Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

Machine learning determines the incidence of Alzheimer's disease based on population gut microbiome profile

The human microbiome is a complex and dynamic community of microbes, thought to have symbiotic benefit to its host. Influences of the gut microbiome on brain microglia have been identified as a potential mechanism contributing to neurodegenerative diseases, such as Alzheimer's disease, motor neurone disease and Parkinson's disease (Boddy SL, Giovannelli I, Sassani M, et al. The gut microbiome: A key player in the complexity of amyotrophic lateral sclerosis (ALS). BMC Med. 2021;19(1):13). We hypothesize that population level differences in the gut microbiome will predict the incidence of Alzheimer's disease using machine learning methods. Cross-sectional analyses were performed in R, using two large, open-access microbiome datasets (n = 959 and n = 2012). Countries in these datasets were grouped based on Alzheimer's disease incidence and the gut microbiome profiles compared. In countries with a high incidence of Alzheimer's disease, there is a significantly lower diversity of the gut microbiome (P < 0.05). A permutational analysis of variance test (P < 0.05) revealed significant differences in the microbiome profile between countries with high versus low incidence of Alzheimer's disease with several contributing taxa identified: at a species level Escherichia coli, and at a genus level Haemophilus and Akkermansia were found to be reproducibly protective in both datasets. Additionally, using machine learning, we were able to predict the incidence of Alzheimer's disease within a country based on the microbiome profile (mean area under the curve 0.889 and 0.927). We conclude that differences in the microbiome can predict the varying incidence of Alzheimer's disease between countries. Our results support a key role of the gut microbiome in neurodegeneration at a population level.

Exploration of the Changes in Facial Microbiota of Maskne Patients and Healthy Controls Before and After Wearing Masks Using 16 S rRNA Analysis

Whether in the field of medical care, or in people's daily life and health protection, the importance of masks has been paid more and more attention. Acne, the most common complication after wearing masks, which is also called maskne, has been successfully introduced into the common language as a common topic of dermatologist consultations. This study aims to study the changes of microflora in maskne patients and healthy controls before and after wearing masks. In the summer of 2023, we collected a total of 50 samples from 15 maskne patients and 10 healthy controls before and after wearing surgical masks for a long time. 16 S ribosomal DNA sequencing and identification technology with V3-V4 variable region were adopted to explore the microbiome changes caused by mask wearing, analyze the changes in microbial diversity, and make interaction network. LDA effect size analysis was used to identify which bacteria showed significant changes in their relative abundance from phylum to genus. After wearing a mask, the microbiome of the maskne patients changed significantly more than that of the healthy controls, with both α diversity and β diversity lower than those of maskne patients before wearing masks and those of healthy controls after wearing masks. Co-occurrence network analysis showed that compared with other groups, the network of maskne patients after wearing masks for a long time had the lowest connectivity and complexity, but the highest clustering property, while the opposite was true for healthy controls. Many microbes that are potentially beneficial to the skin decreased significantly after wearing a mask. There was almost no difference in healthy controls before and after wearing a mask.

Targeting respiratory microbiomes in COPD and bronchiectasis

INTRODUCTION

This review summarizes our current understanding of the respiratory microbiome in COPD and Bronchiectasis. We explore the interplay between microbial communities, host immune responses, disease pathology, and treatment outcomes.

AREAS COVERED

We detail the dynamics of the airway microbiome, its influence on chronic respiratory diseases, and analytical challenges. Relevant articles from PubMed and Medline (January 2010-March 2024) were retrieved and summarized. We examine clinical correlations of the microbiome in COPD and bronchiectasis, assessing how current therapies impact upon it. The potential of emerging immunotherapies, antiinflammatories and antimicrobial strategies is discussed, with focus on the pivotal role of commensal taxa in maintaining respiratory health and the promising avenue of microbiome remodeling for disease management.

EXPERT OPINION

Given the heterogeneity in microbiome composition and its pivotal role in disease development and progression, a shift toward microbiome-directed therapeutics is appealing. This transition, from traditional 'pathogencentric' diagnostic and treatment modalities to those acknowledging the microbiome, can be enabled by evolving crossdisciplinary platforms which have the potential to accelerate microbiome-based interventions into routine clinical practice. Bridging the gap between comprehensive microbiome analysis and clinical application, however, remains challenging, necessitating continued innovation in research, diagnostics, trials, and therapeutic development pipelines.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Microbial biogeography of the eastern Yucatán carbonate aquifer

IMPORTANCE

The extensive Yucatán carbonate aquifer, located primarily in southeastern Mexico, is pockmarked by numerous sinkholes (cenotes) that lead to a complex web of underwater caves. The aquifer hosts a diverse yet understudied microbiome throughout its highly stratified water column, which is marked by a meteoric lens floating on intruding seawater owing to the coastal proximity and high permeability of the Yucatán carbonate platform. Here, we present a biogeographic survey of bacterial and archaeal communities from the eastern Yucatán aquifer. We apply a novel network analysis software that models ecological niche space from microbial taxonomic abundance data. Our analysis reveals that the aquifer community is composed of several distinct niches that follow broader regional and hydrological patterns. This work lays the groundwork for future investigations to characterize the biogeochemical potential of the entire aquifer with other systems biology approaches.

More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes

Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond "who is present" to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.

Connect the dots: sketching out microbiome interactions through networking approaches

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.

Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities

MOTIVATION

Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully decipher and control microbial communities.

RESULTS

We present a novel approach for identifying species driving interactions within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species (MDS) using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile (rCDI) infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn's disease patients, uncovering driver species consistent with previous work. Bakdrive represents a novel approach for capturing microbial interactions.

AVAILABILITY AND IMPLEMENTATION

Bakdrive is open-source and available at: https://gitlab.com/treangenlab/bakdrive.

Univariate and Multivariate Statistical Analysis of Microbiome Data: An Overview

Microbiome data is high dimensional, sparse, compositional, and over-dispersed. Therefore, modeling microbiome data is very challenging and it is an active research area. Microbiome analysis has become a progressing area of research as microorganisms constitute a large part of life. Since many methods of microbiome data analysis have been presented, this review summarizes the challenges, methods used, and the advantages and disadvantages of those methods, to serve as an updated guide for those in the field. This review also compared different methods of analysis to progress the development of newer methods.

Disentangling the Functional Role of Fungi in Cold Seep Sediment

Cold seeps are biological oases of the deep sea fueled by methane, sulfates, nitrates, and other inorganic sources of energy. Chemolithoautotrophic bacteria and archaea dominate seep sediment, and their diversity and biogeochemical functions are well established. Fungi are likewise diverse, metabolically versatile, and known for their ability to capture and oxidize methane. Still, no study has ever explored the functional role of the mycobiota in the cold seep biome. To assess the complex role of fungi and fill in the gaps, we performed network analysis on 147 samples to disentangle fungal-prokaryotic interactions (fungal 18S and prokaryotic 16S) in the Haima cold seep region. We demonstrated that fungi are central species with high connectivity at the epicenter of prokaryotic networks, reduce their random-attack vulnerability by 60%, and enhance information transfer efficiency by 15%. We then scavenged a global metagenomic and metatranscriptomic data set from 10 cold seep regions for fungal genes of interest (hydrophobins, cytochrome P450s, and ligninolytic family of enzymes); this is the first study to report active transcription of 2,500+ fungal genes in the cold seep sediment. The genera Fusarium and Moniliella were of notable importance and directly correlated with high methane abundance in the sulfate-methane transition zone (SMTZ), likely due to their ability to degrade and solubilize methane and oils. Overall, our results highlight the essential yet overlooked contribution of fungi to cold seep biological networks and the role of fungi in regulating cold seep biogeochemistry. IMPORTANCE The challenges we face when analyzing eukaryotic metagenomic and metatranscriptomic data sets have hindered our understanding of cold seep fungi and microbial eukaryotes. This fact does not make the mycobiota any less critical in mediating cold seep biogeochemistry. On the contrary, many fungal genera can oxidize and solubilize methane, produce methane, and play a unique role in nutrient recycling via saprotrophic enzymatic activity. In this study, we used network analysis to uncover key fungal-prokaryotic interactions that can mediate methane biogeochemistry and metagenomics and metatranscriptomics to report that fungi are transcriptionally active in the cold seep sediment. With concerns over rising methane levels and cold seeps being a pivotal source of global methane input, our holistic understanding of methane biogeochemistry with all domains of life is essential. We ultimately encourage scientists to utilize state-of-the-art tools and multifaceted approaches to uncover the role of microeukaryotic organisms in understudied systems.

Dynamic effects of probiotic formula ecologic®825 on human small intestinal ileostoma microbiota: a network theory approach

The gut microbiota plays a pivotal role in health and disease. The use of probiotics as microbiota-targeted therapies is a promising strategy to improve host health. However, the molecular mechanisms involved in such therapies are often not well understood, particularly when targeting the small intestinal microbiota. In this study, we investigated the effects of a probiotic formula (Ecologic®825) on the adult human small intestinal ileostoma microbiota. The results showed that supplementation with the probiotic formula led to a reduction in the growth of pathobionts, such as Enterococcaceae and Enterobacteriaceae, and a decrease in ethanol production. These changes were associated with significant alterations in nutrient utilization and resistance to perturbations. These probiotic mediated alterations which coincided with an initial increase in lactate production and decrease in pH were followed by a sharp increase in the levels of butyrate and propionate. Moreover, the probiotic formula increased the production of multiple N-acyl amino acids in the stoma samples. The study demonstrates the utility of network theory in identifying novel microbiota-targeted therapies and improving existing ones. Overall, the findings provide insights into the dynamic molecular mechanisms underlying probiotic therapies, which can aid in the development of more effective treatments for a range of conditions.

A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors

Fungal microorganisms (mycobiota) comprise a small but immunoreactive component of the human microbiome, yet little is known about their role in human cancers. Pan-cancer analysis of multiple body sites revealed tumor-associated mycobiomes at up to 1 fungal cell per 104 tumor cells. In lung cancer, Blastomyces was associated with tumor tissues. In stomach cancers, high rates of Candida were linked to the expression of pro-inflammatory immune pathways, while in colon cancers Candida was predictive of metastatic disease and attenuated cellular adhesions. Across multiple GI sites, several Candida species were enriched in tumor samples and tumor-associated Candida DNA was predictive of decreased survival. The presence of Candida in human GI tumors was confirmed by external ITS sequencing of tumor samples and by culture-dependent analysis in an independent cohort. These data implicate the mycobiota in the pathogenesis of GI cancers and suggest that tumor-associated fungal DNA may serve as diagnostic or prognostic biomarkers.

MicNet toolbox: Visualizing and unraveling a microbial network

Applications of network theory to microbial ecology are an emerging and promising approach to understanding both global and local patterns in the structure and interplay of these microbial communities. In this paper, we present an open-source python toolbox which consists of two modules: on one hand, we introduce a visualization module that incorporates the use of UMAP, a dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density; on the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple the resulting networks with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) we validate the improvements of our new version of SparCC. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. This easy-to-use implementation is aimed to microbial ecologists with little to no experience in programming, while the most experienced bioinformatics will also be able to manipulate the source code's functions with ease.

Complexity-stability trade-off in empirical microbial ecosystems

May's stability theory, which holds that large ecosystems can be stable up to a critical level of complexity, a product of the number of resident species and the intensity of their interactions, has been a central paradigm in theoretical ecology. So far, however, empirically demonstrating this theory in real ecological systems has been a long-standing challenge with inconsistent results. Especially, it is unknown whether this theory is pertinent in the rich and complex communities of natural microbiomes, mainly due to the challenge of reliably reconstructing such large ecological interaction networks. Here we introduce a computational framework for estimating an ecosystem's complexity without relying on a priori knowledge of its underlying interaction network. By applying this method to human-associated microbial communities from different body sites and sponge-associated microbial communities from different geographical locations, we found that in both cases the communities display a pronounced trade-off between the number of species and their effective connectance. These results suggest that natural microbiomes are shaped by stability constraints, which limit their complexity.

Statistical Methods for Microbiome Compositional Data Network Inference: A Survey

Microbes can be found almost everywhere in the world. They are not isolated, but rather interact with each other and establish connections with their living environments. Studying these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A widely used approach toward this objective involves the inference of microbiome interaction networks. However, owing to the compositional, high-dimensional, sparse, and heterogeneous nature of observed microbial data, applying network inference methods to estimate their associations is challenging. In addition, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this article, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks, and differential networks. Their assumptions, high-level ideas, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial interaction study and future prospects. Finally, we point out several feasible directions of microbial network inference analysis and highlight that future research requires the joint promotion of statistical computation methods and experimental techniques.

A Probabilistic Structural Equation Model to Evaluate Links between Gut Microbiota and Body Weights of Chicken Fed or Not Fed Insect Larvae

Simple Summary

Feeding poultry with insects could reduce production costs, but the impact of this diet on their gut microbiota and growth is little known because the network of relationships between their weights, the composition of their microbiota and their diet is complex and potentially biased by confounding factors (such as the gut compartment, age and sex of the birds). In this study, we were able to unravel these relationships in local breed chickens fed or not fed with black soldier fly larvae thanks to a technique of artificial intelligence (the probabilistic structural equation model). Bacteria were grouped into few entities with distinctive metabolic attributes and were probably linked nutritionally. Birds’ age influenced body weights and bacterial composition. The proposed methodology was thus able to simplify the complex dependencies among bacteria present in the gut and to highlight links potentially important in the response of chicken to insect feed.

Abstract

Feeding chicken with black soldier fly larvae (BSF) may influence their rates of growth via effects on the composition of their gut microbiota. To verify this hypothesis, we aim to evaluate a probabilistic structural equation model because it can unravel the complex web of relationships that exist between the bacteria involved in digestion and evaluate whether these influence bird growth. We followed 90 chickens fed diets supplemented with 0%, 5% or 10% BSF and measured the strength of the relationship between their weight and the relative abundance of bacteria (OTU) present in their cecum or cloaca at 16, 28, 39, 67 or 73 days of age, while adjusting for potential confounding effects of their age and sex. Results showed that OTUs (62 genera) could be combined into ten latent constructs with distinctive metabolic attributes. Links were discovered between these constructs that suggest nutritional relationships. Age directly influenced weights and microbiotal composition, and three constructs indirectly influenced weights via their dependencies on age. The proposed methodology was able to simplify dependencies among OTUs into knowledgeable constructs and to highlight links potentially important to understand the role of insect feed and of microbiota in chicken growth.

Modeling spatial interaction networks of the gut microbiota

How the gut microbiota is organized across space is postulated to influence microbial succession and its mutualistic relationships with the host. The lack of dynamic or perturbed abundance data poses considerable challenges for characterizing the spatial pattern of microbial interactions. We integrate allometric scaling theory, evolutionary game theory, and prey-predator theory into a unified framework under which quasi-dynamic microbial networks can be inferred from static abundance data. We illustrate that such networks can capture the full properties of microbial interactions, including causality, the sign of the causality, strength, and feedback loop, and are dynamically adaptive along spatial gradients, and context-specific, characterizing variability between individuals and within the same individual across time and space. We design and conduct a gut microbiota study to validate the model, characterizing key spatial determinants of the microbial differences between ulcerative colitis and healthy controls. Our model provides a sophisticated means of unraveling a complete atlas of how microbial interactions vary across space and quantifying causal relationships between such spatial variability and change in health state.

Mathematical-based microbiome analytics for clinical translation

Traditionally, human microbiology has been strongly built on the laboratory focused culture of microbes isolated from human specimens in patients with acute or chronic infection. These approaches primarily view human disease through the lens of a single species and its relevant clinical setting however such approaches fail to account for the surrounding environment and wide microbial diversity that exists in vivo. Given the emergence of next generation sequencing technologies and advancing bioinformatic pipelines, researchers now have unprecedented capabilities to characterise the human microbiome in terms of its taxonomy, function, antibiotic resistance and even bacteriophages. Despite this, an analysis of microbial communities has largely been restricted to ordination, ecological measures, and discriminant taxa analysis. This is predominantly due to a lack of suitable computational tools to facilitate microbiome analytics. In this review, we first evaluate the key concerns related to the inherent structure of microbiome datasets which include its compositionality and batch effects. We describe the available and emerging analytical techniques including integrative analysis, machine learning, microbial association networks, topological data analysis (TDA) and mathematical modelling. We also present how these methods may translate to clinical settings including tools for implementation. Mathematical based analytics for microbiome analysis represents a promising avenue for clinical translation across a range of acute and chronic disease states.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Diversity and Interactomics of Bacterial Communities Associated with Dominant Trees During Tropical Forest Recovery

Bacterial communities have been identified as functional key members in soil ecology. A deep relation with these communities maintains forest coverture. Trees harbor particular bacteriomes in the rhizosphere, endosphere, or phyllosphere, different from bulk-soil representatives. Moreover, the plant microbiome appears to be specific for the plant-hosting species, varies through season, and responsive to several environmental factors. This work reports the changes in bacterial communities associated with dominant pioneer trees [Tabebuia rosea and Handroanthus chrysanthus [(Bignoniaceae)] during tropical forest recovery chronosequence in the Mayan forest in Campeche, Mexico. Massive 16S sequencing approach leads to identifying phylotypes associated with rhizosphere, bulk-soil, or recovery stage. Lotka-Volterra interactome modeling suggests the presence of putative regulatory roles of some phylotypes over the rest of the community. Our results may indicate that bacterial communities associated with pioneer trees may establish more complex regulatory networks than those found in bulk-soil. Moreover, modeled regulatory networks predicted from rhizosphere samples resulted in a higher number of nodes and interactions than those found in the analysis of bulk-soil samples.

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next-generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network-level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA-based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community- and network-level properties for monitoring change. We selected crop-associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network-level properties, especially β-properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community-level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network-level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Scientific Prospects for Cannabis-Microbiome Research to Ensure Quality and Safety of Products

Cannabis legalization has occurred in several countries worldwide. Along with steadily growing research in Cannabis healthcare science, there is an increasing interest for scientific-based knowledge in plant microbiology and food science, with work connecting the plant microbiome and plant health to product quality across the value chain of cannabis. This review paper provides an overview of the state of knowledge and challenges in Cannabis science, and thereby identifies critical risk management and safety issues in order to capitalize on innovations while ensuring product quality control. It highlights scientific gap areas to steer future research, with an emphasis on plant-microbiome sciences committed to using cutting-edge technologies for more efficient Cannabis production and high-quality products intended for recreational, pharmaceutical, and medicinal use.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species-species modelling

Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species-metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species-species modelling. Using publicly available data, we examine the ability of species-species models and species-metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species-species interaction models and quadratic SMI models and conclude that only species-metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species-species interaction models cannot match the patterns observed in community growth dynamics, whereas species-metabolite models can. We conclude that species-metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.