Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Role and significance of virus-bacteria interactions in disease progression

Understanding disease pathogenesis caused by bacteria/virus, from the perspective of individual pathogen has provided meaningful insights. However, as viral and bacterial counterparts might inhabit the same infection site, it becomes crucial to consider their interactions and contributions in disease onset and progression. The objective of the review is to highlight the importance of considering both viral and bacterial agents during the course of coinfection. The review provides a unique perspective on the general theme of virus-bacteria interactions, which either lead to colocalized infections that are restricted to one anatomical niche, or systemic infections that have a systemic effect on the human host. The sequence, nature, and underlying mechanisms of certain virus-bacteria interactions have been elaborated with relevant examples from literature. It also attempts to address the various applied aspects, including diagnostic and therapeutic strategies for individual infections as well as virus-bacteria coinfections. The review aims to aid researchers in comprehending the intricate interplay between virus and bacteria in disease progression, thereby enhancing understanding of current methodologies and empowering the development of novel health care strategies to tackle coinfections.

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

Metabolic model-based ecological modeling for probiotic design

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

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.

Cyclic pairwise interaction representing a rock-paper-scissors game maintains the population of the vulnerable yeast Saccharomyces cerevisiae within a multispecies sourdough microbiome

IMPORTANCE

Traditionally, multispecies consisting of lactic acid bacteria and yeasts collaboratively engage sourdough fermentation, which determines the quality of the resulting baked goods. Nonetheless, the successive transfer of these microbial communities can result in undesirable community dynamics that prevent the formation of high-quality sourdough bread. Thus, a mechanistic understanding of the community dynamics is fundamental to engineer sourdough complex fermentation. This study describes the population dynamics of five species of lactic acid bacteria-yeast communities in vitro using a generalized Lotka-Volterra model that examines interspecies interactions. A vulnerable yeast species was maintained within up to five species community dynamics by obtaining support with a cyclic interspecies interaction. Metaphorically, it involves a rock-paper-scissors game between two lactic acid bacteria species. Application of the generalized Lotka-Volterra model to real food microbiomes including sourdoughs will increase the reliability of the model prediction and help identify key microbial interactions that drive microbiome dynamics.

Resolving metabolic interaction mechanisms in plant microbiomes

Metabolic interactions are fundamental to the assembly and functioning of microbiomes, including those of plants. However, disentangling the molecular basis of these interactions and their specific roles remains a major challenge. Here, we review recent applications of experimental and computational methods toward the elucidation of metabolic interactions in plant-associated microbiomes. We highlight studies that span various scales of taxonomic and environmental complexity, including those that test interaction outcomes in vitro and in planta by deconstructing microbial communities. We also discuss how the continued integration of multiple methods can further reveal the general ecological characteristics of plant microbiomes, as well as provide strategies for applications in areas such as improved plant protection, bioremediation, and sustainable agriculture.

New perspectives into the vaginal microbiome with systems biology

The vaginal microbiome (VMB) is critical to female reproductive health; however, the mechanisms associated with optimal and non-optimal states remain poorly understood due to the complex community structure and dynamic nature. Quantitative systems biology techniques applied to the VMB have improved understanding of community composition and function using primarily statistical methods. In contrast, fewer mechanistic models that use a priori knowledge of VMB features to develop predictive models have been implemented despite their use for microbiomes at other sites, including the gastrointestinal tract. Here, we explore systems biology approaches that have been applied in the VMB, highlighting successful techniques and discussing new directions that hold promise for improving understanding of health and disease.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

MiSDEED: a synthetic data engine for microbiome study power analysis and study design

Summary

MiSDEED (Microbial Synthetic Data Engine for Experimental Design) is a command-line tool for generating synthetic longitudinal multinode data from simulated microbial environments. It generates relative-abundance timecourses under perturbations for an arbitrary number of time points, samples, locations and data types. All simulation parameters are exposed to the user to facilitate rapid power analysis and aid in study design. Users who want additional flexibility may also use MiSDEED as a Python package.

Availability and implementation

MiSDEED is written in Python and is freely available at https://github.com/pchlenski/misdeed.

Limited Pairwise Synergistic and Antagonistic Interactions Impart Stability to Microbial Communities

One of the central goals of ecology is to explain and predict coexistence of species. In this context, microbial communities provide a model system where community structure can be studied in environmental niches and in laboratory conditions. A community of microbial population is stabilized by interactions between participating species. However, the nature of these stabilizing interactions has remained largely unknown. Theory and experiments have suggested that communities are stabilized by antagonistic interactions between member species, and destabilized by synergistic interactions. However, experiments have also revealed that a large fraction of all the interactions between species in a community are synergistic in nature. To understand the relative significance of the two types of interactions (synergistic vs. antagonistic) between species, we perform simulations of microbial communities with a small number of participating species using two frameworks—a replicator equation and a Lotka-Volterra framework. Our results demonstrate that synergistic interactions between species play a critical role in maintaining diversity in cultures. These interactions are critical for the ability of the communities to survive perturbations and maintain diversity. We follow up the simulations with quantification of the extent to which synergistic and antagonistic interactions are present in a bacterial community present in a soil sample. Overall, our results show that community stability is largely achieved with the help of synergistic interactions between participating species. However, we perform experiments to demonstrate that antagonistic interactions, in specific circumstances, can also contribute toward community stability.

Exploring changes in bacterioplankton community structure in response to tannic acid, a major component of mangrove litterfall of Sundarbans mangrove ecosystem: a laboratory mesocosm approach

Tannic acid is a secondary compound produced by vascular plants and is a major component of mangrove litterfall. Tannic acid is water soluble, leaches out from mangrove litterfall and contributes to DOC and DON pools in adjacent estuaries. About 50% of the litterfall may be degraded and channelized into the marine microbial loop. The influence of tannic acid on bacterioplankton community structure was tested by setting up laboratory-based barrel experiments. Estuarine water from Stn3 of Sundarbans Biological Observatory Time Series (SBOTS) was enriched with tannic acid, and the change in concentration of dissolved nutrients was determined on a daily basis over a span of 15 days. Concentrations of tannic acid, gallic acid and other dissolved nutrients such as nitrate and ortho-phosphate were determined using a UV-Vis spectrophotometer. Tannic acid significantly affected the concentrations of gallic acid and dissolved nitrate in the barrels. Degradation of tannic acid was tracked by a decrease in concentration of tannic acid and generation of gallic acid. The influence of tannic acid on bacterioplankton community structure was analysed on the start (day 0), intermediate (day 3, day 5, day 7 and day 9) and end (day 15) of the experiment. Bacterioplankton community structure was elucidated by sequencing the V3-V4 region of 16S ribosomal RNA on an Illumina MiSeq platform. Proteobacteria was found to be the most dominant bacterial phylum in control and tannic acid-enriched barrels (barrels 1 and 2) on day 0. With the progression of experiment, the abundance of Proteobacteria altered significantly in the control barrel indicating the possible role of this phylum in the breakdown of tannic acid within estuarine mangroves. The abundance of Proteobacteria in the tannic acid-enriched barrels remained high, indicating that members of Proteobacteria may be capable of using tannic acid as a source of carbon and nitrogen. Tannic acid appeared to inhibit most of the other bacterioplankton phyla including Actinobacteria, Acidobacteria and Verrucomicrobia that existed in large abundance in the control barrel on day 15 but were almost absent in the tannic acid-enriched barrels. At class level, Bacteroides was found to be present in highest abundance in the tannic acid-enriched barrels. Tannic acid appeared to strongly influence the abundant bacterioplankton phyla and families as indicated by Pearson's correlation coefficient and non-metric multidimensional scaling ordination plots. Gallic acid is one of the final products of tannic acid degradation. Breakdown of tannic acid could influence the marine nitrogen and carbon cycling by releasing DON and DOC, respectively, into the adjacent estuaries. Information of breakdown and remineralization of components of litterfall such as tannic acid would also be important for calculation of carbon and nitrogen budgets of coastal ecosystems including in mangroves.

Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

An efficient and scalable top-down method for predicting structures of microbial communities

Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.

Network analysis methods for studying microbial communities: A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Experimental and computational approaches to unravel microbial community assembly

Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.

Correction to: Visual exploration of microbiome data

Correction to: J Biosci (2019) 44:119 https://doi.org/10.1007/s12038-019-9933-z In the October 2019 Special Issue of the Journal of Biosciences on Current Trends in Microbiome Research, in the Review article titled "Visual exploration of microbiome data" by Bhusan K. Kuntal and Sharmila S. Mande (DOI: 10.1007/s12038-019-9933-z; Vol. 44, Article No. 119), affiliation 3 for Bhusan K. Kuntal was incorrectly mentioned as "Academy of Scientific and Innovative Research, CSIR-National Chemical Laboratory Campus, Pune 411008, India''. The correct affiliation should read as ''Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India".

Chemically Stressed Bacterial Communities in Anaerobic Digesters Exhibit Resilience and Ecological Flexibility

Anaerobic digestion is a technology known for its potential in terms of methane production. During the digestion process, multiple metabolites of high value are synthesized. However, recent works have demonstrated the high robustness and resilience of the involved microbiomes; these attributes make it difficult to manipulate them in such a way that a specific metabolite is predominantly produced. Therefore, an exact understanding of the manipulability of anaerobic microbiomes may open up a treasure box for bio-based industries. In the present work, the effect of nalidixic acid, γ-aminobutyric acid (GABA), and sodium phosphate on the microbiome of digested sewage sludge from a water treatment plant fed with glucose was investigated. Despite of the induced process perturbations, high stability was observed at the phylum level. However, strong variations were observed at the genus level, especially for the genera Trichococcus, Candidatus Caldatribacterium, and Phascolarctobacterium. Ecological interactions were analyzed based on the Lotka–Volterra model for Trichococcus, Rikenellaceae DMER64, Sedimentibacter, Candidatus Cloacimonas, Smithella, Cloacimonadaceae W5 and Longilinea. These genera dynamically shifted among positive, negative or no correlation, depending on the applied stressor, which indicates a surprisingly dynamic behavior. Globally, the presented work suggests a massive resilience and stability of the methanogenic communities coupled with a surprising flexibility of the particular microbial key players involved in the process.

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.

Visual exploration of microbiome data

A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improved understanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has been developed to decipher microbial biomarkers for various diseases, cluster different ecosystems based on microbial content, and infer functional potential of the microbiome as well as analyze its temporal behavior. Development of user-friendly visualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patterns corresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance in recent times pertaining to the huge volume of the generated microbiome data. In this review, we present a brief overview of some useful visualization techniques that have significantly enriched microbiome data analytics.