The human microbiome: at the interface of health and disease.

In a majority of the solar/thermal studies to date, a utility economic methodology has been used to assess the potential of solar power systems. The utility sector is precluded from taking advantage of loan leveraging because the effective rate of return is artificially set. Utilities are regulated by public commissions and thus must finance new capital investments according to a prescribed set of rules on after tax cost of capital and fixed charge rates. Commercial ventures have no such externally imposed constraints and make decisions for capital expenditures which include the effect of loan leveraging. The relevant parameters for a commercial institution are interest rate on debt, a discount rate which accounts for risk, and the effect of favorable tax incentives. An expression is developed for a capital cost factor which contains these parameters. Results are shown for various downpayments and discount rates. It will be shown that the effect of loan leveraging can be substantial in affecting the penetration of solar process heat into the commercial energy market. In addition, the relation between loan leveraging and risk is investigated.

Current challenges for the study of population ecology of microtine rodents are reviewed. Comparisons with other taxonomic groups (other mammals, birds and insects) are given throughout. A major challenge is to link patterns and processes (i.e. mechanisms) better than is the case today. Other major challenges include the furthering of our understanding of the interaction between deterministic and stochastic processes, and as part thereof, the interaction between density‐dependent and density‐independent processes. The applicability of comparative studies on populations exhibiting different temporal dynamical patterns is, in this connection, emphasized. Understanding spatiotemporal dynamical patterns is another major challenge, not the least from a methodological point of view. Long‐term and large‐scale ecological data on population dynamics (in space and time) are critical for this purpose. Looking for consistency between hypothesized mechanisms and observed patterns is emphasized as a good platform for further empirical and theoretical work. The intellectual feedback process between different approaches to the study of microtine population ecology (observational studies, experimental manipulative studies, statistical modeling and mathematical modeling) are discussed. We recommend a pluralistic approach (involving both observational and experimental as well as theoretical studies) to the study of small rodent ecology.

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems. https://doi.org/10.7554/eLife.39733.001 Introduction Metabolism, in addition to enabling growth and homeostasis for individual microbes, contributes to the organization of complex, dynamic microbial communities. Within these communities, different microbes have diverse metabolic capabilities that lead to interactions driving microbial community structure and dynamics at multiple spatial and temporal scales (Ponomarova and Patil, 2015; Phelan et al., 2012; Watrous et al., 2013; Harcombe et al., 2014; Embree et al., 2015). For example, through cross-feeding, a compound produced by one species might benefit another, leading to a network of metabolic interdependences (Embree et al., 2015; Goldford et al., 2017; Mee et al., 2014; Pande et al., 2015; D'Souza et al., 2018; Zengler and Zaramela, 2018; Pacheco et al., 2019; Mee and Wang, 2012). This type of interaction has been proposed as one of the main reasons for the prevalence, in natural microbial communities, of uncultivated (or fastidious) microbes (Stewart, 2012; Epstein, 2013; Pande and Kost, 2017; Staley and Konopka, 1985). These microbes do not grow in pure culture on standard laboratory conditions as they may depend on diffusible metabolites produced by neighboring microbes (Pande and Kost, 2017). The prominence of uncultivated/fastidious microbial organisms across the tree of life and their potential importance in microbial community structure is highlighted by the recent identification of the candidate phyla radiation – a large branch of the tree of life consisting mainly of uncultivated organisms with small genomes and unique metabolic properties (Kantor et al., 2013; Brown et al., 2015; Hug et al., 2016). Efforts towards understanding this important component of microbial communities require further knowledge of metabolic interdependencies driven by biosynthetic deficiencies. Some of the most promising strides in understanding metabolic interdependences between microbes have been taken in the study of the human oral microbiome. The human oral microbiome serves as an excellent model system for microbial communities research, due to its importance for human health and ease of access for researchers (Dewhirst et al., 2010; Wade, 2013). For example, the order of colonization of species in dental plaque has been characterized physically (Kolenbrander et al., 2010) and metabolically (Mazumdar et al., 2013), and visualized microscopically (Mark Welch et al., 2016). The human oral microbiome consists of roughly 700 different microbial species, identified by 16S rRNA microbiome sequencing and cataloged in the human oral microbiome database (Dewhirst et al., 2010; Chen et al., 2010). Importantly, 63% of species in the human oral microbiome have been sequenced, including several uncultivated and recently-cultivated strains implicated in oral health and disease (Krishnan et al., 2017; Siqueira Jr and Rôças, 2013). Exciting recent work has led to successful laboratory co-cultivation of at least three previously uncultivated organisms, the Saccharibacteria (TM7) phylum taxa: Saccharibacteria bacterium HMT-952 strain TM7x (Bedree et al., 2018; He et al., 2015; Bor et al., 2016; Bor et al., 2018), Saccharibacteria bacterium HMT-488 strain AC001 (Collins et al., 2019a), and Saccharibacteria bacterium HMT-955 strain PM004 (Collins et al., 2019b). Saccharibacteria are prominent in the oral cavity and relevant for periodontal disease (Brinig et al., 2003; Ouverney et al., 2003). Due to their importance, they were among the first uncultivated organisms from the oral microbiome to be fully sequenced via single-cell sequencing methods (Marcy et al., 2007), and represent the first co-cultivated members of the candidate phyla radiation (He et al., 2015). Thus, their metabolic and phenotypic properties are of great interest for oral health and microbiology in general. In parallel to achieving laboratory growth of diverse and uncultivated bacteria, a major unresolved challenge is understanding the detailed metabolic mechanisms that may underlie their dependencies. Ideally, one would want to computationally predict, directly from the genome of an organism, its biosynthetic capabilities and deficiencies, so as to translate sequence information into mechanisms and community-level phenotypes (Widder et al., 2016). A number of approaches, based on computational analyses of metabolic networks, have contributed significant progress towards this goal (Schuster et al., 2000; Oberhardt et al., 2009; Lewis et al., 2012). At the heart of these methods are metabolic network reconstructions, formal encodings of the stoichiometry of all metabolic reactions in an organism, that are readily amenable to multiple types of in silico analyses and simulations (Feist et al., 2009). Recent exciting progress has led to the automated generation of ‘draft’ metabolic network reconstructions for any organism with a sequenced genome (Henry et al., 2010), opening the door for the quantitative study of large and diverse microbial communities. The most commonly used metabolic network analysis methods – flux balance analysis (FBA) (Orth et al., 2010a) and its dynamic version (dFBA) (Mahadevan et al., 2002) – have been extensively applied to study microbial communities (Harcombe et al., 2014; Embree et al., 2015; Pacheco et al., 2019; Magnúsdóttir et al., 2017; Magnúsdóttir and Thiele, 2018; Zarecki et al., 2014; Stolyar et al., 2007; Klitgord and Segrè, 2010; Freilich et al., 2011; Zelezniak et al., 2015; Cook and Nielsen, 2017; Biggs et al., 2015; Zomorrodi and Segrè, 2016). However, FBA and dFBA are not easily applicable to automatically-generated draft metabolic networks due to gaps (missing or incorrect reactions) in the metabolic network, and are thus difficult to scale to large and diverse microbial communities. Methods for ‘gap-filling’ draft reconstructions can address this problem, and ensemble methods potentially present a promising approach (Biggs and Papin, 2017; Machado et al., 2018). However, any gap-filling comes at the expense of an increased risk for false positive predictions. Additionally, gap-filling typically requires specific knowledge or assumptions on the growth media composition – which are often difficult to obtain for diverse environmental isolates and by definition unknown for uncultivated organisms. Alternatively, topology-based metabolic network analysis methods, such as network expansion (Ebenhöh et al., 2004) and NetSeed-based methods (Borenstein et al., 2008), are less dependent on gap-filling and have been applied to the analysis of draft metabolic reconstructions. These methods have provided valuable large-scale insight into metabolic processes in microbial communities, including the biosynthetic potentials of organisms and metabolites (Basler et al., 2008; Matthäus et al., 2008), the chance of cooperation or competition between species (Carr and Borenstein, 2012; Kreimer et al., 2012; Levy et al., 2015; Opatovsky et al., 2018), and the relationship between organisms and environment (Borenstein et al., 2008; Freilich et al., 2009; Handorf et al., 2008), for example in the human gut microbiome (Levy and Borenstein, 2013). While all of these approaches are promising, an additional issue that continues to limit the use of metabolic network analysis for prediction of biosynthetic capabilities is the difficulty of generating these predictions when the chemical environment of the microbes is unknown. In complex microbial communities, such as the human microbiome, the exact chemical composition of the environment is difficult to estimate, due both to the molecular complexity of the environment itself, and to the likely prevalence of secretions, lysing and cross-feeding within the community. Thus, the capacity to provide metabolic predictions based on unelaborated genome annotation, and on limited knowledge about an organism’s growth environment remains an important open challenge. Here we introduce a new method, which begins to address the above limitations, and provides a novel prediction of an organism’s biosynthetic capabilities. Our method applies a probabilistic approach to define and compute a metric that estimates which metabolites, such as biomass components, are robustly synthesized by a given metabolic network and which would likely need to be supplied from the environment/community. Discrepancies in these calculated estimates between organisms can be used to generate hypotheses regarding microbial auxotrophy and metabolic exchange in microbial communities. Importantly, our metric has the capacity to estimate biosynthetic capabilities in spite of uncertainty about environmental conditions by randomly sampling many different possible nutrient combinations. In this study, we first demonstrated our method on E. coli to clarify its performance and interpretation. Next, we applied our method to a large number of organisms from the human oral microbiome, and predicted broad trends in biosynthetic capabilities associated with taxonomy and microbial co-occurrence. We further focused our analysis on uncultivated microorganisms, including three recently co-cultivated Saccharibacteria (TM7) strains. In addition to highlighting their biosynthetic deficiencies, we developed specific hypotheses for their metabolic exchange with growth-supporting partner microbes. Analysis method Our newly developed method quantifies the robustness with which a given metabolic network can produce a given metabolite from variable metabolic precursors. In essence, we quantify a metabolic network specific metric for metabolite producibility by probabilistically sampling sets of possible environments. While the probabilistic sampling can be adjusted to reflect a specific environment, its power lies largely in the capacity to explicitly incorporate in a statistical way the lack of knowledge about environmental composition. The inspiration for this method comes from the statistical physics concept of percolation. Percolation theory has been applied in a wide range of fields, including the study of cascading metabolic failure upon gene deletions in metabolism (Smart et al., 2008; Barabási, 2015). In percolation theory the robustness of a network can be characterized by randomly adding or removing components (nodes or edges) of a network and assessing network connectivity (Barabási, 2015). The smaller the number of components that need to be randomly added to the network before it becomes connected, the more robust it is to perturbations. We utilized this concept to characterize the network robustness of a particular metabolic network towards producing a specified target metabolite by randomly adding input metabolites to the network and assessing the network’s ability to produce the target. To implement our method, we first introduced a probabilistic framework for analyzing metabolic networks (Figure 1 and Figure 1—figure supplement 1). In this framework, every metabolite can be considered to be drawn from a Bernoulli distribution, i.e. present in the network with a given input probability (Pin). These probabilities could represent beliefs about the environment, chances of metabolites being available from a host organism, or any arbitrary prior assumption on metabolite inputs. Throughout the majority of our analyses we have assigned Pin to be an identical value for all input metabolites. However, as illustrated in an example in our results section (Metabolite producibility in a protein vs. carbohydrate-enriched environment) this probabilistic framework can utilize Pin values that vary across metabolites. Following the assignment of Pin, the network structure is used to calculate the output probability (Pout) of some specified target metabolite. In practice, random sampling of probabilistically drawn input metabolite sets is used to calculate the probability of producing the target metabolite. For each random sample, a modified version of FBA (Orth et al., 2010a) is used to assess the network's ability to produce the target metabolite (for a complete explanation of how FBA is implemented in this context, see methods section: Algorithm functions, feas). Figure 1 with 2 supplements see all Download asset Open asset A probabilistic framework for calculating the producibility metric (PM). (A) Random samples of input metabolites are added to the metabolic network with probability Pin. Samples are shown here with gray or red circles. Sampled input metabolites are then used to calculate if a specified target output metabolite can be produced or not. Here the solid red circled sample leads to production of the target metabolite while the dotted gray circled samples do not. The probability of producing the target output metabolite (Pout) is calculated by taking many random samples at a specified Pin. (B) A producibility curve is calculated which represents Pout as function of Pin. Points along this curve are sampled by assigning the Pin value and estimating Pout. The Pin value at which Pout = 0.5 (Pin,0.5) is used to define the producibility metric (PM) as PM = 1-Pin,0.5. https://doi.org/10.7554/eLife.39733.002 Using the above probabilistic framework, we defined a novel metric quantifying biosynthetic capabilities, the producibility metric (PM) (Figure 1B). The PM is calculated as follows: First, a producibility curve describing Pout as a function of Pin is generated for a given metabolic network and metabolite target. This curve can be estimated by sampling input metabolites for different values of Pin (between 0 and 1), and calculating Pout. Next, we calculated the Pin value along the producibility curve at which Pout is equal to 0.5 (Pin,0.5, analogous to the Km in the Michaelis-Menten curve). Finally, PM is defined as PM = 1-Pin,0.5, such that larger PM values correspond to increased robustness. Our method calculates PM efficiently by random sampling and a nonlinear fitting algorithm (for details, see methods section: Algorithm functions calc_PM_fit_nonlin). In addition to calculating PM computationally for arbitrary metabolic networks and metabolites, we also derived a way to calculate PM analytically using combinatorial equations. The combinatorial equations are built up from simple scenarios to the most general in Figure 1—figure supplement 2. This analytical result, verified in detail for one specific pathway (Figure 2—figure supplement 2) clarifies the connection between our metric and the concept of minimal precursor sets (Andrade et al., 2016). It describes mathematically how the PM captures the multiplicity of routes through which a given target metabolite can be produced, and could serve as the basis for further theoretical work on the fundamental properties of metabolic networks. The algorithms used to implement our method are written in MATLAB and designed as a set of modular functions that interface with the COBRA toolbox – a popular metabolic modeling software compendium (Schellenberger et al., 2011; Heirendt et al., 2019). The methodology behind each function is further explained in the methods section. The code is freely available online at https://github.com/segrelab/biosynthetic_network_robustness (Bernstein, 2019; copy archived at https://github.com/elifesciences-publications/biosynthetic_network_robustness). Results Using the E. coli core metabolic network to demonstrate features of metabolite producibility Before applying our approach to the systematic study of genome-scale metabolic networks from the human oral microbiome, we used the model organism E. coli to illustrate the performance and interpretation of our method. We started with the E. coli core metabolic network, a simplified network consisting of central carbon metabolism and lacking peripheral metabolic pathways, such as amino acid or cofactor biosynthesis (Orth et al., 2010b). We calculated the PM for all intracellular metabolites in this network using a uniform ensemble of environments (as described in the methods). The results are shown in Figure 2A, overlaid on the E. coli core metabolic network itself, with each node’s color indicating its PM value and node size indicating its degree of connectivity. Consistent with the high connectivity of the E. coli core metabolic network, most metabolites have high PM values (PM >0.950). For example, the metabolites H+ and pyruvate are both highly connected in the metabolic network and have high PM (PM = 0.968 and 0.952 respectively). However, the network also contains several metabolites that are well connected, but have lower PM values. These include, for example, the cofactors AMP/ADP/ATP and NAD+/NADH, which have PM values of ~0.7 and ~0.5 respectively, because they can be produced from each other, but not biosynthesized in this network. The network also includes several examples of metabolites that are poorly connected but have high PM values. One example is D-lactate, which is produced only via Lactate Dehydrogenase from the high PM metabolites Pyruvate and H+ (Figure 2B). This reaction also consumes NADH and produces NAD+ but because these cofactors can be easily recycled from each other by a large number of different reactions, their relatively low PM (as described above) has minimal influence on the PM value of D-lactate (Figure 2B). This example demonstrates the fact that our metric captures metabolites which are easily produced because their precursors are easily produced, and that the PM of recycled cofactors has minimal influence on the PM of a target metabolite. Overall, there is also no significant correlation between the PM values and the node degree of a metabolite in the network (Figure 2—figure supplement 1), indicating that our metric describes a more complex property of a metabolite in a network that is not captured simply by node degree. Figure 2 with 2 supplements see all Download asset Open asset E. coli core metabolic network metabolite producibility. (A) The E. coli core metabolic network is represented as a bipartite graph with metabolites shown as circles and reactions shown as squares. Reactions shown with a black border are irreversible in the model, those with no border are reversible. All intracellular metabolites are colored based on their PM value (low – blue, high – red). Reactions and metabolite nodes are sized based on their total node degree. Several key metabolites of interest are highlighted with their corresponding PM values shown. Central metabolites such as H+ and Pyruvate have high degree and high PM. Cofactors such as AMP/ADP/ATP and NAD+/NADH have high degree but low PM, as they cannot be synthesized in this network. Oxygen is an example of a PM=0 metabolite that cannot be produced from any other metabolites in this network. D-lactate is an example of a metabolite with low degree and high PM that is it is easily produced but not well-connected. (B) The lactate dehydrogenase reaction producing D-Lactate is shown as an example to illustrate that poorly connected metabolites can display a high PM, and how recycled cofactors have minimal impact on PM values. Lactate dehydrogenase produces D-lactate and NAD+ from pyruvate, H+ and NADH. The metabolite D-lactate has high PM despite being produced only by this one reaction in the metabolic network because it can be produced from the high PM metabolites pyruvate and which are produced from a large number of possible precursors. NADH is also used to produce D-lactate, and has a relatively low PM in this core model, it has minimal impact on the PM of D-lactate as NADH can be recycled from NAD+ by a large number of reactions by the at the of the and thus production of NADH is not for the production of of metabolites from pathway and captures minimal precursor set structure We applied our method in detail to a specific biosynthetic pathway within a genome-scale model to demonstrate how our PM provides information that is can be from simply the of reactions present in a given biosynthetic we the biosynthetic pathway in the E. coli genome-scale metabolic network (Orth et al., and how the methods in their capacity to the of reaction along the pathway (Figure 2—figure supplement The PM is more pathway as it captures features the of reactions in the biosynthetic For different in the biosynthetic pathway distinct reactions) the PM is to the of the reaction from the target metabolite the would be the of for each (Figure 2—figure supplement 2B). This capacity of PM to of the of reactions in a pathway is also by a analysis of biosynthesis across all oral microbiome draft metabolic networks microbiome network and analysis described in the (Figure 2—figure supplement In in with the of the biosynthetic the PM on the pathway the number of different routes through which the target metabolite can be synthesized the minimal precursor et al., 2016). This property from the way the PM is and is explained by our combinatorial theory (Figure 1—figure supplement While our computational estimate of the PM is based on sampling the of possible precursor the combinatorial theory provides an exact value for the producibility of a with a given minimal precursor set structure. The between the PM and the combinatorial theory for the biosynthetic pathway (Figure 2—figure supplement that the PM captures the complex multiplicity of for producing a given metabolite. analysis to reactions to flux balance analysis One of the we to address with our method is the of robust about the metabolic capabilities of different organisms in spite of reactions – a often upon metabolic networks from newly sequenced To assess the performance of our approach in this context, we it with flux balance analysis (FBA) for a genome-scale metabolic networks with a given number of randomly In we applied both FBA and our method to the E. coli genome-scale metabolic network, which we by removing an number of randomly In this performance the metabolic network used as a which the predictions of our method and FBA on metabolic networks were Figure the of both FBA and the PM as a function of the of reactions from the metabolic network. While the output of our method PM for any is different from that of FBA flux through all one can use the PM values across all biomass components as a for the growth capacity of an organism, a metric that is with the biomass production The specific used to the PM and FBA predictions for biomass production are described further in the Figure One can see that both the FBA and the PM predictions as the metabolic networks are further However, the PM predictions are more to reactions the FBA predictions. While the FBA production of biomass becomes for the majority of the metabolic networks removing less of the reactions, the PM results when removing up to of the This analysis provides insight into the of our method for analyzing metabolic networks with such as draft metabolic networks produced through automated Figure with 1 supplement see all Download asset Open asset The of flux balance analysis and the producibility metric for different E. coli genome-scale metabolic networks. Reactions were randomly from the E. coli metabolic network generating different networks at different of reaction These networks were then with the producibility metric (PM) and flux balance analysis (FBA) in a minimal and complete The of the PM and FBA results were through different and as a function of the number of reactions on a (A) – The based on the of the between the network metric and the randomly network For FBA the as the value of the between the biomass flux of the network and the network. For the PM the calculated as the of the value of the between each PM The for both then and from one to a of The of different randomly networks at different reaction is shown with connected by solid on minimal FBA on complete The standard of the metric is shown as a the (B) production – The by the of randomly metabolic networks that were of producing For FBA this calculated as the of networks of producing biomass flux above of the biomass flux on minimal FBA on complete For the PM, the biomass production calculated as the of networks of producing all biomass components above a specified PM The PM or PM producibility to metabolic mechanisms for E. coli a first of our approach in its capacity to provide metabolic insight about of inter-microbial interactions, we used the PM to estimate the capacity of different E. coli to for each metabolic In we data from of E. coli from and with corresponding PM in silico the specific strains used in this work on the E. coli metabolic we calculated the PM for all biomass components in each and the PM values to the growth of (Figure supplement 1). by PM, were to based on the pathway of the and with in different of the biosynthetic pathway a in PM for the corresponding biomass to in our biosynthetic pathway analysis in Figure 2—figure supplement 2. The between PM values with that with different biosynthetic capabilities could each growth (Figure supplement Several examples and that further this are highlighted in Figure supplement and This analysis also the to in more the capacity of our approach to provide insight into with PM

Meta'omic Analytic Techniques for Studying the Intestinal Microbiome

The Intestinal Metabolome: An Intersection Between Microbiota and Host

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 565:95-111 (2017) - DOI: https://doi.org/10.3354/meps12043 Misidentification of megalopae as a potential source of error in studies of population genetics and ecology of the blue crab Callinectes sapidus Timothy J. Sullivan*, Joseph E. Neigel Department of Biology, University of Louisiana at Lafayette, Lafayette, LA 70503, USA *Corresponding author: tsulli1988@gmail.com ABSTRACT: Inaccuracy in taxonomic identification is an unknown but potentially important source of error in studies of planktonic larval ecology and evolution. We address the misidentification of blue crab (Callinectes sapidus) megalopae (post-larvae) as a source of error in investigations of genetic variation and factors influencing settlement. Callinectes spp. megalopae were sampled monthly in spring and summer from the water column at 2 locations on the Texas (USA) coast and identified by 16S mitochondrial sequences. Most of the megalopae could be assigned to C. sapidus (62%), C. similis (36%), C. rathbunae (1.5%), or C. danae (0.12%), while 5 (0.8%) were ambiguously grouped with both C. similis and C. danae. Previously used morphological characters (rostrum length, carapace length, and their ratio) were not diagnostic. Species composition differed between locations and among monthly samples. A recurring seasonal pattern in species composition was discerned, with ~95% C. similis in April shifting to ~95% C. sapidus by May/June, and variable proportions in August. This pattern strongly parallels changes in allozyme allele frequencies previously reported for blue crab megalopae at the same locations. Models selected by the Akaike information criterion indicated lunar phase, temperature, salinity, storms, and wind stress components all affecting megalopal abundance. The importance and sign of these factors differed between species. Temperature, the most important factor for each species analyzed separately, was not important when species were combined. This study demonstrates that misidentification of larvae could create the appearance of temporal genetic variation, inflate estimates of abundance, and obscure factors influencing settlement. KEY WORDS: Larval ecology · Larval invertebrates · Larval settlement · Gulf of Mexico · Callinectes spp. · 16S rRNA gene · Barcoding · Blue crab Full text in pdf format Supplementary material PreviousNextCite this article as: Sullivan TJ, Neigel JE (2017) Misidentification of megalopae as a potential source of error in studies of population genetics and ecology of the blue crab Callinectes sapidus. Mar Ecol Prog Ser 565:95-111. https://doi.org/10.3354/meps12043 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 565. Online publication date: February 17, 2017 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2017 Inter-Research.

The human genome codes for approximately 23,000 genes,1 yet some experts have suggested that the total information coded by the human genome alone is not enough to carry out all of the body’s biological functions.2 A growing number of studies suggest that part of what determines how the human body functions may be not only our own genes, but also the genes of the trillions of microorganisms that reside on and in our bodies. The genomes of the bacteria and viruses of the human gut alone are thought to encode 3.3 million genes.3 “The genetic richness and complexity of the bugs we carry is much richer than our own,” says Jayne Danska, an immunologist at the Hospital for Sick Children Research Institute in Ontario, Canada. “They serve as a buffer and interpreter of our environment. We are chimeric organisms.” Figure 1 False-color scanning electron micrograph shows the surface of the colon mucosa with pink clusters of rod-shaped bacteria, possibly Escherichia coli, attached. The genomes of the bacteria and viruses of the human gut alone are thought to encode 3.3 million ... A role for gut microbes in gastrointestinal function has been well documented since researchers first described differences in the fecal bacteria of people with inflammatory bowel disease.4 The molecular mechanisms responsible for the gut microbiome’s impact on metabolism and diseases throughout the body remain largely unknown. However, researchers are beginning to decipher how the microorganisms of the human intestinal tract influence biological functions beyond the gut and play a role in immunological, metabolic, and neurological diseases.

The agents we now know as bacteria have been known for centuries to play a key role in certain kinds of illnesses and ailments. But aside from infectious diseases, the communities of microbes we carry in specific parts of our bodies—our microbiomes—are a relatively new topic in human health. Now this field of study has taken an evolutionary leap forward with new research showing human microbiomes may play a far greater role in environ-mental health than ever imagined. The excitement around this field was obvious at a National Academy of Sciences (NAS) workshop on the interplay of the microbiomes, environ-mental agents, and human health held 27–28 April 2011,1 where talks by researchers working in this area inspired numerous “eureka!” moments. New findings about the ways in which human microbiomes transform arsenic and mercury—two of our most prevalent and well-defined external human health hazards—suggest the role of commensal bacteria may equal or exceed that of genetic polymorphisms that regulate metal transformations within the body, says Ellen Silbergeld, a professor of environmental health sciences at the Johns Hopkins University Bloomberg School of Public Health. The implications of these new insights are staggering. Environmental health scientists may need to expand the toxicokinetics of metals and other environmental agents, as well as associated biomarkers, to include the microbial component. “This is a huge thing that has never been thought of before in environmental health sciences,” Silbergeld told workshop attendees. Emerging findings also demand a re-examination of what it means to be exposed to environmental agents, Silbergeld says. To a toxicologist, she explains, a contaminant is only “in the body” once it has crossed from the external environment into circulating blood, or a cell, or an organ. But new findings suggest biologically relevant transformations may take place prior to absorption, when contaminants interact with the microbiome in the mouth, intestines, or other tissues. Because of the metabolic processes mediated by microbiomes, a great deal of what toxicologists attribute to human metabolism—such as methylation of arsenic—may actually take place at least in part before contaminants cross into the internal environment of our bodies.

Complex microbial communities, such as biofilms in the oral cavity and lumenal and mucosal communities in the gastrointestinal tract, play prominent roles in human health and disease [1]. Microbial communities in vivo include many different bacterial species that are in dynamic, intimate association with each other and with the human host. In humans, the intestinal microbiota is composed of well over 500 species [2], and the concept of humans as super-organisms [1,3] is highlighted by estimates that the human microbiome contains roughly 100 times as many genes as does the human genome. Increasingly, live microorganisms—probiotics—are being administered in order to promote human health. But much remains to be understood about the nature of the molecular interactions between newly arrived and resident microbial community members. Can microbial communities be effectively manipulated by administering defined dosages of a specific probiotic? How do probiotics affect the functional properties of indigenous microbial communities?

Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community's dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes. IMPORTANCE Microbial communities drive many processes which affect human well-being directly, as in the human microbiome, or indirectly, as in natural environments or in biotechnological applications. Due to their complexity, their dynamics over time is difficult to monitor, and current sequence-based approaches are limited with respect to the temporal resolution. However, in order to eventually control microbial community dynamics, monitoring schemes of high temporal resolution are required. Flow cytometry provides single-cell-based data in the required temporal resolution, and we here use such data to compute stability properties as easy to interpret univariate indicators of microbial community dynamics. Such monitoring tools will allow for a fast, continuous, and cost-effective screening of stability states of microbiomes. Applicable to various environments, including bioreactors, surface water, and the human body, it will contribute to the development of control schemes to manipulate microbial community structures and performances.

Prematurity and Perinatal Antibiotics: A Tale of Two Factors Influencing Development of the Neonatal Gut Microbiota

The Gut Microbiome in Health and Disease

Conservation and the microbiome.

Preface: The Human Genome and the Human Microbiome.- Chapter 1: The Human Genome, Microbiome and Disease.- Chapter 2: Host Genotype and the effect on Microbial Communities.- Chapter 3: The Human Microbiome and Host-Pathogen Interactions.- Chapter 4: The Human Virome.- Chapter 5: Selection and Sequencing of Strains as References for Human Microbiome studies.- Chapter 6: The Human Vaginal Microbiome.- Chapter 7: The Human Lung Microbiome.- Chapter 8: The Human Skin Microbiome in Health and Skin Diseases.- Chapter 9: The Human Oral metagenome.- Chapter 10: Infectogenomics: aspect of Host Responses to Microbes in the Digestive Tract.- Chapter 11: Autoimmune Disease and the Human Metagenome.- Chapter 12: Metagenomic applications and the potential for understanding chronic liver disease.- Chapter 13: Symbiotic gut microbiota and the modulation of human metabolic phenotypes.- Chapter 14: MetaHIT: The European Union Project on Metagenomics of the Human Intestinal Tract.- Chapter 15: Implications of Human Microbiome Research for the Developing World.

International audience