Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

Dietary intake of micronized avian eggshell membrane in aged mice reduces circulating inflammatory markers, increases microbiota diversity, and attenuates skeletal muscle aging

Introduction

Avian eggshell membrane (ESM) is a complex extracellular matrix comprising collagens, glycoproteins, proteoglycans, and hyaluronic acid. We have previously demonstrated that ESM possesses anti-inflammatory properties in vitro and regulates wound healing processes in vivo. The present study aimed to investigate if oral intake of micronized ESM could attenuate skeletal muscle aging associated with beneficial alterations in gut microbiota profile and reduced inflammation.

Methods

Elderly male C57BL/6 mice were fed an AIN93G diet supplemented with 0, 0.1, 1, or 8% ESM. Young mice were used as reference. The digestibility of ESM was investigated using the static in vitro digestion model INFOGEST for older people and adults, and the gut microbiota profile was analyzed in mice. In addition, we performed a small-scale pre-clinical human study with healthy home-dwelling elderly (>70 years) who received capsules with a placebo or 500 mg ESM every day for 4 weeks and studied the effect on circulating inflammatory markers.

Results and discussion

Intake of ESM in elderly mice impacted and attenuated several well-known hallmarks of aging, such as a reduction in the number of skeletal muscle fibers, the appearance of centronucleated fibers, a decrease in type IIa/IIx fiber type proportion, reduced gene expression of satellite cell markers Sdc3 and Pax7 and increased gene expression of the muscle atrophy marker Fbxo32. Similarly, a transition toward the phenotypic characteristics of young mice was observed for several proteins involved in cellular processes and metabolism. The digestibility of ESM was poor, especially for the elderly condition. Furthermore, our experiments showed that mice fed with 8% ESM had increased gut microbiota diversity and altered microbiota composition compared with the other groups. ESM in the diet also lowered the expression of the inflammation marker TNFA in mice and in vitro in THP-1 macrophages. In the human study, intake of ESM capsules significantly reduced the inflammatory marker CRP. Altogether, our results suggest that ESM, a natural extracellular biomaterial, may be attractive as a nutraceutical candidate with a possible effect on skeletal muscle aging possibly through its immunomodulating effect or gut microbiota.

Shrimp microbiome and immune development in the early life stages

With its contribution to nutrition, development, and disease resistance, gut microbiome has been recognized as a crucial component of the animal's health and well-being. Microbiome in the gastrointestinal tract constantly interacts with the host animal's immune systems as part of the normal function of the intestines. Interactions between the microbiome and the immune system are complex and dynamic, with the microbiome shaping immune development and function. In contrast, the immune system modulates the composition and activity of the microbiome. In shrimp, as with all other aquatic animals, the interaction between the microbiome and the animals occurs at the early developmental stages. This early interaction is likely essential to the development of immune responses of the animal as well as many key physiological developments that further contribute to the health of shrimp. This review provides background knowledge on the early developmental stage of shrimp and its microbiome, examines the interaction between the microbiome and the immune system in the early life stage of shrimp, and discusses potential pitfalls and challenges associated with microbiome research. Understanding the interaction between the microbiome and shrimp immune system at this crucial developmental stage could have the potential to aid in the establishment of a healthy microbiome, improve shrimp survival, and provide ways to shape the microbiome with feed supplements or other strategies.

Laser capture microdissection as a method for investigating the human hair follicle microbiome reveals region-specific differences in the bacteriome profile

OBJECTIVE

Human hair follicles (HFs) are populated by a rich and diverse microbiome, traditionally evaluated by methods that inadvertently sample the skin microbiome and/or miss microbiota located in deeper HF regions. Thereby, these methods capture the human HF microbiome in a skewed and incomplete manner. This pilot study aimed to use laser-capture microdissection of human scalp HFs, coupled with 16S rRNA gene sequencing to sample the HF microbiome and overcome these methodological limitations.

RESULTS

HFs were laser-capture microdissected (LCM) into three anatomically distinct regions. All main known core HF bacterial colonisers, including Cutibacterium, Corynebacterium and Staphylococcus, were identified, in all three HF regions. Interestingly, region-specific variations in α-diversity and microbial abundance of the core microbiome genera and Reyranella were identified, suggestive of variations in microbiologically relevant microenvironment characteristics. This pilot study therefore shows that LCM-coupled with metagenomics is a powerful tool for analysing the microbiome of defined biological niches. Refining and complementing this method with broader metagenomic techniques will facilitate the mapping of dysbiotic events associated with HF diseases and targeted therapeutic interventions.

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

Combining p-values from various statistical methods for microbiome data

MOTIVATION

In the field of microbiome analysis, there exist various statistical methods that have been developed for identifying differentially expressed features, that account for the overdispersion and the high sparsity of microbiome data. However, due to the differences in statistical models or test formulations, it is quite often to have inconsistent significance results across statistical methods, that makes it difficult to determine the importance of microbiome taxa. Thus, it is practically important to have the integration of the result from all statistical methods to determine the importance of microbiome taxa. A standard meta-analysis is a powerful tool for integrative analysis and it provides a summary measure by combining p-values from various statistical methods. While there are many meta-analyses available, it is not easy to choose the best meta-analysis that is the most suitable for microbiome data.

RESULTS

In this study, we investigated which meta-analysis method most adequately represents the importance of microbiome taxa. We considered Fisher's method, minimum value of p method, Simes method, Stouffer's method, Kost method, and Cauchy combination test. Through simulation studies, we showed that Cauchy combination test provides the best combined value of p in the sense that it performed the best among the examined methods while controlling the type 1 error rates. Furthermore, it produced high rank similarity with the true ranks. Through the real data application of colorectal cancer microbiome data, we demonstrated that the most highly ranked microbiome taxa by Cauchy combination test have been reported to be associated with colorectal cancer.

Short- and Long-Term Effects of a Prebiotic Intervention with Polyphenols Extracted from European Black Elderberry—Sustained Expansion of Akkermansia spp

(1) Background: The intestinal microbiome has emerged as a central factor in human physiology and its alteration has been associated with disease. Therefore, great hopes are placed in microbiota-modulating strategies. Among various approaches, prebiotics, substrates with selective metabolization conferring a health benefit to the host, are promising candidates. Herein, we studied the prebiotic properties of a purified extract from European black elderberries, with a high and standardized content of polyphenols and anthocyanins. (2) Methods: The ELDERGUT trial represents a 9-week longitudinal intervention study divided into 3 distinct phases, namely a baseline, an intervention and a washout period, three weeks each. The intervention consisted of capsules containing 300 mg elderberry extract taken twice a day. Patient-reported outcomes and biosamples were collected weekly. Microbiome composition was assessed using 16S amplicon metagenomics. (3) Results: The supplementation was well tolerated. Microbiome trajectories were highly individualized with a profound shift in diversity indices immediately upon initiation and after termination of the compound. This was accompanied by corresponding changes in species abundance over time. Of particular interest, the relative abundance of Akkermansia spp. continued to increase in a subset of participants even beyond the supplementation period. Associations with participant metadata were detected.

Investigating differential abundance methods in microbiome data: A benchmark study

The development of increasingly efficient and cost-effective high throughput DNA sequencing techniques has enhanced the possibility of studying complex microbial systems. Recently, researchers have shown great interest in studying the microorganisms that characterise different ecological niches. Differential abundance analysis aims to find the differences in the abundance of each taxa between two classes of subjects or samples, assigning a significance value to each comparison. Several bioinformatic methods have been specifically developed, taking into account the challenges of microbiome data, such as sparsity, the different sequencing depth constraint between samples and compositionality. Differential abundance analysis has led to important conclusions in different fields, from health to the environment. However, the lack of a known biological truth makes it difficult to validate the results obtained. In this work we exploit metaSPARSim, a microbial sequencing count data simulator, to simulate data with differential abundance features between experimental groups. We perform a complete comparison of recently developed and established methods on a common benchmark with great effort to the reliability of both the simulated scenarios and the evaluation metrics. The performance overview includes the investigation of numerous scenarios, studying the effect on methods’ results on the main covariates such as sample size, percentage of differentially abundant features, sequencing depth, feature variability, normalisation approach and ecological niches. Mainly, we find that methods show a good control of the type I error and, generally, also of the false discovery rate at high sample size, while recall seem to depend on the dataset and sample size.

The Microbiota is the set of microorganisms that characterize an ecological environment or niche. Several studies have shown that the microbiota is involved in various biological mechanisms that affect the health or balance of the host organism or the ecosystem. New discoveries and insights have been possible thanks to the increasingly efficient sequencing technologies together with the development of bioinformatic computational methods. One of the most interesting analyses in this landscape is the identification of microorganisms that show significant different abundances when two groups of subjects are analysed. Although many computational methods have been developed, it is still unclear which one has the best performance. Therefore, we exploited a simulator of microbiome data to build a simulation framework that allowed us to carry out an extensive benchmarking of the known tools of differential abundance analysis. Our work is not only a starting point to guide analysts in the choice of tools, but also a first step towards a robust, reliable and fair simulation framework.

A Three-Day Intervention With Granola Containing Cereal Beta-Glucan Improves Glycemic Response and Changes the Gut Microbiota in Healthy Individuals: A Crossover Study

Intake of soluble fibers including beta-glucan, is known to improve post-prandial glycemic response. The mechanisms have been attributed to the viscous gel forming in the stomach and small intestine, giving a longer absorption time. However, recent evidence suggests a link between intake of beta-glucan and improved glycemic regulation at subsequent meals through the gut microbiota. We investigated the short-term effect of granola with different amounts of cereal beta-glucan on glycemic response and gut microbiota. After a two-week run-in period (baseline), fourteen healthy, normal weight adults completed a dose-response dietary crossover study. Different amounts of cereal beta-glucan (low: 0.8 g, medium: 3.2 g and high: 6.6 g) were provided in granola and eaten with 200 ml low-fat milk as an evening meal for three consecutive days. Blood glucose and insulin were measured fasted and after an oral glucose tolerance test (OGTT) the following day, in addition to peptide YY (PYY) and glucagon-like peptide (GLP-2), fasting short chain fatty acids (SCFA) in blood, breath H2, and gut microbiota in feces. Only the intervention with medium amounts of beta-glucan decreased blood glucose and insulin during OGTT compared to baseline. Fasting PYY increased with both medium and high beta-glucan meal compared to the low beta-glucan meal. The microbiota and SCFAs changed after all three interventions compared to baseline, where acetate and butyrate increased, while propionate was unchanged. Highest positive effect size after intake of beta-glucan was found with Haemophilus, followed by Veillonella and Sutterella. Furthermore, we found several correlations between different bacterial taxa and markers of glycemic response. In summary, intake of granola containing 3.2 g cereal beta-glucan as an evening meal for three consecutive days reduced the glycemic response after an OGTT 0-180 min and changed gut microbiota composition. Since we cannot rule out that other fiber types have contributed to the effect, more studies are needed to further explore the effect of cereal beta-glucan on glycemic regulation.

Clinical Trial Registration

[www.clinicaltrials.gov], identifier [NCT03293693].