Microbial Communities as Experimental Units

Artificial ecosystem selection is an experimental technique that treats microbial communities as though they were discrete units by applying selection on community-level properties. Highly diverse microbial communities associated with humans and other organisms can have significant impacts on the health of the host. It is difficult to find correlations between microbial community composition and community-associated diseases, in part because it may be impossible to define a universal and robust species concept for microbes. Microbial communities are composed of potentially thousands of unique populations that evolved in intimate contact, so it is appropriate in many situations to view the community as the unit of analysis. This perspective is supported by recent discoveries using metagenomics and pangenomics. Artificial ecosystem selection experiments can be costly, but they bring the logical rigor of biological model systems to the emerging field of microbial community analysis.     

Applying trait‐based models to achieve functional targets for theory‐driven ecological restoration

Manipulating community assemblages to achieve functional targets is a key component of restoring degraded ecosystems. The response‐and‐effect trait framework provides a conceptual foundation for translating restoration goals into functional trait targets, but a quantitative framework has been lacking for translating trait targets into assemblages of species that practitioners can actually manipulate. This study describes new trait‐based models that can be used to generate ranges of species abundances to test theories about which traits, which trait values and which species assemblages are most effective for achieving functional outcomes. These models are generalisable, flexible tools that can be widely applied across many terrestrial ecosystems. Examples illustrate how the framework generates assemblages of indigenous species to (1) achieve desired community responses by applying the theories of environmental filtering, limiting similarity and competitive hierarchies, or (2) achieve desired effects on ecosystem functions by applying the theories of mass ratios and niche complementarity. Experimental applications of this framework will advance our understanding of how to set functional trait targets to achieve the desired restoration goals. A trait‐based framework provides restoration ecology with a robust scaffold on which to apply fundamental ecological theory to maintain resilient and functioning ecosystems in a rapidly changing world.     

Analysis of the Cystic Fibrosis Lung Microbiota via Serial Illumina Sequencing of Bacterial 16S rRNA Hypervariable Regions

The characterization of bacterial communities using DNA sequencing has revolutionized our ability to study microbes in nature and discover the ways in which microbial communities affect ecosystem functioning and human health. Here we describe Serial Illumina Sequencing (SI-Seq): a method for deep sequencing of the bacterial 16S rRNA gene using next-generation sequencing technology. SI-Seq serially sequences portions of the V5, V6 and V7 hypervariable regions from barcoded 16S rRNA amplicons using an Illumina short-read genome analyzer. SI-Seq obtains taxonomic resolution similar to 454 pyrosequencing for a fraction of the cost, and can produce hundreds of thousands of reads per sample even with very high multiplexing. We validated SI-Seq using single species and mock community controls, and via a comparison to cystic fibrosis lung microbiota sequenced using 454 FLX Titanium. Our control runs show that SI-Seq has a dynamic range of at least five orders of magnitude, can classify >96% of sequences to the genus level, and performs just as well as 454 and paired-end Illumina methods in estimation of standard microbial ecology diversity measurements. We illustrate the utility of SI-Seq in a pilot sample of central airway secretion samples from cystic fibrosis patients.     

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism. Understanding the networks that form the systems is of growing importance, e.g. to understand how species will respond to climate change, or to predict potential knock-on effects of a biological control agent. In recent years, a variety of summary statistics for characterising the global and local properties of such networks have been derived, which provide a measure for gauging the accuracy of a mathematical model for network formation processes. However, the critical underlying assumption is that the true network is known. This is not a straightforward task to accomplish, and typically requires minute observations and detailed field work. More importantly, knowledge about species interactions is restricted to specific kinds of interactions. For instance, while the interactions between pollinators and their host plants are amenable to direct observation, other types of species interactions, like those mentioned above, are not, and might not even be clearly defined from the outset. To discover information about complex ecological systems efficiently, new tools for inferring the structure of networks from field data are needed. In the present study, we investigate the viability of various statistical and machine learning methods recently applied in molecular systems biology: graphical Gaussian models, L1-regularised regression with least absolute shrinkage and selection operator (LASSO), sparse Bayesian regression and Bayesian networks. We have assessed the performance of these methods on data simulated from food webs of known structure, where we combined a niche model with a stochastic population model in a 2-dimensional lattice. We assessed the network reconstruction accuracy in terms of the area under the receiver operating characteristic (ROC) curve, which was typically in the range between 0.75 and 0.9, corresponding to the recovery of about 60% of the true species interactions at a false prediction rate of 5%. We also applied the models to presence/absence data for 39 European warblers, and found that the inferred species interactions showed a weak yet significant correlation with phylogenetic similarity scores, which tended to weakly increase when including bio-climate covariates and allowing for spatial autocorrelation. Our findings demonstrate that relevant patterns in ecological networks can be identified from large-scale spatial data sets with machine learning methods, and that these methods have the potential to contribute novel important tools for gaining deeper insight into the structure and stability of ecosystems.     

SuperFine: fast and accurate supertree estimation

Many research groups are estimating trees containing anywhere from a few thousands to hundreds of thousands of species, toward the eventual goal of the estimation of a Tree of Life, containing perhaps as many as several million leaves. These phylogenetic estimations present enormous computational challenges, and current computational methods are likely to fail to run even on data sets in the low end of this range. One approach to estimate a large species tree is to use phylogenetic estimation methods (such as maximum likelihood) on a supermatrix produced by concatenating multiple sequence alignments for a collection of markers; however, the most accurate of these phylogenetic estimation methods are extremely computationally intensive for data sets with more than a few thousand sequences. Supertree methods, which assemble phylogenetic trees from a collection of trees on subsets of the taxa, are important tools for phylogeny estimation where phylogenetic analyses based upon maximum likelihood (ML) are infeasible. In this paper, we introduce SuperFine, a meta-method that utilizes a novel two-step procedure in order to improve the accuracy and scalability of supertree methods. Our study, using both simulated and empirical data, shows that SuperFine-boosted supertree methods produce more accurate trees than standard supertree methods, and run quickly on very large data sets with thousands of sequences. Furthermore, SuperFine-boosted matrix representation with parsimony (MRP, the most well-known supertree method) approaches the accuracy of ML methods on supermatrix data sets under realistic conditions.     

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.     

The generation and maintenance of diversity in microbial communities

Microorganisms play a central role in the regulation of ecosystem processes, and they comprise the vast majority of species on Earth. With recent developments in molecular methods, it has become tractable to quantify the extent of microbial diversity in natural environments. Here we examine this revolution in our understanding of microbial diversity, and we explore the factors that contribute to the seemingly astounding numbers of microbial taxa found within individual environmental samples. We conducted a meta-analysis of bacterial richness estimates from a variety of ecosystems. Nearly all environments contained hundreds to thousands of bacterial taxa, and richness levels increased with the number of individuals in a sample, a pattern consistent with those reported for nonmicrobial taxa. A cursory comparison might suggest that bacterial richness far exceeds the richness levels typically observed for plant and animal taxa. However, the apparent diversity of bacterial communities is influenced by phylogenetic breadth and allometric scaling issues. When these features are taken into consideration, the levels of microbial diversity may appear less astounding. Although the fields of ecology and biogeography have traditionally ignored microorganisms, there are no longer valid excuses for neglecting microorganisms in surveys of biodiversity. Many of the concepts developed to explain plant and animal diversity patterns can also be applied to microorganisms once we reconcile the scale of our analyses to the scale of the organisms being observed. Furthermore, knowledge from microbial systems may provide insight into the mechanisms that generate and maintain species richness in nonmicrobial systems.     

Dynamics of the Microbiota in Response to Host Infection

Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions.     

A Novel Nutritional Predictor Links Microbial Fastidiousness with Lowered Ubiquity,Growth Rate,and Cooperativeness

Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect the adaptation of fastidious microorganisms to unique niches with few cohabitating species. They also explain how non-fastidious species inhabit many ecological niches with high abundance rates. Taken together, these results advance our understanding microbial nutrition on a large scale, by presenting new nutrition-related associations that govern the distribution of microorganisms in nature.     

我国深海微生物资源研发现状、挑战与对策

深海具有多种复杂独特的生境,蕴藏着极为丰富的物种多样性,被公认为未来重要的基因资源来源地,具有巨大的应用开发潜力。目前,深海微生物资源已成为国家重要的战略资源储备,也是各国海洋战略的核心关注点。预计在未来20年内,深海微生物资源将在多个领域得到产业化应用。我国在历时近二十年的深海微生物资源采探中,已分离鉴定并规范保藏大洋来源的菌株9 376株,分属于1 443个种444个属,实现了一定的资源积累,并快速提升了知识产权拥有量。但是,随着深海微生物资源获取和开发活动的快速发展,联合国大会"养护和可持续利用国家管辖范围以外海域的生物多样性"谈判进程趋紧,我国深海微生物资源领域依然存在采样区域较为局限、绝大多数微生物难以培养、资源量有待扩容、产业化推进机制缺失、知识产权占有量不足等问题。因此,本文认为应从加强政策引导、强化资源库的基础建设、建立产学研一体化机制、增强国际合作几个方面入手,实现我国深海生物资源产业由"跟跑者"到"领跑者"的跨越。     

The value of microbial diversity

In the past few years, due to the use of molecular methods, our knowledge of microbial diversity has increased dramatically, not only from a phylogenetic and taxonomic perspective but also from an ecological basis. We now know that microorganisms exist in every conceivable place on Earth, even in extreme environments. Temperature may be the only limitation as to where they can and cannot exist and/or function. As more small subunit rDNA sequence information becomes available there is a real need to start turning the information into knowledge that can be applied to better elucidate and understand structure-function relationships within ecosystems, develop new culturing methods, and discover new products and processes. It has been stated on numerous occasions that the 21(st) century is the century for biology. Within that context, we must address the real value of microbial diversity.     

Neotropical rainforest restoration: comparing passive,plantation and nucleation approaches

Neotropical rainforests are global biodiversity hotspots and are challenging to restore. A core part of this challenge is the very long recovery trajectory of the system: recovery of structure can take 20–190 years, species composition 60–500 years, and reestablishment of rare/endemic species thousands of years. Passive recovery may be fraught with instances of arrested succession, disclimax or emergence of novel ecosystems. In these cases, active restoration methods are essential to speed recovery and set a desired restoration trajectory. Tree plantation is the most common active approach to reestablish a high density of native tree species and facilitate understory regeneration. While this approach may speed the successional trajectory, it may not achieve, and possibly inhibit, a long-term restoration trajectory towards the high species diversity characteristic of these forests. A range of nucleation techniques (e.g., tree island planting) are important restoration options: although they may not speed recovery of structure as quickly as plantations, their emphasis on natural regeneration processes may enable greater and more natural patterns of diversity to develop. While more work needs to be done to compare forest restoration techniques in different environmental contexts, it appears that nucleation and, at times, passive restoration may best preserve the diverse legacy of these forested systems (both with lower costs). An integrated approach using both plantation productivity but also the natural functions associated with nucleation may develop composition and diversity trajectory desired in Neotropical conservation efforts.     

Biological Soil Crust Rehabilitation in Theory and Practice: An Underexploited Opportunity

Biological soil crusts (BSCs) are ubiquitous lichen–bryophyte microbial communities, which are critical structural and functional components of many ecosystems. However, BSCs are rarely addressed in the restoration literature. The purposes of this review were to examine the ecological roles BSCs play in succession models, the backbone of restoration theory, and to discuss the practical aspects of rehabilitating BSCs to disturbed ecosystems. Most evidence indicates that BSCs facilitate succession to later seres, suggesting that assisted recovery of BSCs could speed up succession. Because BSCs are ecosystem engineers in high abiotic stress systems, loss of BSCs may be synonymous with crossing degradation thresholds. However, assisted recovery of BSCs may allow a transition from a degraded steady state to a more desired alternative steady state. In practice, BSC rehabilitation has three major components: (1) establishment of goals; (2) selection and implementation of rehabilitation techniques; and (3) monitoring. Statistical predictive modeling is a useful method for estimating the potential BSC condition of a rehabilitation site. Various rehabilitation techniques attempt to correct, in decreasing order of difficulty, active soil erosion (e.g., stabilization techniques), resource deficiencies (e.g., moisture and nutrient augmentation), or BSC propagule scarcity (e.g., inoculation). Success will probably be contingent on prior evaluation of site conditions and accurate identification of constraints to BSC reestablishment. Rehabilitation of BSCs is attainable and may be required in the recovery of some ecosystems. The strong influence that BSCs exert on ecosystems is an underexploited opportunity for restorationists to return disturbed ecosystems to a desirable trajectory.     

Rotifers as predators on components of the microbial web (bacteria,heterotrophic flagellates,ciliates) — a review

Recent investigations have shown that processes within the planktonic microbial web are of great significance for the functioning of limnetic ecosystems. However, the general importance of protozoans and bacteria as food sources for rotifers, a major component of planktonic habitats, has seldom been evaluated. Results of feeding experiments and the analysis of the food size spectrum of rotifers suggest that larger bacteria, heterotrophic flagellates and small ciliates should be a common part of the food of most rotifer species. About 10–40 per cent of rotifers' food can consist of heterotrophic organisms of the microbial web. Field experiments have indicated that rotifer grazing should generally play a minor role in bacteria consumption compared to feeding by coexisting protozoans. However, according to recent experiments regarding food selection, rotifers should be efficient predators on protozoans. Laboratory experiments have revealed that even nanophagous rotifers can feed on ciliates. Preliminary microcosm and chemostat experiments have indicated that rotifers, due to their relatively low community grazing rates compared to the growth rates of bacteria and protozoans, should generally not be able (in contrast to some cladocerans) to suppress the microbial web via grazing, though they may structure it. Filter-feeding nanophagous rotifers (e.g. brachionids) seem to be significant feeders on the smaller organisms of the microbial web (bacteria, flagellates, small ciliates), whereas grasping species (e.g. synchaetids and asplanchnids) seem to be efficient predators on larger organisms (esp. ciliates). Another important role of rotifers is their feedback effect on the microbial web. Rotifers provide degraded algae, bacteria and protozoans to the microbial web and may promote microbial activity. Additional experimental work is necessary for a better understanding of the function of rotifers in aquatic ecosystems.     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.     

Marker-evaluated selection in rice: shifts in allele frequency among bulks selected in contrasting agricultural environments identify genomic regions of importance to rice adaptation and breeding

Conventional methods for quantitative trait locus (QTL) mapping require the selection of particular traits to be measured based on assumptions as to their importance. We have tested an alternative approach for the location of QTLs—marker-evaluated selection—that makes no prior assumptions as to which traits are important. The results of phenotype selection were evaluated in the products of modified bulk-population breeding that was replicated across a range of rice ecosystems. Selection was carried out in close collaboration with farmers in bulk populations that were all derived from a cross between an Indian upland variety (Kalinga III) and a high-yielding semi-dwarf variety (IR64). Twenty-seven diverse bulks were produced that were screened with molecular markers in order to determine whether shifts could be detected in marker allele frequency as a result of selection and if such changes varied by genomic region across ecosystems. Marker loci linked to important traits for adaptation to specific environments were identified without making any prior assumptions about which traits might be important. Genomic regions from Kalinga III were strongly selected in the upland environments and regions from IR64 in the lowland ones. However, exceptions occurred where the upland parent contributed positively to lowland adaptation and vice versa. The results can be used as a basis for the development of second-cycle varieties, using marker-assisted selection to produce genotypic ideotypes for specific target environments. The very strong selection for genomic regions from the adapted parents of the wide (upland × lowland) cross indicates that, in non-marker-assisted breeding, where genetically distant parents have been used, modified backcross breeding should be efficient. A single backcross to the adapted parent for a specific ecosystem will result in a higher frequency of segregants with the desired high genetic contribution from the adapted parent.     

Impact of Next Generation Sequencing Techniques in Food Microbiology

Understanding the Maxam-Gilbert and Sanger sequencing as the first generation, in recent years there has been an explosion of newly-developed sequencing strategies, which are usually referred to as next generation sequencing (NGS) techniques. NGS techniques have high-throughputs and produce thousands or even millions of sequences at the same time. These sequences allow for the accurate identification of microbial taxa, including uncultivable organisms and those present in small numbers. In specific applications, NGS provides a complete inventory of all microbial operons and genes present or being expressed under different study conditions. NGS techniques are revolutionizing the field of microbial ecology and have recently been used to examine several food ecosystems. After a short introduction to the most common NGS systems and platforms, this review addresses how NGS techniques have been employed in the study of food microbiota and food fermentations, and discusses their limits and perspectives. The most important findings are reviewed, including those made in the study of the microbiota of milk, fermented dairy products, and plant-, meat- and fish-derived fermented foods. The knowledge that can be gained on microbial diversity, population structure and population dynamics via the use of these technologies could be vital in improving the monitoring and manipulation of foods and fermented food products. They should also improve their safety.     

Fast-forward genetics enabled by new sequencing technologies

New sequencing technologies are dramatically accelerating progress in forward genetics, and the use of such methods for the rapid identification of mutant alleles will be soon routine in many laboratories. A straightforward extension will be the cloning of major-effect genetic variants in crop species. In the near future, it can be expected that mapping by sequencing will become a centerpiece in efforts to discover the genes responsible for quantitative trait loci. The largest impact, however, might come from the use of these strategies to extract genes from non-model, non-crop plants that exhibit heritable variation in important traits. Deployment of such genes to improve crops or engineer microbes that produce valuable compounds heralds a potential paradigm shift for plant biology.