The gut microbiome in konzo

Vilain, Éric; Vashist, Neerja; Ko, Arthur; Priya, Sambhawa; Musasa, Céleste; Mathieu, Alban; Spencer, David A.; Kasendue, Michel Lupamba; Dilufwasayo, Patrick Mamona; Karume, Kevin; Nsibu, Joanna; Manya, Hans; Uy, Mary Nadine Alessandra R.; Colwell, Brian; Boivin, Michael J.; Mayambu, J. P. Banae; Okitundu, Daniel; Droit, Arnaud; Ngoyi, Dieudonné Mumba; Blekhman, Ran; Tshala-Katumbay, Desiré; Vilain, Éric

doi:10.1038/s41467-021-25694-1

Cited by 10 publications

(11 citation statements)

References 56 publications

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“…Our systematic evaluation of strategies to jointly analyze microbiome and metabolome data has permitted the illustration of the best methods depending on the research question. Thus, through an application on metabolomics and metagenomics data of the Konzo disease [29], we applied the more appropriate approaches to highlight different biological patterns occurring between microorganisms and metabolites. We presented the exact workflow in the Konzo data analysis section and Figure S26.…”

Section: Resultsmentioning

confidence: 99%

Section: Real-data Applicationmentioning

confidence: 99%

“…Bracken for taxonomic classifications [29] . Additionally, stool was analyzed by the company Metabolon, harnessing their large in-house repository of rigorously tested and validated metabolites that are used as reference, to detect metabolites present in the samples.…”

Section: Konzo Data Analysis Workflowmentioning

confidence: 99%

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Decoding the Microbiome-Metabolome Nexus: A Systematic Benchmark of Integrative Strategies

Mangnier,

Bodein,

Mariaz

et al. 2024

Preprint

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Background: The exponential growth of high-throughput sequencing technologies was an incredible opportunity for researchers to combine different -omics within computational frameworks. In particular metagenomics and metabolomics data have gained an increasing interest due to their implication in many complex diseases. However, currently, no standard seems to emerge for jointly integrating both microbiome and metabolome datasets within statistical models. Results: Thus, in this paper we comprehensively benchmarked fifteen different integrative methods to link microorganisms and metabolites. Methods evaluated in this paper cover most of the researcher's goals such as global associations, data summarization, individual associations and feature selection. Through an extensive simulation study and an application to real gut microbial datasets, we highlighted the best approaches for unraveling complementary biological processes involved between the two omics. We provided general guidelines for practitioners depending on the scientific question and the data at-hand. Conclusion: In summary, we argue that this paper constitutes a promising avenue for establishing research standards when mutually analyzing metagenomics and metabolomics data, while providing foundations for future methodological developments.

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Section: Resultsmentioning

confidence: 99%

Section: Real-data Applicationmentioning

confidence: 99%

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Decoding the Microbiome-Metabolome Nexus: A Systematic Benchmark of Integrative Strategies

Mangnier,

Bodein,

Mariaz

et al. 2024

Preprint

Self Cite

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“…For example, Bramble et al used shotgun metagenomic sequencing to perform a large-scale comparison of gut microbiome profiles in 180 children (from the urbanized capital Kinshasa to extreme rural areas in the southwest of the Democratic Republic of the Congo, including children affected by Konzo disease from prone villages). It was found that the intestinal microbiome structure varied greatly in different regions, but there was no significant difference in intestinal microbiome or functional enrichment between konzo-prone regions [ 44 ]. Yan et al characterized the intestinal microbiota of the population from 14 regions in one province and found that the influence of region on intestinal microbiota was much greater than other factors, and regional differences affected the cross-regional application of disease models [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Using whole-genome sequencing (WGS) to plot colorectal cancer-related gut microbiota in a population with varied geography

Han

Zhao

et al. 2022

Gut Pathog

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Background Colorectal cancer (CRC) is a multifactorial disease with genetic and environmental factors. Regional differences in risk factors are an important reason for the different incidences of CRC in different regions. Objective The goal was to clarify the intestinal microbial composition and structure of CRC patients in different regions and construct CRC risk prediction models based on regional differences. Methods A metagenomic dataset of 601 samples from 6 countries in the GMrepo and NCBI databases was collected. All whole-genome sequencing (WGS) data were annotated for species by MetaPhlAn2. We obtained the relative abundance of species composition at the species level and genus level. The MicrobiotaProcess package was used to visualize species composition and PCA. LEfSe analysis was used to analyze the differences in the datasets in each region. Spearman correlation analysis was performed for CRC differential species. Finally, the CRC risk prediction model was constructed and verified in each regional dataset. Results The composition of the intestinal bacterial community varied in different regions. Differential intestinal bacteria of CRC in different regions are inconsistent. There was a common diversity of bacteria in all six countries, such as Peptostreptococcus stomatis and Fusobacterium nucleatum at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) are important CRC-related bacteria that are related to other bacteria in different regions. Region has little influence on the accuracy of the CRC risk prediction model. Peptostreptococcus stomatis is an important variable in CRC risk prediction models in all regions. Conclusion Peptostreptococcus stomatis is a common high-risk pathogen of CRC worldwide, and it is an important variable in CRC risk prediction models in all regions. However, regional differences in intestinal bacteria had no significant impact on the accuracy of the CRC risk prediction model.

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“…Microbial studies usually perform global hypothesis tests to explore the association between the microbiome and host phenotype, where the type of phenotype is determined by the distribution it follows, for example, Gaussian (BMI), Bernoulli (health/disease) and multinomial distribution (health/moderate disease/severe disease). Adonis [23] and ANOSIM [24,25] are classic distance-based statistical methods widely used to detect the association between the microbiome and host phenotypes in most microbial studies [26][27][28][29]. However, the disadvantage of these methods is that they do not adequately consider the effects of confounding factors, resulting in power loss.…”

Section: Introductionmentioning

confidence: 99%

multiMiAT: An optimal microbiome-based association test for multicategory phenotypes

Sun

Wang

Xiao

et al. 2022

Preprint

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Microbes affect the metabolism, immunity, digestion and other aspects of the human body incessantly, and dysbiosis of the microbiome drives not only the occurrence but also the development of disease (i.e., multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g., disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the excellent performance of our method. multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer development and the association between the gut microbiome and diverse development statuses of Clostridium difficile infections.

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The gut microbiome in konzo

Cited by 10 publications

References 56 publications

Decoding the Microbiome-Metabolome Nexus: A Systematic Benchmark of Integrative Strategies

Decoding the Microbiome-Metabolome Nexus: A Systematic Benchmark of Integrative Strategies

Using whole-genome sequencing (WGS) to plot colorectal cancer-related gut microbiota in a population with varied geography

multiMiAT: An optimal microbiome-based association test for multicategory phenotypes

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