Evaluating the Information Content of Shallow Shotgun Metagenomics

Hillmann, Benjamin; Al‐Ghalith, Gabriel A.; Shields-Cutler, Robin R.; Zhu, Quanyin; Gohl, Daryl M.; Beckman, Kenneth B.; Knight, Rob; Knights, Dan

doi:10.1128/msystems.00069-18

Cited by 325 publications

(289 citation statements)

References 26 publications

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“…We estimated that approximately 300,000 non-plant reads constitute a lower bound for robust description of taxonomic profiles (Supplementary Figure 18). This agrees well with similar estimates for human gut microbiome samples [11] , and translates into 7.5 million total reads, or just under 1.12 Gb total sequencing reads per sample for 90% of the dataset. As a counterpoint to downsampling reads, we were curious how much could be gained by having much deeper sequence coverage from a single plant.…”

Section: Overview Of Microbial Taxa In the Metagenomessupporting

confidence: 88%

“…Furthermore, one gene cannot reliably predict the other genes and genetic and metabolic functions in a microbe beyond some conserved features [9,10] . Whole metagenome shotgun sequencing of DNA extracts has become an attractive tool for dissecting complex microbial communities, and databases and algorithms that support such efforts are being developed [11,12] . Shotgun sequencing supplies information on the total DNA content of microorganisms as opposed to a specific locus, which in principle enables us to ask many new types of questions [8,13,14] .…”

Section: Introductionmentioning

confidence: 99%

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

Regalado¹,

Lundberg²,

Deusch³

et al. 2019

Preprint

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Microorganisms 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 1 Regalado et al.Combining sequencing methods in plant metagenomics 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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Section: Overview Of Microbial Taxa In the Metagenomessupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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

Regalado¹,

Lundberg²,

Deusch³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…There is a large amount of sequencing data on microbial communities available today. This is due in part to the falling cost of 16S rRNA and shallow shotgun sequencing (53). There is wide interest in extracting information from sequencing data that goes beyond bacterial proportions (54).…”

Section: Discussionmentioning

confidence: 99%

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

2020

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Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.

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“…Part of the challenge in early life samples is the low biomass of the stool samples. Fortunately, improved methods for profiling low-biomass samples 23,24 make it increasingly feasible to study the strain-level infant gut colonization at the very early stages of life.…”

mentioning

confidence: 99%

Delivery mode impacts newborn gut colonization efficiency

Mitchell

Hogstrom

Bryant

et al. 2020

Preprint

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Delivery mode is the variable with the greatest influence on the infant gut microbiome composition in the first few months of life. Children born by Cesarean section (C-section) lack species from the Bacteroides genus in their gut microbial community, and this difference can be detectable until 6-18 months of age. One hypothesis is that these differences stem from lack of exposure to the maternal vaginal microbiome, as children born by C-section do not pass through the birth canal; however, Bacteroides species are not common members of the vaginal microbiome, thus this explanation seems inadequate. Here, we set out to re-evaluate this hypothesis by collecting rectal and vaginal samples before delivery from 73 mothers with paired stool from their infants in the first two weeks of life. We compared microbial profiles of infants born by planned, pre-labor C-section to those born by emergent, post-labor surgery (where the child was in the birth canal, but eventually delivered through an abdominal incision), and found no significant differences in the microbiome between these two groups. Both groups showed the characteristic signature lack of Bacteroides species, despite their difference in exposure to the birth canal. Surprisingly, this signature was only evident in samples from week two of life, but not in the first week. Children born by C-section often had high abundance of Bacteroides in

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Evaluating the Information Content of Shallow Shotgun Metagenomics

Cited by 325 publications

References 26 publications

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

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

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

Delivery mode impacts newborn gut colonization efficiency

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