Metagenomics meets time series analysis: unraveling microbial community dynamics

Trios-promising in silico biomarkers for differentiating the effect of disease on the human microbiome network

Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam's razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.

Toward a Biopsychosocial Ecology of the Human Microbiome, Brain-Gut Axis, and Health

OBJECTIVE

Rapidly expanding insights into the human microbiome and health suggest that Western medicine is poised for significant evolution, or perhaps revolution-this while medicine continues on a trajectory from reductionism to a biopsychosocial (BPS) paradigm recognizing biological, psychological, and social influences on health. The apparent sensitivity of the microbiota to perturbations across BPS domains suggests that a broad and inclusive framework is needed to develop applicable knowledge in this emerging area. We outline an ecological framework of the human microbiome by extending the BPS concept to better incorporate environmental and human factors as members of a global, dynamic set of systems that interact over time.

METHODS

We conducted a selective literature review across disciplines to integrate microbiome research into a BPS framework.

RESULTS

The microbiome can be understood in terms of ecological systems encompassing BPS domains at four levels: (a) immediate (molecular, genetic, and neural processes), (b) proximal (physiology, emotion, social integration), (c) intermediate (built environments, behaviors, societal practices), and (d) distal (physical environments, attitudes, and broad cultural, economic, and political factors). The microbiota and host are thus understood in terms of their immediate interactions and the more distal physical and social arenas in which they participate.

CONCLUSIONS

A BPS ecological paradigm encourages replicable, generalizable, and interdisciplinary/transdisciplinary research and practices that take into account the vast influences on the human microbiome that may otherwise be overlooked or understood out of context. It also underscores the importance of sustainable bioenvironmental, psychological, and social systems that broadly support microbial, neural, and general health.

Bringing the Dynamic Microbiome to Life with Animations

Our bodies and natural environment contain complex microbial communities, colloquially termed microbiomes. We previously created a web-based application, EMPeror, for visualizing ordinations derived from comparisons of these microbiome communities. We have now improved EMPeror to create interactive animations that connect successive samples to highlight patterns over time.

Human Gut Microbiota: Toward an Ecology of Disease

Composed of trillions of individual microbes, the human gut microbiota has adapted to the uniquely diverse environments found in the human intestine. Quickly responding to the variances in the ingested food, the microbiota interacts with the host via reciprocal biochemical signaling to coordinate the exchange of nutrients and proper immune function. Host and microbiota function as a unit which guards its balance against invasion by potential pathogens and which undergoes natural selection. Disturbance of the microbiota composition, or dysbiosis, is often associated with human disease, indicating that, while there seems to be no unique optimal composition of the gut microbiota, a balanced community is crucial for human health. Emerging knowledge of the ecology of the microbiota-host synergy will have an impact on how we implement antibiotic treatment in therapeutics and prophylaxis and how we will consider alternative strategies of global remodeling of the microbiota such as fecal transplants. Here we examine the microbiota-human host relationship from the perspective of the microbial community dynamics.

On the core bacterial flora of Ixodes persulcatus (Taiga tick)

Ixodes persulcatus is a predominant hard tick species that transmits a wide range of human and animal pathogens. Since bacterial flora of the tick dwelling in the wild always vary according to their hosts and the environment, it is highly desirable that species-associated microbiomes are fully determined by using next-generation sequencing and based on comparative metagenomics. Here, we examine such metagenomic changes of I. persulcatus starting with samples collected from the wild ticks and followed by the reared animals under pathogen-free laboratory conditions over multiple generations. Based on high-coverage genomic sequences from three experimental groups–wild, reared for a single generation or R1, and reared for eight generations or R8 –we identify the core bacterial flora of I. persulcatus, which contains 70 species that belong to 69 genera of 8 phyla; such a core is from the R8 group, which is reduced from 4625 species belonging to 1153 genera of 29 phyla in the wild group. Our study provides a novel example of tick core bacterial flora acquired based on wild-to-reared comparison, which paves a way for future research on tick metagenomics and tick-borne disease pandemics.

A Diverse Community To Study Communities: Integration of Experiments and Mathematical Models To Study Microbial Consortia

The last few years have seen the advancement of high-throughput experimental techniques that have produced an extraordinary amount of data. Bioinformatics and statistical analyses have become instrumental to interpreting the information coming from, e.g., sequencing data and often motivate further targeted experiments. The broad discipline of “computational biology” extends far beyond the well-established field of bioinformatics, but it is our impression that more theoretical methods such as the use of mathematical models are not yet as well integrated into the research studying microbial interactions. The empirical complexity of microbial communities presents challenges that are difficult to address with in vivo/in vitro approaches alone, and with microbiology developing from a qualitative to a quantitative science, we see stronger opportunities arising for interdisciplinary projects integrating theoretical approaches with experiments. Indeed, the addition of in silico experiments, i.e., computational simulations, has a discovery potential that is, unfortunately, still largely underutilized and unrecognized by the scientific community. This minireview provides an overview of mathematical models of natural ecosystems and emphasizes that one critical point in the development of a theoretical description of a microbial community is the choice of problem scale. Since this choice is mostly dictated by the biological question to be addressed, in order to employ theoretical models fully and successfully it is vital to implement an interdisciplinary view at the conceptual stages of the experimental design.

Bacterial Community Composition and Dynamics Spanning Five Years in Freshwater Bog Lakes

Lakes are excellent systems for investigating bacterial community dynamics because they have clear boundaries and strong environmental gradients. The results of our research demonstrate that bacterial community composition varies by year, a finding which likely applies to other ecosystems and has implications for study design and interpretation. Understanding the drivers and controls of bacterial communities on long time scales would improve both our knowledge of fundamental properties of bacterial communities and our ability to predict community states. In this specific ecosystem, bog lakes play a disproportionately large role in global carbon cycling, and the information presented here may ultimately help refine carbon budgets for these lakes. Finally, all data and code in this study are publicly available. We hope that this will serve as a resource for anyone seeking to answer their own microbial ecology questions using a multiyear time series.

Bacteria play a key role in freshwater biogeochemical cycling, but long-term trends in freshwater bacterial community composition and dynamics are not yet well characterized. We used a multiyear time series of 16S rRNA gene amplicon sequencing data from eight bog lakes to census the freshwater bacterial community and observe annual and seasonal trends in abundance. The sites that we studied encompassed a range of water column mixing frequencies, which we hypothesized would be associated with trends in alpha and beta diversity. Each lake and layer contained a distinct bacterial community, with distinct levels of richness and indicator taxa that likely reflected the environmental conditions of each lake type sampled, including Actinobacteria in polymictic lakes (i.e., lakes with multiple mixing events per year), Methylophilales in dimictic lakes (lakes with two mixing events per year, usually in spring and fall), and “Candidatus Omnitrophica” in meromictic lakes (lakes with no recorded mixing events). The community present during each year at each site was also surprisingly unique. Despite unexpected interannual variability in community composition, we detected a core community of taxa found in all lakes and layers, including Actinobacteria tribe acI-B2 and Betaprotobacteria lineage PnecC. Although trends in abundance did not repeat annually, each freshwater lineage within the communities had a consistent lifestyle, defined by persistence, abundance, and variability. The results of our analysis emphasize the importance of long-term multisite observations, as analyzing only a single year of data or one lake would not have allowed us to describe the dynamics and composition of these freshwater bacterial communities to the extent presented here.

IMPORTANCE Lakes are excellent systems for investigating bacterial community dynamics because they have clear boundaries and strong environmental gradients. The results of our research demonstrate that bacterial community composition varies by year, a finding which likely applies to other ecosystems and has implications for study design and interpretation. Understanding the drivers and controls of bacterial communities on long time scales would improve both our knowledge of fundamental properties of bacterial communities and our ability to predict community states. In this specific ecosystem, bog lakes play a disproportionately large role in global carbon cycling, and the information presented here may ultimately help refine carbon budgets for these lakes. Finally, all data and code in this study are publicly available. We hope that this will serve as a resource for anyone seeking to answer their own microbial ecology questions using a multiyear time series.

Temporal microbiota changes of high-protein diet intake in a rat model

Alterations of specific microbes serve as important indicators that link gut health with specific diet intake. Although a six-week high-protein diet (45% protein) upregulates the pro-inflammatory response and oxidative stress in colon of rats, the dynamic alteration of gut microbiota remains unclear. To dissect temporal changes of microbiota, dynamic analyses of fecal microbiota were conducted using a rat model. Adult rats were fed a normal-protein diet or an HPD for 6 weeks, and feces collected at different weeks were used for microbiota and metabolite analysis. The structural alteration of fecal microbiota was observed after 4 weeks, especially for the decreased appearance of bands related to Akkermansia species. HPD increased numbers of Escherichia coli while decreased Akkermansia muciniphila, Bifidobacterium, Prevotella, Ruminococcus bromii, and Roseburia/Eubacterium rectale (P < 0.05), compared to the normal-protein diet. HPD also decreased the copies of genes encoding butyryl-CoA:acetate CoA-transferase and Prevotella-associated methylmalonyl-CoA decarboxylase α-subunit (P < 0.05). The concentrations of acetate, propionate, and butyrate were decreased by HPD (P < 0.05). Additionally, HPD tended to decrease (P = 0.057) the concentration of IgG in the colonic lumen, which was positively correlated with fecal butyrate at week 6 (P < 0.05). Collectively, this study found the temporal alteration of fecal microbiota related to the decreased numbers and activity of propionate- and butyrate-producing bacteria in feces after the HPD. These findings may provide important reference for linking changes of specific fecal microbes with gut health under high-protein diet.

Multi-stability and the origin of microbial community types

The study of host-associated microbial community composition has suggested the presence of alternative community types. We discuss three mechanisms that could explain these observations. The most commonly invoked mechanism links community types to a response to environmental change; alternatively, community types were shown to emerge from interactions between members of local communities sampled from a metacommunity. Here, we emphasize multi-stability as a third mechanism, giving rise to different community types in the same environmental conditions. We illustrate with a toy model how multi-stability can generate community types and discuss the consequences of multi-stability for data interpretation.

Second Era of OMICS in Caries Research: Moving Past the Phase of Disillusionment

Novel approaches using OMICS techniques enable a collective assessment of multiple related biological units, including genes, gene expression, proteins, and metabolites. In the past decade, next-generation sequencing ( NGS) technologies were improved by longer sequence reads and the development of genome databases and user-friendly pipelines for data analysis, all accessible at lower cost. This has generated an outburst of high-throughput data. The application of OMICS has provided more depth to existing hypotheses as well as new insights in the etiology of dental caries. For example, the determination of complete bacterial microbiomes of oral samples rather than selected species, together with oral metatranscriptome and metabolome analyses, supports the viewpoint of dysbiosis of the supragingival biofilms. In addition, metabolome studies have been instrumental in disclosing the contributions of major pathways for central carbon and amino acid metabolisms to biofilm pH homeostasis. New, often noncultured, oral streptococci have been identified, and their phenotypic characterization has revealed candidates for probiotic therapy. Although findings from OMICS research have been greatly informative, problems related to study design, data quality, integration, and reproducibility still need to be addressed. Also, the emergence and continuous updates of these computationally demanding technologies require expertise in advanced bioinformatics for reliable interpretation of data. Despite the obstacles cited above, OMICS research is expected to encourage the discovery of novel caries biomarkers and the development of next-generation diagnostics and therapies for caries control. These observations apply equally to the study of other oral diseases.

Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks

We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth's major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.

Microbial communities of aquatic environments on Heard Island characterized by pyrotag sequencing and environmental data

Heard Island in the Southern Ocean is a biological hotspot that is suffering the effects of climate change. Significant glacier retreat has generated proglacial lagoons, some of which are open to the ocean. We used pyrotag sequencing of SSU rRNA genes and environmental data to characterize microorganisms from two pools adjacent to animal breeding areas, two glacial lagoons and Atlas Cove (marine site). The more abundant taxa included Actinobacteria, Bacteroidetes and Proteobacteria, ciliates and picoflagellates (e.g. Micromonas), and relatively few Archaea. Seal Pool, which is rich in organic matter, was characterized by a heterotrophic degradative community, while the less eutrophic Atlas Pool had more eucaryotic primary producers. Brown Lagoon, with the lowest nutrient levels, had Eucarya and Bacteria predicted to be oligotrophs, possess small cell sizes, and have the ability to metabolize organic matter. The marine influence on Winston Lagoon was evident by its salinity and the abundance of marine-like Gammaproteobacteria, while also lacking typical marine eucaryotes indicating the system was still functioning as a distinct niche. This is the first microbiology study of Heard Island and revealed that communities are distinct at each location and heavily influenced by local environmental factors.

Intestinal microbiome landscaping: insight in community assemblage and implications for microbial modulation strategies

High individuality, large complexity and limited understanding of the mechanisms underlying human intestinal microbiome function remain the major challenges for designing beneficial modulation strategies. Exemplified by the analysis of intestinal bacteria in a thousand Western adults, we discuss key concepts of the human intestinal microbiome landscape, i.e. the compositional and functional 'core', the presence of community types and the existence of alternative stable states. Genomic investigation of core taxa revealed functional redundancy, which is expected to stabilize the ecosystem, as well as taxa with specialized functions that have the potential to shape the microbiome landscape. The contrast between Prevotella- and Bacteroides-dominated systems has been well described. However, less known is the effect of not so abundant bacteria, for example, Dialister spp. that have been proposed to exhibit distinct bistable dynamics. Studies employing time-series analysis have highlighted the dynamical variation in the microbiome landscape with and without the effect of defined perturbations, such as the use of antibiotics or dietary changes. We incorporate ecosystem-level observations of the human intestinal microbiota and its keystone species to suggest avenues for designing microbiome modulation strategies to improve host health.

Disentangling Interactions in the Microbiome: A Network Perspective

Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

Two dynamic regimes in the human gut microbiome

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)—a multivariate method developed for econometrics—to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.

Dynamics reveal crucial information about how a system functions. In this study, we develop an approach for disentangling two types of dynamics within the human gut microbiome. We find that autoregressive dynamics involve recovery from large deviations in community structure. These recovery processes appear to involve the blooming of facultative anaerobes and aerotolerant taxa, likely due to transient shifts in redox potential, followed by re-establishment of obligate anaerobes. Non-autoregressive dynamics carry a strong phylogenetic signal, wherein highly related taxa fluctuate coherently. These non-autoregressive dynamics appear to be driven by external, non-autoregressive variables like diet. We find that most of the community variance is driven by day-to-day fluctuations in the environment, with occasional autoregressive dynamics as the system recovers from larger shocks. Despite frequently observed disruptions to the gut ecosystem, there exists a returning force that continually pushes the gut microbiome back towards its steady-state configuration.

Negative binomial mixed models for analyzing microbiome count data

Background

Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data.

Results

In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models.

Conclusions

We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data.

Impact of 6:2 fluorotelomer alcohol aerobic biotransformation on a sediment microbial community

Sediment microbial communities are responsible for many chemical biotransformation processes in the aquatic environment and play a critical role in various ecosystems and biogeochemical cycling. However, the impact of polyfluoroalkyl substances on sediment microbial communities remains unclear. These substances are increasingly being used in consumer and industrial products to replace environmentally persistent perfluoroalkyl substances. In this study, we investigated the effects of low (5mg/L) and high (15mg/L) doses of 6:2 fluorotelomer alcohol [6:2 FTOH, F(CF₂)₆CH₂CH₂OH] on the structure of a sediment microbial community. 6:2 FTOH biotransformation was rapid in the sediment mixture with a half-life <3days, regardless of the initial doses. After 28days, major products produced in the high dose condition included 28mol% 5:2 sFTOH [F(CF₂)₅CH(OH)CH₃], 9.6mol% 5:3 Acid [F(CF₂)₅CH₂CH₂COOH] and 11mol% PFHxA [F(CF₂)₅COOH], while 73mol% 5:2 sFTOH, 23mol% 5:3 Acid and 26mol% PFHxA were observed in the low dose condition. In the original (control) sediment without 6:2 FTOH dosing, Proteobacteria was the predominant microorganism (18%), followed by Chloroflexi (14%), Verrucomicrobia (13%), Firmicutes (3.4%), Bacterioidetes (2.4%), Actinobacteria (1.7%) and Planctomycetes (1.3%). The presence of 6:2 FTOH and the accumulation of transient transformation products in the sediment exerted selection pressure on the microbial taxonomic distribution and diversity. Our observations indicate that potential 6:2 FTOH degraders and tolerant strains, such as Dokdonella spp., Thauera spp., Albidovulum spp. and Caldanaerovirga spp., existed in the sediment mixture and began to dominate over time. This suggests that these genera might have higher tolerance towards elevated 6:2 FTOH and its transformation products. These findings on the characterization of sediment microbial community stability and dynamics will help predict changes in response to perfluoroalkyl and polyfluoroalkyl substances and also help identify robust microbial strains to degrade polyfluoroalkyl substances in the environment.

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.

Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions

Complex microbial communities inhabit vertebrate digestive systems but thorough understanding of the ecological dynamics and functions of host-associated microbiota within natural habitats is limited. We investigate the role of environmental conditions in shaping gut and skin microbiota under natural conditions by performing a field survey and reciprocal transfer experiments with salamander larvae inhabiting two distinct habitats (ponds and streams). We show that gut and skin microbiota are habitat-specific, demonstrating environmental factors mediate community structure. Reciprocal transfer reveals that gut microbiota, but not skin microbiota, responds differentially to environmental change. Stream-to-pond larvae shift their gut microbiota to that of pond-to-pond larvae, whereas pond-to-stream larvae change to a community structure distinct from both habitat controls. Predicted functions, however, match that of larvae from the destination habitats in both cases. Thus, microbial function can be matched without taxonomic coherence and gut microbiota appears to exhibit metagenomic plasticity.

Host-associated microbial communities can shift in structure or function when hosts change locations. Bletz et al. reciprocally transfer salamander larvae between pond and stream habitats to show that gut microbiomes shift in function, but not necessarily taxonomic identities, when hosts encounter a new environment.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Insights in Waste Management Bioprocesses Using Genomic Tools

Microbial capacities drive waste stabilization and resource recovery in environmental friendly processes. Depending on the composition of waste, a stress-mediated selection process ensures a scenario that generates a specific enrichment of microbial community. These communities dynamically change over a period of time while keeping the performance through the required utilization capacities. Depending on the environmental conditions, these communities select the appropriate partners so as to maintain the desired functional capacities. However, the complexities of these organizations are difficult to study. Individual member ratios and sharing of genetic intelligence collectively decide the enrichment and survival of these communities. The next-generation sequencing options with the depth of structure and function analysis have emerged as a tool that could provide the finer details of the underlying bioprocesses associated and shared in environmental niches. These tools can help in identification of the key biochemical events and monitoring of expression of associated phenotypes that will support the operation and maintenance of waste management systems. In this chapter, we link genomic tools with process optimization and/or management, which could be applied for decision making and/or upscaling. This review describes both, the aerobic and anaerobic, options of waste utilization process with the microbial community functioning as flocs, granules, or biofilms. There are a number of challenges involved in harnessing the microbial community intelligence with associated functional plasticity for efficient extension of microbial capacities for resource recycling and waste management. Mismanaged wastes could lead to undesired genotypes such as antibiotic/multidrug-resistant microbes.

A critical evaluation of ecological indices for the comparative analysis of microbial communities based on molecular datasets

In times of global change and intensified resource exploitation, advanced knowledge of ecophysiological processes in natural and engineered systems driven by complex microbial communities is crucial for both safeguarding environmental processes and optimising rational control of biotechnological processes. To gain such knowledge, high-throughput molecular techniques are routinely employed to investigate microbial community composition and dynamics within a wide range of natural or engineered environments. However, for molecular dataset analyses no consensus about a generally applicable alpha diversity concept and no appropriate benchmarking of corresponding statistical indices exist yet. To overcome this, we listed criteria for the appropriateness of an index for such analyses and systematically scrutinised commonly employed ecological indices describing diversity, evenness and richness based on artificial and real molecular datasets. We identified appropriate indices warranting interstudy comparability and intuitive interpretability. The unified diversity concept based on 'effective numbers of types' provides the mathematical framework for describing community composition. Additionally, the Bray-Curtis dissimilarity as a beta-diversity index was found to reflect compositional changes. The employed statistical procedure is presented comprising commented R-scripts and example datasets for user-friendly trial application.

Distinguishing the Signals of Gingivitis and Periodontitis in Supragingival Plaque: a Cross-Sectional Cohort Study in Malawi

Periodontal disease ranges from gingival inflammation (gingivitis) to the inflammation and loss of tooth-supporting tissues (periodontitis). Previous research has focused mainly on subgingival plaque, but supragingival plaque composition is also known to be associated with disease. Quantitative modeling of bacterial abundances across the natural range of periodontal severities can distinguish which features of disease are associated with particular changes in composition. We assessed a cross-sectional cohort of 962 Malawian women for periodontal disease and used 16S rRNA gene amplicon sequencing (V5 to V7 region) to characterize the bacterial compositions of supragingival plaque samples. Associations between bacterial relative abundances and gingivitis/periodontitis were investigated by using negative binomial models, adjusting for epidemiological factors. We also examined bacterial cooccurrence networks to assess community structure. The main differences in supragingival plaque compositions were associated more with gingivitis than periodontitis, including higher bacterial diversity and a greater abundance of particular species. However, even after controlling for gingivitis, the presence of subgingival periodontitis was associated with an altered supragingival plaque. A small number of species were associated with periodontitis but not gingivitis, including members of Prevotella, Treponema, and Selenomonas, supporting a more complex disease model than a linear progression following gingivitis. Cooccurrence networks of periodontitis-associated taxa clustered according to periodontitis across all gingivitis severities. Species including Filifactor alocis and Fusobacterium nucleatum were central to this network, which supports their role in the coaggregation of periodontal biofilms during disease progression. Our findings confirm that periodontitis cannot be considered simply an advanced stage of gingivitis even when only considering supragingival plaque.

IMPORTANCE

Periodontal disease is a major public health problem associated with oral bacteria. While earlier studies focused on a small number of periodontal pathogens, it is now accepted that the whole bacterial community may be important. However, previous high-throughput marker gene sequencing studies of supragingival plaque have largely focused on high-income populations with good oral hygiene without including a range of periodontal disease severities. Our study includes a large number of low-income participants with poor oral hygiene and a wide range of severities, and we were therefore able to quantitatively model bacterial abundances as functions of both gingivitis and periodontitis. A signal associated with periodontitis remains after controlling for gingivitis severity, which supports the concept that, even when only considering supragingival plaque, periodontitis is not simply an advanced stage of gingivitis. This suggests the future possibility of diagnosing periodontitis based on bacterial occurrences in supragingival plaque.

Absolute quantification of microbial taxon abundances

High-throughput amplicon sequencing has become a well-established approach for microbial community profiling. Correlating shifts in the relative abundances of bacterial taxa with environmental gradients is the goal of many microbiome surveys. As the abundances generated by this technology are semi-quantitative by definition, the observed dynamics may not accurately reflect those of the actual taxon densities. We combined the sequencing approach (16S rRNA gene) with robust single-cell enumeration technologies (flow cytometry) to quantify the absolute taxon abundances. A detailed longitudinal analysis of the absolute abundances resulted in distinct abundance profiles that were less ambiguous and expressed in units that can be directly compared across studies. We further provide evidence that the enrichment of taxa (increase in relative abundance) does not necessarily relate to the outgrowth of taxa (increase in absolute abundance). Our results highlight that both relative and absolute abundances should be considered for a comprehensive biological interpretation of microbiome surveys.

Metatranscriptomic evidence of pervasive and diverse chemolithoautotrophy relevant to C, S, N and Fe cycling in a shallow alluvial aquifer

Groundwater ecosystems are conventionally thought to be fueled by surface-derived allochthonous organic matter and dominated by heterotrophic microbes living under often-oligotrophic conditions. However, in a 2-month study of nitrate amendment to a perennially suboxic aquifer in Rifle (CO), strain-resolved metatranscriptomic analysis revealed pervasive and diverse chemolithoautotrophic bacterial activity relevant to C, S, N and Fe cycling. Before nitrate injection, anaerobic ammonia-oxidizing (anammox) bacteria accounted for 16% of overall microbial community gene expression, whereas during the nitrate injection, two other groups of chemolithoautotrophic bacteria collectively accounted for 80% of the metatranscriptome: (1) members of the Fe(II)-oxidizing Gallionellaceae family and (2) strains of the S-oxidizing species, Sulfurimonas denitrificans. Notably, the proportion of the metatranscriptome accounted for by these three groups was considerably greater than the proportion of the metagenome coverage that they represented. Transcriptional analysis revealed some unexpected metabolic couplings, in particular, putative nitrate-dependent Fe(II) and S oxidation among nominally microaerophilic Gallionellaceae strains, including expression of periplasmic (NapAB) and membrane-bound (NarGHI) nitrate reductases. The three most active groups of chemolithoautotrophic bacteria in this study had overlapping metabolisms that allowed them to occupy different yet related metabolic niches throughout the study. Overall, these results highlight the important role that chemolithoautotrophy can have in aquifer biogeochemical cycling, a finding that has broad implications for understanding terrestrial carbon cycling and is supported by recent studies of geochemically diverse aquifers.

Hidden drivers of low-dose pharmaceutical pollutant mixtures revealed by the novel GSA-QHTS screening method

The ecological impacts of emerging pollutants such as pharmaceuticals are not well understood. The lack of experimental approaches for the identification of pollutant effects in realistic settings (that is, low doses, complex mixtures, and variable environmental conditions) supports the widespread perception that these effects are often unpredictable. To address this, we developed a novel screening method (GSA-QHTS) that couples the computational power of global sensitivity analysis (GSA) with the experimental efficiency of quantitative high-throughput screening (QHTS). We present a case study where GSA-QHTS allowed for the identification of the main pharmaceutical pollutants (and their interactions), driving biological effects of low-dose complex mixtures at the microbial population level. The QHTS experiments involved the integrated analysis of nearly 2700 observations from an array of 180 unique low-dose mixtures, representing the most complex and data-rich experimental mixture effect assessment of main pharmaceutical pollutants to date. An ecological scaling-up experiment confirmed that this subset of pollutants also affects typical freshwater microbial community assemblages. Contrary to our expectations and challenging established scientific opinion, the bioactivity of the mixtures was not predicted by the null mixture models, and the main drivers that were identified by GSA-QHTS were overlooked by the current effect assessment scheme. Our results suggest that current chemical effect assessment methods overlook a substantial number of ecologically dangerous chemical pollutants and introduce a new operational framework for their systematic identification.

Spatial and Temporal Dynamics of Pacific Oyster Hemolymph Microbiota across Multiple Scales

Unveiling the factors and processes that shape the dynamics of host associated microbial communities (microbiota) under natural conditions is an important part of understanding and predicting an organism's response to a changing environment. The microbiota is shaped by host (i.e., genetic) factors as well as by the biotic and abiotic environment. Studying natural variation of microbial community composition in multiple host genetic backgrounds across spatial as well as temporal scales represents a means to untangle this complex interplay. Here, we combined a spatially-stratified with a longitudinal sampling scheme within differentiated host genetic backgrounds by reciprocally transplanting Pacific oysters between two sites in the Wadden Sea (Sylt and Texel). To further differentiate contingent site from host genetic effects, we repeatedly sampled the same individuals over a summer season to examine structure, diversity and dynamics of individual hemolymph microbiota following experimental removal of resident microbiota by antibiotic treatment. While a large proportion of microbiome variation could be attributed to immediate environmental conditions, we observed persistent effects of antibiotic treatment and translocation suggesting that hemolymph microbial community dynamics is subject to within-microbiome interactions and host population specific factors. In addition, the analysis of spatial variation revealed that the within-site microenvironmental heterogeneity resulted in high small-scale variability, as opposed to large-scale (between-site) stability. Similarly, considerable within-individual temporal variability was in contrast with the overall temporal stability at the site level. Overall, our longitudinal, spatially-stratified sampling design revealed that variation in hemolymph microbiota is strongly influenced by site and immediate environmental conditions, whereas internal microbiome dynamics and oyster-related factors add to their long-term stability. The combination of small and large scale resolution of spatial and temporal observations therefore represents a crucial but underused tool to study host-associated microbiome dynamics.

Metagenomic applications in environmental monitoring and bioremediation

With the rapid advances in sequencing technology, the cost of sequencing has dramatically dropped and the scale of sequencing projects has increased accordingly. This has provided the opportunity for the routine use of sequencing techniques in the monitoring of environmental microbes. While metagenomic applications have been routinely applied to better understand the ecology and diversity of microbes, their use in environmental monitoring and bioremediation is increasingly common. In this review we seek to provide an overview of some of the metagenomic techniques used in environmental systems biology, addressing their application and limitation. We will also provide several recent examples of the application of metagenomics to bioremediation. We discuss examples where microbial communities have been used to predict the presence and extent of contamination, examples of how metagenomics can be used to characterize the process of natural attenuation by unculturable microbes, as well as examples detailing the use of metagenomics to understand the impact of biostimulation on microbial communities.

Summer holidays as break-point in shaping a tannery sludge microbial community around a stable core microbiota

Recently, several investigations focused on the discovery of a bacterial consortium shared among different wastewater treatment plants (WWTPs). Nevertheless, the definition of a core microbiota over time represents the necessary counterpart in order to unravel the dynamics of bacterial communities in these environments. Here we performed a monthly survey on the bacterial community of a consortial industrial plant. Objectives of this study were: (1) to identify a core microbiota constant over time; (2) to evaluate the temporal dynamics of the community during one year. A conspicuous and diversified core microbiota is constituted by operational taxonomic units which are present throughout the year in the plant. Community composition data confirm that the presence and abundance of bacteria in WWTPs is highly consistent at high taxonomic level. Our results indicate however a difference in microbial community structure between two groups of samples, identifying the summer holiday period as the break-point. Changes in the structure of the microbial community occur otherwise gradually, one month after another. Further studies will clarify how the size and diversity of the core microbiota could affect the observed dynamics.

CoNet app: inference of biological association networks using Cytoscape

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

CoNet app: inference of biological association networks using Cytoscape

Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

Microbial interactions and community assembly at microscales

In most environments, microbial interactions take place within microscale cell aggregates. At the scale of these aggregates (∼100μm), interactions are likely to be the dominant driver of population structure and dynamics. In particular, organisms that exploit interspecific interactions to increase ecological performance often co-aggregate. Conversely, organisms that antagonize each other will tend to spatially segregate, creating distinct micro-communities and increased diversity at larger length scales. We argue that, in order to understand the role that biological interactions play in microbial community function, it is necessary to study microscale spatial organization with enough throughput to measure statistical associations between taxa and possible alternative community states. We conclude by proposing strategies to tackle this challenge.

A two-part mixed-effects model for analyzing longitudinal microbiome compositional data

MOTIVATION

The human microbial communities are associated with many human diseases such as obesity, diabetes and inflammatory bowel disease. High-throughput sequencing technology has been widely used to quantify the microbial composition in order to understand its impacts on human health. Longitudinal measurements of microbial communities are commonly obtained in many microbiome studies. A key question in such microbiome studies is to identify the microbes that are associated with clinical outcomes or environmental factors. However, microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures in longitudinal studies are correlated. A method that takes into account these features is needed for association analysis in longitudinal microbiome data.

RESULTS

In this paper, we propose a two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. The model includes a logistic regression component to model presence/absence of a microbe in the samples and a Beta regression component to model non-zero microbial abundance, where each component includes a random effect to account for the correlations among the repeated measurements on the same subject. Both simulation studies and the application to real microbiome data have shown that ZIBR model outperformed the previously used methods. The method provides a useful tool for identifying the relevant taxa based on longitudinal or repeated measures in microbiome research.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chvlyl/ZIBR CONTACT: hongzhe@upenn.edu.

Co-occurrence Analysis of Microbial Taxa in the Atlantic Ocean Reveals High Connectivity in the Free-Living Bacterioplankton

We determined the taxonomic composition of the bacterioplankton of the epipelagic zone of the Atlantic Ocean along a latitudinal transect (51°S–47°N) using Illumina sequencing of the V5-V6 region of the 16S rRNA gene and inferred co-occurrence networks. Bacterioplankon community composition was distinct for Longhurstian provinces and water depth. Free-living microbial communities (between 0.22 and 3 μm) were dominated by highly abundant and ubiquitous taxa with streamlined genomes (e.g., SAR11, SAR86, OM1, Prochlorococcus) and could clearly be separated from particle-associated communities which were dominated by Bacteroidetes, Planktomycetes, Verrucomicrobia, and Roseobacters. From a total of 369 different communities we then inferred co-occurrence networks for each size fraction and depth layer of the plankton between bacteria and between bacteria and phototrophic micro-eukaryotes. The inferred networks showed a reduction of edges in the deepest layer of the photic zone. Networks comprised of free-living bacteria had a larger amount of connections per OTU when compared to the particle associated communities throughout the water column. Negative correlations accounted for roughly one third of the total edges in the free-living communities at all depths, while they decreased with depth in the particle associated communities where they amounted for roughly 10% of the total in the last part of the epipelagic zone. Co-occurrence networks of bacteria with phototrophic micro-eukaryotes were not taxon-specific, and dominated by mutual exclusion (~60%). The data show a high degree of specialization to micro-environments in the water column and highlight the importance of interdependencies particularly between free-living bacteria in the upper layers of the epipelagic zone.

Functional Redundancy-Induced Stability of Gut Microbiota Subjected to Disturbance

The microbiota should be considered as just another component of the human epigenetic landscape. Thus, health is also a reflection of the diversity and composition of gut microbiota and its metabolic status. In defining host health, it remains unclear whether diversity is paramount, or whether greater weight is held by gut microbiota composition or mono- or multiple-functional capacity of the different taxa and the mechanisms involved. A network-biology approach may shed light on the key gut players acting to protect against, or promote, disorders or diseases. This could be achieved by integrating data on total and active species, proteins and molecules, and their association with host response. In this review, we discuss the utilization of top-down and bottom-up approaches, following a functional hierarchy perspective.

High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved

The systems biology of microbial communities, organismal communities inhabiting all ecological niches on earth, has in recent years been strongly facilitated by the rapid development of experimental, sequencing and data analysis methods. Novel experimental approaches and binning methods in metagenomics render the semi-automatic reconstructions of near-complete genomes of uncultivable bacteria possible, while advances in high-resolution amplicon analysis allow for efficient and less biased taxonomic community characterization. This will also facilitate predictive modeling approaches, hitherto limited by the low resolution of metagenomic data. In this review, we pinpoint the most promising current developments in metagenomics. They facilitate microbial systems biology towards a systemic understanding of mechanisms in microbial communities with scopes of application in many areas of our daily life.

Bacterial Electron Transfer Chains Primed by Proteomics

Electron transport phosphorylation is the central mechanism for most prokaryotic species to harvest energy released in the respiration of their substrates as ATP. Microorganisms have evolved incredible variations on this principle, most of these we perhaps do not know, considering that only a fraction of the microbial richness is known. Besides these variations, microbial species may show substantial versatility in using respiratory systems. In connection herewith, regulatory mechanisms control the expression of these respiratory enzyme systems and their assembly at the translational and posttranslational levels, to optimally accommodate changes in the supply of their energy substrates. Here, we present an overview of methods and techniques from the field of proteomics to explore bacterial electron transfer chains and their regulation at levels ranging from the whole organism down to the Ångstrom scales of protein structures. From the survey of the literature on this subject, it is concluded that proteomics, indeed, has substantially contributed to our comprehending of bacterial respiratory mechanisms, often in elegant combinations with genetic and biochemical approaches. However, we also note that advanced proteomics offers a wealth of opportunities, which have not been exploited at all, or at best underexploited in hypothesis-driving and hypothesis-driven research on bacterial bioenergetics. Examples obtained from the related area of mitochondrial oxidative phosphorylation research, where the application of advanced proteomics is more common, may illustrate these opportunities.

Predicting microbial interactions through computational approaches

Microorganisms play a vital role in various ecosystems and characterizing interactions between them is an essential step towards understanding the organization and function of microbial communities. Computational prediction has recently become a widely used approach to investigate microbial interactions. We provide a thorough review of emerging computational methods organized by the type of data they employ. We highlight three major challenges in inferring interactions using metagenomic survey data and discuss the underlying assumptions and mathematics of interaction inference algorithms. In addition, we review interaction prediction methods relying on metabolic pathways, which are increasingly used to reveal mechanisms of interactions. Furthermore, we also emphasize the importance of mining the scientific literature for microbial interactions - a largely overlooked data source for experimentally validated interactions.