Reduced metagenome sequencing for strain-resolution taxonomic profiles

Abstract Background Studies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction-associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or not explore the full potential of RMS data. Results We suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock community datasets show the potential to clearly separate strains even when the 16S is 100% identical, and genome-wide differences is < 0.02, indicating RMS has a very high resolution. From a simulation study, we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real dataset of infant guts, we show that RMS is capable of detecting a strain diversity gradient for Escherichia coli across time. Conclusion We find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain level. Like shotgun metagenomics, it requires a good database of reference genomes and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS.

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Reduced Metagenome Sequencing for strain-resolution taxonomic profiles

10.21203/rs.3.rs-44151/v3 ◽

2021 ◽

Author(s):

Lars Snipen ◽

Inga-Leena Angell ◽

Torbjørn Rognes ◽

Knut Rudi

Keyword(s):

Microbial Community ◽

Microbial Community Composition ◽

Real Data ◽

Shotgun Sequencing ◽

Least Square ◽

Full Potential ◽

Data Set ◽

Shotgun Metagenomics ◽

Metagenome Sequencing ◽

Reference Genomes

Abstract BackgroundStudies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution, and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or do not explore the full potential of RMS data.ResultsWe suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock-community data sets shows the potential to clearly separate between strains even when the 16S is 100% identical and genome-wide differences is <0.02, indicating RMS has a very high resolution. From a simulation study we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real data set of infants guts we show that RMS is capable of detecting a strain-diversity gradient for Escherichia coli across time.ConclusionWe find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain-level. Like shotgun metagenomics, it requires a good database of reference genomes, and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS.

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Reduced Metagenome Sequencing for strain-resolution taxonomic proles

10.21203/rs.3.rs-44151/v1 ◽

2020 ◽

Author(s):

Lars Snipen ◽

Inga-Leena Angell ◽

Torbjørn Rognes ◽

Knut Rudi

Keyword(s):

Microbial Community ◽

Microbial Community Composition ◽

Real Data ◽

Shotgun Sequencing ◽

Least Square ◽

Full Potential ◽

Data Set ◽

Shotgun Metagenomics ◽

Metagenome Sequencing ◽

Reference Genomes

Abstract Background: Studies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution, and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or do not explore the full potential of RMS data.Results: We suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock-community data sets shows the potential to clearly separate between strains even when the 16S is 100% identical and genome-wide dierences is < 0:02, indicating RMS has a very high resolution. From a simulation study we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real data set of infants guts we show that RMS is capable of detecting a strain-diversity gradient for Escherichia coli across time.Conclusion: We find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain-level. Like shotgun metagenomics, it requires a good database of reference genomes, and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS.

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Reduced Metagenome Sequencing for strain-resolution taxonomic proles

10.21203/rs.3.rs-44151/v2 ◽

2020 ◽

Author(s):

Lars Snipen ◽

Inga-Leena Angell ◽

Torbjørn Rognes ◽

Knut Rudi

Keyword(s):

Microbial Community ◽

Microbial Community Composition ◽

Real Data ◽

Shotgun Sequencing ◽

Least Square ◽

Full Potential ◽

Data Set ◽

Shotgun Metagenomics ◽

Metagenome Sequencing ◽

Reference Genomes

Abstract Background: Studies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution, and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or do not explore the full potential of RMS data. Results: We suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock-community data sets shows the potential to clearly separate between strains even when the 16S is 100% identical and genome-wide dierences is < 0:02, indicating RMS has a very high resolution. From a simulation study we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real data set of infants guts we show that RMS is capable of detecting a strain-diversity gradient for Escherichia coli across time. Conclusion: We find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain-level. Like shotgun metagenomics, it requires a good database of reference genomes, and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS. Keywords: metagenome; strains; ddRADseq

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Host-associated microbe PCR (hamPCR): accessing new biology through convenient measurement of both microbial load and community composition

10.1101/2020.05.19.103937 ◽

2020 ◽

Cited By ~ 1

Author(s):

Derek S. Lundberg ◽

Pratchaya Pramoj Na Ayutthaya ◽

Annett Strauß ◽

Gautam Shirsekar ◽

Wen-Sui Lo ◽

...

Keyword(s):

Microbial Community ◽

Community Composition ◽

Compositional Data ◽

Microbial Community Composition ◽

Cost Effective ◽

Amplicon Sequencing ◽

Microbial Load ◽

Rrna Gene ◽

Metagenome Sequencing ◽

Transformative Approach

AbstractThe ratio of microbial population size relative to the amount of host tissue, or “microbial load”, is a fundamental metric of colonization and infection, but it cannot be directly deduced from microbial amplicon data such as 16S rRNA gene counts. Because conventional methods to determine load, such as serial dilution plating or quantitative PCR, add substantial experimental burden, they are only rarely paired with amplicon sequencing. Alternatively, whole metagenome sequencing of DNA contributed by host and microbes both reveals microbial community composition and enables determination of microbial load, but host DNA typically greatly outweighs microbial DNA, severely limiting the cost-effectiveness and scalability of this approach. We introduce host-associated microbe PCR (hamPCR), a robust amplicon sequencing strategy to quantify microbial load and describe interkingdom microbial community composition in a single, cost-effective library. We demonstrate its accuracy and flexibility across multiple host and microbe systems, including nematodes and major crops. We further present a technique that can be used, prior to sequencing, to optimize the host representation in a batch of libraries without loss of information. Because of its simplicity, and the fact that it provides an experimental solution to the well-known statistical challenges provided by compositional data, hamPCR will become a transformative approach throughout culture-independent microbiology.

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Do initial concentration and activated sludge seasonality affect pharmaceutical biotransformation rate constants?

Applied Microbiology and Biotechnology ◽

10.1007/s00253-021-11475-9 ◽

2021 ◽

Author(s):

Tamara J. H. M. van Bergen ◽

Ana B. Rios-Miguel ◽

Tom M. Nolte ◽

Ad M. J. Ragas ◽

Rosalie van Zelm ◽

...

Keyword(s):

Microbial Community ◽

Activated Sludge ◽

Community Composition ◽

Rate Constants ◽

Wastewater Treatment Plants ◽

Microbial Community Composition ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Initial Concentration ◽

Different Seasons

Abstract Pharmaceuticals find their way to the aquatic environment via wastewater treatment plants (WWTPs). Biotransformation plays an important role in mitigating environmental risks; however, a mechanistic understanding of involved processes is limited. The aim of this study was to evaluate potential relationships between first-order biotransformation rate constants (kb) of nine pharmaceuticals and initial concentration of the selected compounds, and sampling season of the used activated sludge inocula. Four-day bottle experiments were performed with activated sludge from WWTP Groesbeek (The Netherlands) of two different seasons, summer and winter, spiked with two environmentally relevant concentrations (3 and 30 nM) of pharmaceuticals. Concentrations of the compounds were measured by LC–MS/MS, microbial community composition was assessed by 16S rRNA gene amplicon sequencing, and kb values were calculated. The biodegradable pharmaceuticals were acetaminophen, metformin, metoprolol, terbutaline, and phenazone (ranked from high to low biotransformation rates). Carbamazepine, diatrizoic acid, diclofenac, and fluoxetine were not converted. Summer and winter inocula did not show significant differences in microbial community composition, but resulted in a slightly different kb for some pharmaceuticals. Likely microbial activity was responsible instead of community composition. In the same inoculum, different kb values were measured, depending on initial concentration. In general, biodegradable compounds had a higher kb when the initial concentration was higher. This demonstrates that Michealis-Menten kinetic theory has shortcomings for some pharmaceuticals at low, environmentally relevant concentrations and that the pharmaceutical concentration should be taken into account when measuring the kb in order to reliably predict the fate of pharmaceuticals in the WWTP. Key points • Biotransformation and sorption of pharmaceuticals were assessed in activated sludge. • Higher initial concentrations resulted in higher biotransformation rate constants for biodegradable pharmaceuticals. • Summer and winter inocula produced slightly different biotransformation rate constants although microbial community composition did not significantly change. Graphical abstract

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Evaluation of the microbial community structure of potable water samples from occupied and unoccupied buildings using16S rRNA amplicon sequencing

10.1101/2020.07.17.209346 ◽

2020 ◽

Author(s):

Kimothy L Smith ◽

Howard A Shuman ◽

Douglas Findeisen

Keyword(s):

Microbial Community ◽

16S Rrna ◽

Water Samples ◽

Ad Hoc ◽

Microbial Community Composition ◽

Amplicon Sequencing ◽

The Other ◽

Water Usage ◽

16S Rrna Amplicon Sequencing ◽

Oxford Nanopore

AbstractWe conducted two studies of water samples from buildings with normal occupancy and water usage compared to water from buildings that were unoccupied with little or no water usage due to the COVID-19 shutdown. Study 1 had 52 water samples obtained ad hoc from buildings in four metropolitan locations in different states in the US and a range of building types. Study 2 had 36 water samples obtained from two buildings in one metropolitan location with matched water sample types. One of the buildings had been continuously occupied, and the other substantially vacant for approximately 3 months. All water samples were analyzed using 16S rRNA amplicon sequencing with a MinION from Oxford Nanopore Technologies. More than 127 genera of bacteria were identified, including genera with members that are known to include more than 50 putative frank and opportunistic pathogens. While specific results varied among sample locations, 16S rRNA amplicon abundance and the diversity of bacteria were higher in water samples from unoccupied buildings than normally occupied buildings as was the abundance of sequenced amplicons of genera known to include pathogenic bacterial members. In both studies Legionella amplicon abundance was relatively small compared to the abundance of the other bacteria in the samples. Indeed, when present, the relative abundance of Legionella amplicons was lower in samples from unoccupied buildings. Legionella did not predominate in any of the water samples and were found, on average, in 9.6% of samples in Study 1 and 8.3% of samples in Study 2.SynopsisComparison of microbial community composition in the plumbing of occupied and unoccupied buildings during the COVID-19 pandemic shutdown.

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Combining whole genome shotgun sequencing and rDNA amplicon analyses to improve detection of microbe-microbe interaction networks in plant leaves

10.1101/823492 ◽

2019 ◽

Cited By ~ 2

Author(s):

Julian Regalado ◽

Derek S. Lundberg ◽

Oliver Deusch ◽

Sonja Kersten ◽

Talia Karasov ◽

...

Keyword(s):

16S Rdna ◽

High Throughput Sequencing ◽

Cost Effective ◽

Amplicon Sequencing ◽

Low Complexity ◽

Shotgun Sequencing ◽

Wild Plants ◽

Hybrid Strategy ◽

Domains Of Life ◽

Metagenome Sequencing

AbstractMicroorganisms from all domains of life establish associations with plants. Although some harm the plant, others antagonize pathogens or prime the plant immune system, acquire nutrients, tune plant hormone levels, or perform additional services. Most culture-independent plant microbiome research has focused on amplicon sequencing of 16S rDNA and/or the internal transcribed spacer (ITS) of rDNA loci, but the decreasing cost of high-throughput sequencing has made shotgun metagenome sequencing increasingly accessible. Here, we describe shotgun sequencing of 275 wild Arabidopsis thaliana leaf microbiomes from southwest Germany, with additional bacterial 16S rDNA and eukaryotic ITS1 amplicon data from 176 of these samples. The shotgun data were dominated by bacterial sequences, with eukaryotes contributing only a minority of reads. For shotgun and amplicon data, microbial membership showed weak associations with both site of origin and plant genotype, both of which were highly confounded in this dataset. There was large variation among microbiomes, with one extreme comprising samples of low complexity and a high load of microorganisms typical of infected plants, and the other extreme being samples of high complexity and a low microbial load. We use the metagenome data, which captures the ratio of bacterial to plant DNA in leaves of wild plants, to scale the 16S rDNA amplicon data such that they reflect absolute bacterial abundance. We show that this cost-effective hybrid strategy overcomes compositionality problems in amplicon data and leads to fundamentally different conclusions about microbiome community assembly.

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pH and Phosphate Induced Shifts in Carbon Flow and Microbial Community during Thermophilic Anaerobic Digestion

Microorganisms ◽

10.3390/microorganisms8020286 ◽

2020 ◽

Vol 8 (2) ◽

pp. 286

Author(s):

Nina Lackner ◽

Andreas O. Wagner ◽

Rudolf Markt ◽

Paul Illmer

Keyword(s):

Microbial Community ◽

Microbial Community Composition ◽

Amplicon Sequencing ◽

Carbon Flow ◽

Rrna Gene ◽

Organic Substrates ◽

Ch4 Production ◽

Ph Conditions ◽

Ph Range ◽

In The Beginning

pH is a central environmental factor influencing CH4 production from organic substrates, as every member of the complex microbial community has specific pH requirements. Here, we show how varying pH conditions (5.0–8.5, phosphate buffered) and the application of a phosphate buffer per se induce shifts in the microbial community composition and the carbon flow during nine weeks of thermophilic batch digestion. Beside monitoring the methane production as well as volatile fatty acid concentrations, amplicon sequencing of the 16S rRNA gene was conducted. The presence of 100 mM phosphate resulted in reduced CH4 production during the initial phase of the incubation, which was characterized by a shift in the dominant methanogenic genera from a mixed Methanosarcina and Methanoculleus to a pure Methanoculleus system. In buffered samples, acetate strongly accumulated in the beginning of the batch digestion and subsequently served as a substrate for methanogens. Methanogenesis was permanently inhibited at pH values ≤5.5, with the maximum CH4 production occurring at pH 7.5. Adaptations of the microbial community to the pH variations included shifts in the archaeal and bacterial composition, as less competitive organisms with a broad pH range were able to occupy metabolic niches at unfavorable pH conditions.

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