Gut microbiome diversity detected by high-coverage 16S and shotgun sequencing of paired stool and colon sample

Mas-Lloret, Joan; Obón-Santacana, Mireia; Ibáñez‐Sanz, Gemma; Guinó, Elisabet; Pato, Miguel L; Rodríguez-Moranta, Francisco; Mata, Alfredo; García-Rodríguez, Ana; Moreno, Vı́ctor; Pimenoff, Ville N.

doi:10.1038/s41597-020-0427-5

Cited by 48 publications

(34 citation statements)

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“…An in silico analysis of the xylem microbiome of field-grown olives exposed to natural X. fastidiosa infection was performed with two sequencing approaches, a classical 16S/ITS rRNA gene amplicon sequencing and WMSS. Both approaches have their pros and cons, which mainly rely on the analysis of a single gene, consolidated pipelines for the analysis, and low costs for 16S/ITS rRNA gene, opposed to higher sequencing depths, costs, and data recovery using WMSS [ 47 , 48 , 49 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

Differences in the Endophytic Microbiome of Olive Cultivars Infected by Xylella fastidiosa across Seasons

et al. 2020

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The dynamics of Xylella fastidiosa infections in the context of the endophytic microbiome was studied in field-grown plants of the susceptible and resistant olive cultivars Kalamata and FS17. Whole metagenome shotgun sequencing (WMSS) coupled with 16S/ITS rRNA gene sequencing was carried out on the same trees at two different stages of the infections: In Spring 2017 when plants were almost symptomless and in Autumn 2018 when the trees of the susceptible cultivar clearly showed desiccations. The progression of the infections detected in both cultivars clearly unraveled that Xylella tends to occupy the whole ecological niche and suppresses the diversity of the endophytic microbiome. However, this trend was mitigated in the resistant cultivar FS17, harboring lower population sizes and therefore lower Xylella average abundance ratio over total bacteria, and a higher α-diversity. Host cultivar had a negligible effect on the community composition and no clear associations of a single taxon or microbial consortia with the resistance cultivar were found with both sequencing approaches, suggesting that the mechanisms of resistance likely reside on factors that are independent of the microbiome structure. Overall, Proteobacteria, Actinobacteria, Firmicutes, and Bacteriodetes dominated the bacterial microbiome while Ascomycota and Basidiomycota those of Fungi.

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Section: Discussionmentioning

confidence: 99%

Differences in the Endophytic Microbiome of Olive Cultivars Infected by Xylella fastidiosa across Seasons

et al. 2020

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“…We do accept that sometimes, due to cost or sample quality, 16S rRNA sequencing may be the only option and it is important to note that efforts have been made to predict function from 16S data with some success [ 45 ]. The advantages offered by shotgun metagenomic sequencing are reviewed elsewhere [ 44 , 46 ], but it is appropriate to highlight the different analytical techniques used in studies (Tables 1 and 2 ). Thus far, only Blacher et al [ 17 ] have reported data based on metagenomic sequencing.…”

Section: Main Textmentioning

confidence: 99%

The gut microbiome: a key player in the complexity of amyotrophic lateral sclerosis (ALS)

Boddy

Giovannelli

Sassani

et al. 2021

BMC Med

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Background Much progress has been made in mapping genetic abnormalities linked to amyotrophic lateral sclerosis (ALS), but the majority of cases still present with no known underlying cause. Furthermore, even in families with a shared genetic abnormality there is significant phenotypic variability, suggesting that non-genetic elements may modify pathogenesis. Identification of such disease-modifiers is important as they might represent new therapeutic targets. A growing body of research has begun to shed light on the role played by the gut microbiome in health and disease with a number of studies linking abnormalities to ALS. Main body The microbiome refers to the genes belonging to the myriad different microorganisms that live within and upon us, collectively known as the microbiota. Most of these microbes are found in the intestines, where they play important roles in digestion and the generation of key metabolites including neurotransmitters. The gut microbiota is an important aspect of the environment in which our bodies operate and inter-individual differences may be key to explaining the different disease outcomes seen in ALS. Work has begun to investigate animal models of the disease, and the gut microbiomes of people living with ALS, revealing changes in the microbial communities of these groups. The current body of knowledge will be summarised in this review. Advances in microbiome sequencing methods will be highlighted, as their improved resolution now enables researchers to further explore differences at a functional level. Proposed mechanisms connecting the gut microbiome to neurodegeneration will also be considered, including direct effects via metabolites released into the host circulation and indirect effects on bioavailability of nutrients and even medications. Conclusion Profiling of the gut microbiome has the potential to add an environmental component to rapidly advancing studies of ALS genetics and move research a step further towards personalised medicine for this disease. Moreover, should compelling evidence of upstream neurotoxicity or neuroprotection initiated by gut microbiota emerge, modification of the microbiome will represent a potential new avenue for disease modifying therapies. For an intractable condition with few current therapeutic options, further research into the ALS microbiome is of crucial importance.

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“…While we did not set inclusion criteria for samples based on these differences, previous research has demonstrated distinct differences in microbiome composition between sample types and demographic groups [18, 70, 71]. For example, principal coordinate analysis (PCoA) with weighted UniFrac distance [72] and principal component analysis (PCA) of CLR-transformed taxonomic features indicated paired biopsy and stool samples from the same individual cluster separately [73].…”

Section: Resultsmentioning

confidence: 99%

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

Kubinski¹,

Djamen-Kepaou²,

Zhanabaev³

et al. 2021

Preprint

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BackgroundInflammatory bowel disease (IBD) patients wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome’s composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools.ResultsWe collected fifteen 16S rRNA microbiome datasets (7707 samples) from North America to benchmark combinations of gut microbiome features, data normalization methods, batch effect reduction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with four binary classification metrics following leave-one dataset-out cross validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features obtained from QIIME2 lead to better classification of samples from IBD patients than inferred functional features obtained from PICRUSt2. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Prior to training a non-linear machine learning model on taxonomic features, it is important to apply a compositional normalization method and remove batch effects with the naive zero-centering method. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics.ConclusionsThese findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

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Gut microbiome diversity detected by high-coverage 16S and shotgun sequencing of paired stool and colon sample

Cited by 48 publications

References 50 publications

Differences in the Endophytic Microbiome of Olive Cultivars Infected by Xylella fastidiosa across Seasons

Differences in the Endophytic Microbiome of Olive Cultivars Infected by Xylella fastidiosa across Seasons

The gut microbiome: a key player in the complexity of amyotrophic lateral sclerosis (ALS)

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

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