Extension of PERMANOVA to Testing the Mediation Effect of the Microbiome

Yue, Ye; Hu, Yi‐Juan

doi:10.3390/genes13060940

Cited by 7 publications

(8 citation statements)

References 39 publications

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“…In short, this approach specifies regressions for potentially mediating taxa at genus level on education, and MCI adjusted for education, in turn utilizing resulting p values to test mediation. Two functions were used, allowing to estimate mediation by abundance of specific taxa or by the overall composition of the microbiome (ldm [LDM]; permanovaFL [LDM]), while controlling for false discovery rates (40)(41)(42). Ldm suggests mediation if education affects the microbiome and consequentially the outcome.…”

Section: Discussionmentioning

confidence: 99%

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Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

Klee,

Aho,

May

et al. 2024

J Prev Alz Dis

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Background With differences apparent in the gut microbiome in mild cognitive impairment (MCI) and dementia, and risk factors of dementia linked to alterations of the gut microbiome, the question remains if gut microbiome characteristics may mediate associations of education with MCI. Objectives We sought to examine potential mediation of the association of education and MCI by gut microbiome diversity or composition. Design Cross-sectional study. Setting Luxembourg, the Greater Region (surrounding areas in Belgium, France, Germany). Participants Control participants of the Luxembourg Parkinson’s Study. Measurements Gut microbiome composition, ascertained with 16S rRNA gene amplicon sequencing. Differential abundance, assessed across education groups (0–10, 11–16, 16+ years of education). Alpha diversity (Chao1, Shannon and inverse Simpson indices). Mediation analysis with effect decomposition was conducted with education as exposure, MCI as outcome and gut microbiome metrics as mediators. Results After exclusion of participants below 50, or with missing data, n=258 participants (n=58 MCI) were included (M [SD] Age=64.6 [8.3] years). Higher education (16+ years) was associated with MCI (Odds ratio natural direct effect=0.35 [95% CI 0.15–0.81]. Streptococcus and Lachnospiraceae -UCG-001 genera were more abundant in higher education. Conclusions Education is associated with gut microbiome composition and MCI risk without clear evidence for mediation. However, our results suggest signatures of the gut microbiome that have been identified previously in AD and MCI to be reflected in lower education and suggest education as important covariate in microbiome studies. Electronic Supplementary Material Supplementary material is available for this article at 10.14283/jpad.2024.19 and is accessible for authorized users.

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Section: Discussionmentioning

confidence: 99%

“…For beta diversity as mediator, a previously described inverse-regression-based approach to mediation was employed at genus level ( 40 , 41 ). In short, this approach specifies regressions for potentially mediating taxa at genus level on education, and MCI adjusted for education, in turn utilizing resulting p values to test mediation.…”

Section: Methodsmentioning

confidence: 99%

Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

Klee,

Aho,

May

et al. 2024

J Prev Alz Dis

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“…Our approach is readily applicable to microbiome data generated from shotgun metagenomic sequencing, although these data have different error profiles than 16S rRNA sequencing data. In fact, in a recent publication [ 28 ], we have applied permanovaFL using the approach developed here to analyze the shotgun metagenomic sequencing data of the gut microbiome and the outcome data on progression-free survival that were generated from a melanoma immunotherapy study [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Testing microbiome associations with survival times at both the community and individual taxon levels

Satten

et al. 2022

PLoS Comput Biol

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Background Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. Methods We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. Results Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. Conclusions Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings.

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“…Statistical analyses were performed to establish relationships between tested variables and to correlate how important each one is to the outcome where dependencies exist for dimension reduction of variables [ 66 ]. Permutational multivariate analysis of variance (PERMANOVA) was applied to a dataset as a nonparametric method to conduct multivariate ANOVA and test for differences between variables [ 67 ]. Afterward, multivariate analysis was conducted using two different approaches (unsupervised and supervised).…”

Section: Methodsmentioning

confidence: 99%

Why Do These Yeasts Smell So Good? Volatile Organic Compounds (VOCs) Produced by Malassezia Species in the Exponential and Stationary Growth Phases

et al. 2023

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Malassezia synthesizes and releases volatile organic compounds (VOCs), small molecules that allow them to carry out interaction processes. These lipid-dependent yeasts belong to the human skin mycobiota and are related to dermatological diseases. However, knowledge about VOC production and its function is lacking. This study aimed to determine the volatile profiles of Malassezia globosa, Malassezia restricta, and Malassezia sympodialis in the exponential and stationary growth phases. The compounds were separated and characterized in each growth phase through headspace solid-phase microextraction (HS-SPME) and gas chromatography–mass spectrometry (GC–MS). We found a total of 54 compounds, 40 annotated. Most of the compounds identified belong to alcohols and polyols, fatty alcohols, alkanes, and unsaturated aliphatic hydrocarbons. Unsupervised and supervised statistical multivariate analyses demonstrated that the volatile profiles of Malassezia differed between species and growth phases, with M. globosa being the species with the highest quantity of VOCs. Some Malassezia volatiles, such as butan-1-ol, 2-methylbutan-1-ol, 3-methylbutan-1-ol, and 2-methylpropan-1-ol, associated with biological interactions were also detected. All three species show at least one unique compound, suggesting a unique metabolism. The ecological functions of the compounds detected in each species and growth phase remain to be studied. They could interact with other microorganisms or be an important clue in understanding the pathogenic role of these yeasts.

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Extension of PERMANOVA to Testing the Mediation Effect of the Microbiome

Cited by 7 publications

References 39 publications

Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

Testing microbiome associations with survival times at both the community and individual taxon levels

Why Do These Yeasts Smell So Good? Volatile Organic Compounds (VOCs) Produced by Malassezia Species in the Exponential and Stationary Growth Phases

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