Treating Bugs as Features: A compositional guide to the statistical analysis of the microbiome-gut-brain axis

Bastiaanssen, Thomaz F.S.; Quinn, Thomas P.; Loughman, Amy

doi:10.48550/arxiv.2207.12475

Cited by 7 publications

(3 citation statements)

References 75 publications

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“…To correct for multiple testing in tests involving microbiome features, the Benjamini-Hochberg (BH) post-hoc was performed with a q-value of 0.1 used as a cutoff for species and 0.2 for functional modules. Plotting was done using the ggplot2 [57] and patchwork [58] libraries in R. Custom R scripts and functions are available online at https://github.com/thomazbastiaanssen/ Tjazi [59]. A linear modelling approach was used to test for a group effect on taxonomic and functional differences, whilst adjusting for covariates including age, sex, BMI, exercise and dietary differences.…”

Section: Taxanomic and Functional Analysismentioning

confidence: 99%

The gut microbiome in social anxiety disorder: evidence of altered composition and function

et al. 2023

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The microbiome-gut-brain axis plays a role in anxiety, the stress response and social development, and is of growing interest in neuropsychiatric conditions. The gut microbiota shows compositional alterations in a variety of psychiatric disorders including depression, generalised anxiety disorder (GAD), autism spectrum disorder (ASD) and schizophrenia but studies investigating the gut microbiome in social anxiety disorder (SAD) are very limited. Using whole-genome shotgun analysis of 49 faecal samples (31 cases and 18 sex- and age-matched controls), we analysed compositional and functional differences in the gut microbiome of patients with SAD in comparison to healthy controls. Overall microbiota composition, as measured by beta-diversity, was found to be different between the SAD and control groups and several taxonomic differences were seen at a genus- and species-level. The relative abundance of the genera Anaeromassillibacillus and Gordonibacter were elevated in SAD, while Parasuterella was enriched in healthy controls. At a species-level, Anaeromassilibacillus sp An250 was found to be more abundant in SAD patients while Parasutterella excrementihominis was higher in controls. No differences were seen in alpha diversity. In relation to functional differences, the gut metabolic module ‘aspartate degradation I’ was elevated in SAD patients. In conclusion, the gut microbiome of patients with SAD differs in composition and function to that of healthy controls. Larger, longitudinal studies are warranted to validate these preliminary results and explore the clinical implications of these microbiome changes.

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Section: Taxanomic and Functional Analysismentioning

confidence: 99%

The gut microbiome in social anxiety disorder: evidence of altered composition and function

et al. 2023

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“…We found associations solely using Bray-Curtis dissimilarity and not with Unifrac or Weighted Unifrac. Since Bray-Curtis dissimilarity measures unshared microbial abundances and Unifrac in turn unshared microbial taxas between two samples and Weighted Unifrac takes into account both abundances and taxas (Bastiaanssen et al, 2022) differing results here could be explained by the difference of these indices. Our results refer to differences in microbial abundances instead of specific taxas in relation to negative reactivity for boys.…”

Section: Discussionmentioning

confidence: 92%

Infant gut microbiota and negative and fear reactivity

Huovinen,

Aatsinki,

Kataja

et al. 2023

Dev Psychopathol

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Background: Studies indicate that gut microbiota is related to neurodevelopmental and behavioral outcomes. Accordingly, early gut microbiota composition (GMC) has been linked to child temperament, but research is still scarce. The aim of this study was to examine how early GMC at 2.5 months is associated with child negative and fear reactivity at 8 and 12 months since they are potentially important intermediate phenotypes of later child psychiatric disorders. Methods: Our study population was 330 infants enrolled in the longitudinal FinnBrain Birth Cohort Study. Gut microbiota composition was analyzed using stool sample 16s rRNA sequencing. Negative and fear reactivity were assessed using the Laboratory Temperament Assessment Battery (Lab-TAB) at child’s age of 8 months (n =150) and the Infant Behavior Questionnaire-Revised Short Form (IBQ-R SF) at child’s age of 12 months (n = 276). Conclusions: We found a positive association between alpha diversity and reported fear reactivity and differing microbial community composition based on negative reactivity for boys. Isobutyric acid correlated with observed negative reactivity, however, this association attenuated in the linear model. Several genera were associated with the selected infant temperament traits. This study adds to the growing literature on links between infant gut microbiota and temperament informing future mechanistic studies.

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“…We used several batch-correction and transformation methods on the NH22 data instead of the voom-SNM normalization. Methods for data transformation included: Centered-Log Ratio 25 [CLR] (with zero values offset to 1), CLR_C 26 , a variant of CLR that imputes zero counts, Relative abundance, and voom 20 . voom was utilized after log-CPM transformation and quantile normalization as in NH22 and P20.…”

Section: Methodsmentioning

confidence: 99%

Race-dependent variability in the human tumor mycobiome

Coster,

Margalit,

Boursi

et al. 2024

Preprint

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IntroductionRecently, Narunsky-Haziza et. al. showed that fungi species identified in a variety of cancer types may have prognostic and diagnostic signficane. We used that data in order to better understand the effects of demographic factors (age, sex, BMI, and race) on the intratumor mycobiome composition.Materials and MethodsWe first tested the data in view of recent critiques of microbiome data processing procedures, and concluded that the batch correction and transformation used on it may produce false signals. Instead, we explored 14 combinations of data transformation and batch correction methods on data of 224 fungal species across 13 cancer types. Propensity scores were utilized to adjust for potential confounders such as histological type and tumor stage. To minimize false outcomes, we identified as positive results only those fungi species that showed significant difference in abundance across a demographic factor within a particular cancer type, using data normalized according to all 14 combinations.Results and DiscussionWe observed significant differences in fungal species abundance within tumors for certain demographic characteristics. Most differences were among races in specific cancers. The findings indicate that there are intricate interactions among the mycobiome, cancer types, and patient demographics. Our study highlights the need for accounting for potential confounders in order to further understanding of the mycobiome’s role in cancer, and underscores the importance of data processing techniques.

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Treating Bugs as Features: A compositional guide to the statistical analysis of the microbiome-gut-brain axis

Cited by 7 publications

References 75 publications

The gut microbiome in social anxiety disorder: evidence of altered composition and function

The gut microbiome in social anxiety disorder: evidence of altered composition and function

Infant gut microbiota and negative and fear reactivity

Race-dependent variability in the human tumor mycobiome

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