experiments.

scholarly journals FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Wade T. Rogers ◽  
Herbert A. Holyst
Keyword(s):  
Flow Cytometry ◽  
Gaussian Mixture ◽  
Empirical Modeling ◽  
Data Sets ◽  
Automated Classification ◽  
Data Quality Control ◽  
Computationally Efficient ◽  
Flow Cytometry Data ◽  
Flow Cytometric Data ◽  
Modeling Software

A new software package called flowFP for the analysis of flow cytometry data is introduced. The package, which is tightly integrated with other Bioconductor software for analysis of flow cytometry, provides tools to transform raw flow cytometry data into a form suitable for direct input into conventional statistical analysis and empirical modeling software tools. The approach of flowFP is to generate a description of the multivariate probability distribution function of flow cytometry data in the form of a “fingerprint.” As such, it is independent of a presumptive functional form for the distribution, in contrast with model-based methods such as Gaussian Mixture Modeling. FlowFP is computationally efficient and able to handle extremely large flow cytometry data sets of arbitrary dimensionality. Algorithms and software implementation of the package are described. Use of the software is exemplified with applications to data quality control and to the automated classification of Acute Myeloid Leukemia.

scholarly journals Changes in the Microbial Community Diversity of Oil Exploitation

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 556 ◽  
Author(s):  
Jingjing Liu ◽  
Jing Wu ◽  
Jiawei Lin ◽  
Jian Zhao ◽  
Tianyi Xu ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Environmental Factors ◽  
Oil Recovery ◽  
Microbial Community Structure ◽  
Storage Tank ◽  
Oil Production ◽  
Operational Taxonomic Unit ◽  
Community Diversity ◽  
Microbial Resources

To systematically evaluate the ecological changes of an active offshore petroleum production system, the variation of microbial communities at several sites (virgin field, wellhead, storage tank) of an oil production facility in east China was investigated by sequencing the V3 to V4 regions of 16S ribosomal ribonucleic acid (rRNA) of microorganisms. In general, a decrease of microbial community richness and diversity in petroleum mining was observed, as measured by operational taxonomic unit (OTU) numbers, α (Chao1 and Shannon indices), and β (principal coordinate analysis) diversity. Microbial community structure was strongly affected by environmental factors at the phylum and genus levels. At the phylum level, virgin field and wellhead were dominated by Proteobacteria, while the storage tank had higher presence of Firmicutes (29.3–66.9%). Specifically, the wellhead displayed a lower presentence of Proteobacteria (48.6–53.4.0%) and a higher presence of Firmicutes (24.4–29.6%) than the virgin field. At the genus level, the predominant genera were Ochrobactrum and Acinetobacter in the virgin field, Lactococcus and Pseudomonas in the wellhead, and Prauseria and Bacillus in the storage tank. Our study revealed that the microbial community structure was strongly affected by the surrounding environmental factors, such as temperature, oxygen content, salinity, and pH, which could be altered because of the oil production. It was observed that the various microbiomes produced surfactants, transforming the biohazard and degrading hydro-carbon. Altering the microbiome growth condition by appropriate human intervention and taking advantage of natural microbial resources can further enhance oil recovery technology.

scholarly journals Microbial community diversity patterns are related to physical and chemical differences among temperate lakes near Beaver Island, MI

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3937 ◽  
Author(s):  
Miranda H. Hengy ◽  
Dean J. Horton ◽  
Donald G. Uzarski ◽  
Deric R. Learman
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Community Structure ◽  
Bottom Water ◽  
Community Diversity ◽  
Freshwater Lakes ◽  
Microbial Community Diversity ◽  
Physical And Chemical ◽  
Beaver Island

Lakes are dynamic and complex ecosystems that can be influenced by physical, chemical, and biological processes. Additionally, individual lakes are often chemically and physically distinct, even within the same geographic region. Here we show that differences in physicochemical conditions among freshwater lakes located on (and around) the same island, as well as within the water column of each lake, are significantly related to aquatic microbial community diversity. Water samples were collected over time from the surface and bottom-water within four freshwater lakes located around Beaver Island, MI within the Laurentian Great Lakes region. Three of the sampled lakes experienced seasonal lake mixing events, impacting either O2, pH, temperature, or a combination of the three. Microbial community alpha and beta diversity were assessed and individual microbial taxa were identified via high-throughput sequencing of the 16S rRNA gene. Results demonstrated that physical and chemical variability (temperature, dissolved oxygen, and pH) were significantly related to divergence in the beta diversity of surface and bottom-water microbial communities. Despite its correlation to microbial community structure in unconstrained analyses, constrained analyses demonstrated that dissolved organic carbon (DOC) concentration was not strongly related to microbial community structure among or within lakes. Additionally, several taxa were correlated (either positively or negatively) to environmental variables, which could be related to aerobic and anaerobic metabolisms. This study highlights the measurable relationships between environmental conditions and microbial communities within freshwater temperate lakes around the same island.

scholarly journals Methane related changes in prokaryotic activity along geochemical profiles in sediments of Lake Kinneret (Israel)

2014 ◽  
Vol 11 (6) ◽  
pp. 9813-9852 ◽  
Author(s):  
I. Bar Or ◽  
E. Ben-Dov ◽  
A. Kushmaro ◽  
W. Eckert ◽  
O. Sivan
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
16S Rrna ◽  
Microbial Community Structure ◽  
Iron Reduction ◽  
Lake Kinneret ◽  
Community Diversity ◽  
Rrna Gene ◽  
Anaerobic Oxidation Of Methane ◽  
Deep Sediments

Abstract. Microbial methane oxidation process (methanotrophy) is the primary control on the emission of the greenhouse gas methane (CH4) to the atmosphere. In terrestrial environments, aerobic methanotrophic bacteria are mainly responsible for oxidizing the methane. In marine sediments the coupling of the anaerobic oxidation of methane (AOM) with sulfate reduction, often by a consortium of anaerobic methanotrophic archaea (ANME) and sulfate reducing bacteria, was found to consume almost all the upward diffusing methane. Recently, we showed geochemical evidence for AOM driven by iron reduction in Lake Kinneret (LK) (Israel) deep sediments and suggested that this process can be an important global methane sink. The goal of the present study was to link the geochemical gradients found in the porewater (chemical and isotope profiles) with possible changes in microbial community structure. Specifically, we examined the possible shift in the microbial community in the deep iron-driven AOM zone and its similarity to known sulfate driven AOM populations. Screening of archaeal 16S rRNA gene sequences revealed Thaumarchaeota and Euryarchaeota as the dominant phyla in the sediment. Thaumarchaeota, which belongs to the family of copper containing membrane-bound monooxgenases, increased with depth while Euryarchaeota decreased. This may indicate the involvement of Thaumarchaeota, which were discovered to be ammonia oxidizers but whose activity could also be linked to methane, in AOM in the deep sediment. ANMEs sequences were not found in the clone libraries, suggesting that iron-driven AOM is not through sulfate. Bacterial 16S rRNA sequences displayed shifts in community diversity with depth. Proteobacteria and Chloroflexi increased with depth, which could be connected with their different dissimilatory anaerobic processes. The observed changes in microbial community structure suggest possible direct and indirect mechanisms for iron-driven AOM in deep sediments.

Cyanobacteria and Photosynthetic Species as Part of the Microbial Community Structure of Biofilms in Copahue Geothermal Springs (Neuquén, Argentina)

2013 ◽  
Vol 825 ◽  
pp. 11-14
Author(s):  
María Sofía Urbieta ◽  
Elena González Toril ◽  
Ángeles Aguilera ◽  
Maria Alejandra Giaveno ◽  
Edgardo Donati ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Microbial Community Structure ◽  
Water Samples ◽  
Alternative Energy ◽  
Hot Springs ◽  
Extreme Temperature ◽  
Community Diversity ◽  
Thermal Activity ◽  
Ph Conditions

Copahue is a geothermal field located in the Northwest corner of Neuquén province in Argentina. It is dominated by the still active Copahue volcano. In the area there are many acidic pools, hot springs and solfataras with different temperature and pH conditions that influence their microbial diversity. On the surrounding rocks and the borders of the pools, where water movements and thermal activity are less intense, many biofilms can be found. They have different aspects and structure, and they present less extreme temperature and pH conditions than the ponds and hot springs. Biofilms are a different ecological niche and they have different microbial community structure. In this study carried out by molecular ecology techniques, mainly 16S and 18S rRNA sequencing, we report a strong presence of cyanobacterias, cloroflexi and eukaryotes, not detected in previous biodiversity studies done on water samples. Almost no acidophilic bacteria were found, with the exception of members of genusThiomonas, also found in the acidic pools. Archaea were detected only in one of the biofilms and the structure of that community seems to be similar to those found in water samples, with many uncultured species mainly related to orderSulfolobales. The aim of this study is to assess microbial community diversity in the biofilms present in this acidic geothermal area, with particular emphasis on detection of cyanobacterias and eukaryotes with potential biotechnological applications like production of alternative energy sources, synthesis and accumulation of biomolecules with antiviral or antibiotic activities or potential ability to bioremediate contaminated areas.

scholarly journals Merging Mixture Components for Cell Population Identification in Flow Cytometry

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Greg Finak ◽  
Ali Bashashati ◽  
Ryan Brinkman ◽  
Raphaël Gottardo
Keyword(s):  
Flow Cytometry ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Model Fit ◽  
Flow Cytometry Data ◽  
Bioconductor Project ◽  
Distinct Cell ◽  
Cell Subpopulations ◽  
Fcm Analysis ◽  
Selection Of

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project.

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