Modeling time-series data from microbial communities

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Prediction and analysis of time series data based on granular computing

The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.

Microbial Growth under Limiting Conditions-Future Perspectives

Microorganisms rule the functioning of our planet and each one of the individual macroscopic living creature. Nevertheless, microbial activity and growth status have always been challenging tasks to determine both in situ and in vivo. Microbial activity is generally related to growth, and the growth rate is a result of the availability of nutrients under adequate or adverse conditions faced by microbial cells in a changing environment. Most studies on microorganisms have been carried out under optimum or near-optimum growth conditions, but scarce information is available about microorganisms at slow-growing states (i.e., near-zero growth and maintenance metabolism). This study aims to better understand microorganisms under growth-limiting conditions. This is expected to provide new perspectives on the functions and relevance of the microbial world. This is because (i) microorganisms in nature frequently face conditions of severe growth limitation, (ii) microorganisms activate singular pathways (mostly genes remaining to be functionally annotated), resulting in a broad range of secondary metabolites, and (iii) the response of microorganisms to slow-growth conditions remains to be understood, including persistence strategies, gene expression, and cell differentiation both within clonal populations and due to the complexity of the environment.

An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach

The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.

How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome

A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies.

Fusarium oxysporum Disrupts Microbiome-Metabolome Networks in Arabidopsis thaliana Roots

While the plant host metabolome drives distinct enrichment of detrimental and beneficial members of the microbiome, the mechanistic interomics relationships remain poorly understood. Here, we studied microbiome and metabolome profiles of two Arabidopsis thaliana accessions after Fusarium oxysporum f.sp. mathioli (FOM) inoculation, Landsberg erecta (Ler-0) being susceptible and Col-0 being resistant against FOM. By using bacterial and fungal amplicon sequencing and targeted metabolite analysis, we observed highly dynamic microbiome and metabolome profiles across FOM host progression, while being markedly different between FOM-inoculated and noninoculated Col-0 and Ler-0. Co-occurrence network analysis revealed more robust microbial networks in the resistant Col-0 compared to Ler-0 during FOM infection. Correlation analysis revealed distinct metabolite-OTU correlations in Ler-0 compared with Col-0 which could possibly be explained by missense variants of the Rfo3 and Rlp2 genes in Ler-0. Remarkably, we observed positive correlations in Ler-0 between most of the analyzed metabolites and the bacterial phyla Proteobacteria, Bacteroidetes, Planctomycetes, Acidobacteria, and Verrucomicrobia, and negative correlations with Actinobacteria, Firmicutes, and Chloroflexi. The glucosinolates 4-methyoxyglucobrassicin, glucoerucin and indole-3 carbinol, but also phenolic compounds were strongly correlating with the relative abundances of indicator and hub OTUs and thus could be active in structuring the A. thaliana root-associated microbiome. Our results highlight interactive effects of host plant defense and root-associated microbiota on Fusarium infection and progression. Our findings provide significant insights into plant interomic dynamics during pathogen invasion and could possibly facilitate future exploitation of microbiomes for plant disease control. IMPORTANCE Plant health and fitness are determined by plant-microbe interactions which are guided by host-synthesized metabolites. To understand the orchestration of this interaction, we analyzed the distinct interomic dynamics in resistant and susceptible Arabidopsis ecotypes across different time points after infection with Fusarium oxysporum (FOM). Our results revealed distinct microbial profiles and network resilience during FOM infection in the resistant Col-0 compared with the susceptible Ler-0 and further pinpointed specific microbe-metabolite associations in the Arabidopsis microbiome. These findings provide significant insights into plant interomics dynamics that are likely affecting fungal pathogen invasion and could possibly facilitate future exploitation of microbiomes for plant disease control.

Temporal Alignment of Longitudinal Microbiome Data

A major challenge in working with longitudinal data when studying some temporal process is the fact that differences in pace and dynamics might overshadow similarities between processes. In the case of longitudinal microbiome data, this may hinder efforts to characterize common temporal trends across individuals or to harness temporal information to better understand the link between the microbiome and the host. One possible solution to this challenge lies in the field of “temporal alignment” – an approach for optimally aligning longitudinal samples obtained from processes that may vary in pace. In this work we investigate the use of alignment-based analysis in the microbiome domain, focusing on microbiome data from infants in their first years of life. Our analyses center around two main use-cases: First, using the overall alignment score as a measure of the similarity between microbiome developmental trajectories, and showing that this measure can capture biological differences between individuals. Second, using the specific matching obtained between pairs of samples in the alignment to highlight changes in pace and temporal dynamics, showing that it can be utilized to predict the age of infants based on their microbiome and to uncover developmental delays. Combined, our findings serve as a proof-of-concept for the use of temporal alignment as an important and beneficial tool in future longitudinal microbiome studies.

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.

Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System

Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”

Functional response regression model on correlated longitudinal microbiome sequencing data

Functional regression has been widely used on longitudinal data, but it is not clear how to apply functional regression to microbiome sequencing data. We propose a novel functional response regression model analyzing correlated longitudinal microbiome sequencing data, which extends the classic functional response regression model only working for independent functional responses. We derive the theory of generalized least squares estimators for predictors' effects when functional responses are correlated, and develop a data transformation technique to solve the computational challenge for analyzing correlated functional response data using existing functional regression method. We show by extensive simulations that our proposed method provides unbiased estimations for predictors' effect, and our model has accurate type I error and power performance for correlated functional response data, compared with classic functional response regression model. Finally we implement our method to a real infant gut microbiome study to evaluate the relationship of clinical factors to predominant taxa along time.

Time-course analysis of metabolomic and microbial responses in anaerobic digesters exposed to ammonia

Omics longitudinal studies are effective experimental designs to inform on the stability and dynamics of microbial communities in response to perturbations, but time-course analytical frameworks are required to fully exploit the temporal information acquired in this context. In this study we investigate the influence of ammonia on the stability of anaerobic digestion (AD) microbiome with a new statistical framework. Ammonia can severely reduce AD performance. Understanding how it affects microbial communities development and the degradation progress is a key operational issue to propose more stable processes. Thirty batch digesters were set-up with different levels of ammonia. Microbial community structure and metabolomic profiles were monitored with 16 S-metabarcoding and GCMS (gas-chromatography-mass-spectrometry). Digesters were first grouped according to similar degradation performances. Within each group, time profiles of OTUs and metabolites were modelled, then clustered into similar time trajectories, evidencing for example a syntrophic interaction between Syntrophomonas and Methanoculleus that was maintained up to 387 mg FAN/L. Metabolites resulting from organic matter fermentation, such as dehydroabietic or phytanic acid, decreased with increasing ammonia levels. Our analytical framework enabled to fully account for time variability and integrate this parameter in data analysis.

The rice histone methylation regulates hub species of the root microbiota

Plants have a close relationship with their root microbiota, which comprises a complex microbial network. Histone methylation is an important epigenetic modification influencing multiple plant traits; however, little is known about the role of plant histone methylation in the assembly and network structure of the root microbiota. In this study, we established that the rice (Oryza sativa) histone methylation regulates the structure and composition of the root microbiota, especially the hub species in the microbial network. DJ-jmj703 (defective in histone H3K4 demethylation) and ZH11-sdg714 (defective in H3K9 methylation) showed significant different root microbiota compared with the corresponding wild types at the phylum and family levels, with a consistent increase in the abundance of Betaproteobacteria and a decrease in the Firmicutes. In the root microbial network, 35 of 44 hub species in the top 10 modules in the tested field were regulated by at least one histone methylation-related gene. These observations establish that the rice histone methylation plays a pivotal role in regulating the assembly of the root microbiota, providing insights into the links between plant epigenetic regulation and root microbiota.

Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study

BACKGROUND

The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time.

RESULTS

We propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice.

CONCLUSIONS

The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data

MOTIVATION

Research shows that human microbiome is highly dynamic on longitudinal timescales, changing dynamically with diet, or due to medical interventions. In this paper, we propose a novel deep learning framework "phyLoSTM", using a combination of Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) for feature extraction and analysis of temporal dependency in longitudinal microbiome sequencing data along with host's environmental factors for disease prediction. Additional novelty in terms of handling variable timepoints in subjects through LSTMs, as well as, weight balancing between imbalanced cases and controls is proposed.

RESULTS

We simulated 100 datasets across multiple time points for model testing. To demonstrate the model's effectiveness, we also implemented this novel method into two real longitudinal human microbiome studies: (i) DIABIMMUNE three country cohort with food allergy outcomes (Milk, Egg, Peanut and Overall) (ii) DiGiulio study with preterm delivery as outcome. Extensive analysis and comparison of our approach yields encouraging performance with an AUC of 0.897 (increased by 5%) on simulated studies and AUCs of 0.762 (increased by 19%) and 0.713 (increased by 8%) on the two real longitudinal microbiome studies respectively, as compared to the next best performing method, Random Forest. The proposed methodology improves predictive accuracy on longitudinal human microbiome studies containing spatially correlated data, and evaluates the change of microbiome composition contributing to outcome prediction.

AVAILABILITY AND IMPLEMENTATION

https://github.com/divya031090/phyLoSTM.

Structural identifiability of the generalized Lotka-Volterra model for microbiome studies

Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

Comparable response of wild rodent gut microbiome to anthropogenic habitat contamination

Species identity is thought to dominate over environment in shaping wild rodent gut microbiota, but it remains unknown whether the responses of host gut microbiota to shared anthropogenic habitat impacts are species-specific or if the general gut microbiota response is similar across host species. Here, we compare the influence of exposure to radionuclide contamination on the gut microbiota of four wild mouse species: Apodemus flavicollis, A. sylvaticus, A. speciosus and A. argenteus. Building on the evidence that radiation impacts bank vole (Myodes glareolus) gut microbiota, we hypothesized that radiation exposure has a general impact on rodent gut microbiota. Because we sampled (n = 288) two species pairs of Apodemus mice that occur in sympatry in habitats affected by the Chernobyl and Fukushima nuclear accidents, these comparisons provide an opportunity for a general assessment of the effects of exposure to environmental contamination (radionuclides) on gut microbiota across host phylogeny and geographical areas. In general agreement with our hypothesis, analyses of bacterial 16S rRNA gene sequences revealed that radiation exposure alters the gut microbiota composition and structure in three of the four species of Apodemus mice. The notable lack of an association between the gut microbiota and soil radionuclide contamination in one mouse species from Fukushima (A. argenteus) probably reflects host "radiation escape" through its unique tree-dwelling lifestyle. The finding that host ecology can modulate effects of radiation exposure offers an interesting counterpoint for future analyses into effects of radiation or any other toxic exposure on host and its associated microbiota. Our data show that exposure to radionuclide contamination is linked to comparable gut microbiota responses across multiple species of rodents.

Cystic fibrosis airway microbiota associated with outcomes of nontuberculous mycobacterial infection

Rationale

Pulmonary infections with nontuberculous mycobacteria (NTM) are increasingly prevalent in people with cystic fibrosis (CF). Clinical outcomes following NTM acquisition are highly variable, ranging from transient self-resolving infection to NTM pulmonary disease associated with significant morbidity. Relationships between airway microbiota and variability of NTM outcomes in CF are unclear.

Objective

To identify features of CF airway microbiota associated with outcomes of NTM infection.

Methods

188 sputum samples, obtained from 24 subjects with CF, each with three or more samples collected from 3.5 years prior to, and up to 6 months following incident NTM infection, were selected from a sample repository. Sputum DNA underwent bacterial 16S rRNA gene sequencing. Airway microbiota were compared based on the primary outcome, a diagnosis of NTM pulmonary disease, using Wilcoxon rank-sum testing, autoregressive integrated moving average modelling and network analyses.

Measurements and main results

Subjects with and without NTM pulmonary disease were similar in clinical characteristics, including age and lung function at the time of incident NTM infection. Time-series analyses of sputum samples prior to incident NTM infection identified positive correlations between Pseudomonas, Streptococcus, Veillonella, Prevotella and Rothia with diagnosis of NTM pulmonary disease and with persistent NTM infection. Network analyses identified differences in clustering of taxa between subjects with and without NTM pulmonary disease, and between subjects with persistent versus transient NTM infection.

Conclusions

CF airway microbiota prior to incident NTM infection are associated with subsequent outcomes, including diagnosis of NTM pulmonary disease, and persistence of NTM infection. Associations between airway microbiota and NTM outcomes represent targets for validation as predictive markers and for future therapies.

Emerging computational tools and models for studying gut microbiota composition and function

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

From sequence to information

Today massive amounts of sequenced metagenomic and metatranscriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding of biological systems analysed from different perspectives is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data are of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data have been successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes

BACKGROUND

The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome.

RESULTS

For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation model to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein-Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein-Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein-Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics.

CONCLUSIONS

There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8-3.2 days.

Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Waterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.

Growth Stage-dependent Bacterial Communities in Soybean Plant Tissues: Methylorubrum Transiently Dominated in the Flowering Stage of the Soybean Shoot

Plant-associated bacteria are critical for plant growth and health. However, the effects of plant growth stages on the bacterial community remain unclear. Analyses of the microbiome associated with field-grown soybean revealed a marked shift in the bacterial community during the growth stages. The relative abundance of Methylorubrum in the leaf and stem increased from 0.2% to more than 45%, but decreased to approximately 15%, with a peak at the flowering stage at which nitrogen metabolism changed in the soybean plant. These results suggest the significance of a time-series analysis for understanding the relationship between the microbial community and host plant physiology.

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research

To date, most insights into the processes shaping vertebrate gut microbiomes have emerged from studies with cross-sectional designs. While this approach has been valuable, emerging time series analyses on vertebrate gut microbiomes show that gut microbial composition can change rapidly from 1 day to the next, with consequences for host physical functioning, health, and fitness. Hence, the next frontier of microbiome research will require longitudinal perspectives. Here we argue that primatologists, with their traditional focus on tracking the lives of individual animals and familiarity with longitudinal fecal sampling, are well positioned to conduct research at the forefront of gut microbiome dynamics. We begin by reviewing some of the most important ecological processes governing microbiome change over time, and briefly summarizing statistical challenges and approaches to microbiome time series analysis. We then introduce five questions of general interest to microbiome science where we think field-based primate studies are especially well positioned to fill major gaps: (a) Do early life events shape gut microbiome composition in adulthood? (b) Do shifting social landscapes cause gut microbial change? (c) Are gut microbiome phenotypes heritable across variable environments? (d) Does the gut microbiome show signs of host aging? And (e) do gut microbiome composition and dynamics predict host health and fitness? For all of these questions, we highlight areas where primatologists are uniquely positioned to make substantial contributions. We review preliminary evidence, discuss possible study designs, and suggest future directions.

An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data

BACKGROUND

The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass).

METHODS

We present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM).

RESULTS

BEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM's application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species.

CONCLUSIONS

BEEM addresses a key bottleneck in "systems analysis" of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.

Timescales of gut microbiome dynamics

Vast communities of microorganisms inhabit the gastrointestinal tracts of humans and other animals. Understanding their initial development, fluctuations in composition, stability over long times, and responses to transient perturbations - in other words their dynamics - is important both for gaining basic insights into these ecosystems and for rationally manipulating them for therapeutic ends. Gut microbiome dynamics, however, remain poorly understood. We review here studies of gut microbiome dynamics in the presence and absence of external perturbations, noting especially the long timescales associated with overall stability and the short timescales associated with various underlying biological processes. Integrating these disparate timescales, we suggest, is an important goal for future work and is necessary for developing a predictive understanding of microbiome dynamics.

Modeling the temporal dynamics of the gut microbial community in adults and infants

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

The ability to characterize and predict temporal trajectories of the microbial community in the human gut is crucial to our understanding of the structure and functions of this ecosystem. In this study we develop MTV-LMM, a method for modeling time-series microbial community data. Using MTV-LMM we find that in contrast to previous reports, a considerable portion of microbial taxa in both infants and adults display temporal structure that is predictable using the previous composition of the microbial community. In reaching this conclusion we have adopted a number of concepts common in statistical genetics for use with longitudinal microbiome studies. We introduce concepts such as time-explainability and the temporal kinship matrix, which we believe will be of use to other researchers studying microbial dynamics, through the framework of linear mixed models. In particular we find that the association matrix estimated by MTV-LMM reveals known phylogenetic relationships and that the temporal kinship matrix uncovers known temporal structure in infant microbiome and inter-individual differences in adult microbiome. Finally, we demonstrate that MTV-LMM significantly outperforms commonly used methods for temporal modeling of the microbiome, both in terms of its prediction accuracy as well as in its ability to identify time-dependent taxa.

From hairballs to hypotheses-biological insights from microbial networks

Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.

MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies

BACKGROUND

Microbial longitudinal studies are powerful experimental designs utilized to classify diseases, determine prognosis, and analyze microbial systems dynamics. In longitudinal studies, only identifying differential features between two phenotypes does not provide sufficient information to determine whether a change in the relative abundance is short-term or continuous. Furthermore, sample collection in longitudinal studies suffers from all forms of variability such as a different number of subjects per phenotypic group, a different number of samples per subject, and samples not collected at consistent time points. These inconsistencies are common in studies that collect samples from human subjects.

RESULTS

We present MetaLonDA, an R package that is capable of identifying significant time intervals of differentially abundant microbial features. MetaLonDA is flexible such that it can perform differential abundance tests despite inconsistencies associated with sample collection. Extensive experiments on simulated datasets quantitatively demonstrate the effectiveness of MetaLonDA with significant improvement over alternative methods. We applied MetaLonDA to the DIABIMMUNE cohort ( https://pubs.broadinstitute.org/diabimmune ) substantiating significant early lifetime intervals of exposure to Bacteroides and Bifidobacterium in Finnish and Russian infants. Additionally, we established significant time intervals during which novel differentially relative abundant microbial genera may contribute to aberrant immunogenicity and development of autoimmune disease.

CONCLUSION

MetaLonDA is computationally efficient and can be run on desktop machines. The identified differentially abundant features and their time intervals have the potential to distinguish microbial biomarkers that may be used for microbial reconstitution through bacteriotherapy, probiotics, or antibiotics. Moreover, MetaLonDA can be applied to any longitudinal count data such as metagenomic sequencing, 16S rRNA gene sequencing, or RNAseq. MetaLonDA is publicly available on CRAN ( https://CRAN.R-project.org/package=MetaLonDA ).