Molecular eco-systems biology: towards an understanding of community function

RiboTagger: fast and unbiased 16S/18S profiling using whole community shotgun metagenomic or metatranscriptome surveys

Background

Taxonomic profiling of microbial communities is often performed using small subunit ribosomal RNA (SSU) amplicon sequencing (16S or 18S), while environmental shotgun sequencing is often focused on functional analysis. Large shotgun datasets contain a significant number of SSU sequences and these can be exploited to perform an unbiased SSU--based taxonomic analysis.

Results

Here we present a new program called RiboTagger that identifies and extracts taxonomically informative ribotags located in a specified variable region of the SSU gene in a high-throughput fashion.

Conclusions

RiboTagger permits fast recovery of SSU-RNA sequences from shotgun nucleic acid surveys of complex microbial communities. The program targets all three domains of life, exhibits high sensitivity and specificity and is substantially faster than comparable programs.

Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton

Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks.

Critical review on biofilm methods

Biofilms are widespread in nature and constitute an important strategy implemented by microorganisms to survive in sometimes harsh environmental conditions. They can be beneficial or have a negative impact particularly when formed in industrial settings or on medical devices. As such, research into the formation and elimination of biofilms is important for many disciplines. Several new methodologies have been recently developed for, or adapted to, biofilm studies that have contributed to deeper knowledge on biofilm physiology, structure and composition. In this review, traditional and cutting-edge methods to study biofilm biomass, viability, structure, composition and physiology are addressed. Moreover, as there is a lack of consensus among the diversity of techniques used to grow and study biofilms. This review intends to remedy this, by giving a critical perspective, highlighting the advantages and limitations of several methods. Accordingly, this review aims at helping scientists in finding the most appropriate and up-to-date methods to study their biofilms.

Methodologies for probing the metatranscriptome of grassland soil

Metatranscriptomics provides an opportunity to identify active microbes and expressed genes in complex soil communities in response to particular conditions. Currently, there are a limited number of soil metatranscriptome studies to provide guidance for using this approach in this challenging matrix. Hence, we evaluated the technical challenges of applying soil metatranscriptomics to a highly diverse, low activity natural system. We used a non-targeted rRNA removal approach, duplex nuclease specific (DSN) normalization, to generate a metatranscriptomic library from field collected soil supporting a perennial grass, Miscanthus x giganteus (a biofuel crop), and evaluated its ability to provide insight into its active community members and their expressed protein-coding genes. We also evaluated various bioinformatics approaches for analyzing our soil metatranscriptome, including annotation of unassembled transcripts, de novo assembly, and aligning reads to known genomes. Further, we evaluated various databases for their ability to provide annotations for our metatranscriptome. Overall, our results emphasize that low activity, highly genetically diverse and relatively stable microbiomes, like soil, requires very deep sequencing to sample the transcriptome beyond the common core functions. We identified several key areas that metatranscriptomic analyses will benefit from including increased rRNA removal, assembly of short read transcripts, and more relevant reference bases while providing a priority set of expressed genes for functional assessment.

Effects of captivity and artificial breeding on microbiota in feces of the red-crowned crane (Grus japonensis)

Reintroduction of the threatened red-crowned crane has been unsuccessful. Although gut microbiota correlates with host health, there is little information on gut microbiota of cranes under different conservation strategies. The study examined effects of captivity, artificial breeding and life stage on gut microbiota of red-crown cranes. The gut microbiotas of wild, captive adolescent, captive adult, artificially bred adolescent and artificially bred adult cranes were characterized by next-generation sequencing of 16S rRNA gene amplicons. The gut microbiotas were dominated by three phyla: Firmicutes (62.9%), Proteobacteria (29.9%) and Fusobacteria (9.6%). Bacilli dominated the 'core' community consisting of 198 operational taxonomic units (OTUs). Both captivity and artificial breeding influenced the structures and diversities microbiota of the gut. Especially, wild cranes had distinct compositions of gut microbiota from captive and artificially bred cranes. The greatest alpha diversity was found in captive cranes, while wild cranes had the least. According to the results of ordination analysis, influences of captivity and artificial breeding were greater than that of life stage. Overall, captivity and artificial breeding influenced the gut microbiota, potentially due to changes in diet, vaccination, antibiotics and living conditions. Metagenomics can serve as a supplementary non-invasive screening tool for disease control.

Evaluation of 16S rRNA Gene Primer Pairs for Monitoring Microbial Community Structures Showed High Reproducibility within and Low Comparability between Datasets Generated with Multiple Archaeal and Bacterial Primer Pairs

The application of next-generation sequencing technology in microbial community analysis increased our knowledge and understanding of the complexity and diversity of a variety of ecosystems. In contrast to Bacteria, the archaeal domain was often not particularly addressed in the analysis of microbial communities. Consequently, established primers specifically amplifying the archaeal 16S ribosomal gene region are scarce compared to the variety of primers targeting bacterial sequences. In this study, we aimed to validate archaeal primers suitable for high throughput next generation sequencing. Three archaeal 16S primer pairs as well as two bacterial and one general microbial 16S primer pairs were comprehensively tested by in-silico evaluation and performing an experimental analysis of a complex microbial community of a biogas reactor. The results obtained clearly demonstrate that comparability of community profiles established using different primer pairs is difficult. 16S rRNA gene data derived from a shotgun metagenome of the same reactor sample added an additional perspective on the community structure. Furthermore, in-silico evaluation of primers, especially those for amplification of archaeal 16S rRNA gene regions, does not necessarily reflect the results obtained in experimental approaches. In the latter, archaeal primer pair ArchV34 showed the highest similarity to the archaeal community structure compared to observed by the metagenomic approach and thus appears to be the appropriate for analyzing archaeal communities in biogas reactors. However, a disadvantage of this primer pair was its low specificity for the archaeal domain in the experimental application leading to high amounts of bacterial sequences within the dataset. Overall our results indicate a rather limited comparability between community structures investigated and determined using different primer pairs as well as between metagenome and 16S rRNA gene amplicon based community structure analysis. This finding, previously shown for Bacteria, was as well observed for the archaeal domain.

Bioinformatics Approach in Plant Genomic Research

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.

Longitudinal Analysis of Microbiota in Microalga Nannochloropsis salina Cultures

Large-scale open microalgae cultivation has tremendous potential to make a significant contribution to replacing petroleum-based fuels with biofuels. Open algal cultures are unavoidably inhabited with a diversity of microbes that live on, influence, and shape the fate of these ecosystems. However, there is little understanding of the resilience and stability of the microbial communities in engineered semicontinuous algal systems. To evaluate the dynamics and resilience of the microbial communities in microalgae biofuel cultures, we conducted a longitudinal study on open systems to compare the temporal profiles of the microbiota from two multigenerational algal cohorts, which include one seeded with the microbiota from an in-house culture and the other exogenously seeded with a natural-occurring consortia of bacterial species harvested from the Pacific Ocean. From these month-long, semicontinuous open microalga Nannochloropsis salina cultures, we sequenced a time-series of 46 samples, yielding 8804 operational taxonomic units derived from 9,160,076 high-quality partial 16S rRNA sequences. We provide quantitative evidence that clearly illustrates the development of microbial community is associated with microbiota ancestry. In addition, N. salina growth phases were linked with distinct changes in microbial phylotypes. Alteromonadeles dominated the community in the N. salina exponential phase whereas Alphaproteobacteria and Flavobacteriia were more prevalent in the stationary phase. We also demonstrate that the N. salina-associated microbial community in open cultures is diverse, resilient, and dynamic in response to environmental perturbations. This knowledge has general implications for developing and testing design principles of cultivated algal systems.

Molecular trophic markers in marine food webs and their potential use for coral ecology

Notable advances in ecological genomics have been driven by high-throughput sequencing technology and taxonomically broad sequence repositories that allow us to accurately assess species interactions with great taxonomic resolution. The use of DNA as a marker for ingested food is particularly relevant to address predator-prey interactions and disentangle complex marine food webs. DNA-based methods benefit from reductionist molecular approaches to address ecosystem scale processes, such as community structure and energy flow across trophic levels, among others. Here we review how molecular trophic markers have been used to better understand trophic interactions in the marine environment and their advantages and limitations. We focus on animal groups where research has been focused, such as marine mammals, seabirds, fishes, pelagic invertebrates and benthic invertebrates, and use case studies to illustrate how DNA-based methods unraveled food-web interactions. The potential of molecular trophic markers for disentangling the complex trophic ecology of corals is also discussed.

Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes

With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential "keystone" genes, defined as either "hubs" or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

Methane Emission in a Specific Riparian-Zone Sediment Decreased with Bioelectrochemical Manipulation and Corresponded to the Microbial Community Dynamics

Dissimilatory metal-reducing bacteria are widespread in terrestrial ecosystems, especially in anaerobic soils and sediments. Thermodynamically, dissimilatory metal reduction is more favorable than sulfate reduction and methanogenesis but less favorable than denitrification and aerobic respiration. It is critical to understand the complex relationships, including the absence or presence of terminal electron acceptors, that govern microbial competition and coexistence in anaerobic soils and sediments, because subsurface microbial processes can effect greenhouse gas emissions from soils, possibly resulting in impacts at the global scale. Here, we elucidated the effect of an inexhaustible, ferrous-iron and humic-substance mimicking terminal electron acceptor by deploying potentiostatically poised electrodes in the sediment of a very specific stream riparian zone in Upstate New York state. At two sites within the same stream riparian zone during the course of 6 weeks in the spring of 2013, we measured CH4 and N2/N2O emissions from soil chambers containing either poised or unpoised electrodes, and we harvested biofilms from the electrodes to quantify microbial community dynamics. At the upstream site, which had a lower vegetation cover and highest soil temperatures, the poised electrodes inhibited CH4 emissions by ∼45% (when normalized to remove temporal effects). CH4 emissions were not significantly impacted at the downstream site. N2/N2O emissions were generally low at both sites and were not impacted by poised electrodes. We did not find a direct link between bioelectrochemical treatment and microbial community membership; however, we did find a correspondence between environment/function and microbial community dynamics.

Bacterial diversity indicates dietary overlap among bats of different feeding habits

Bats are among the most conspicuous mammals with extraordinary adaptations. They play a key role in the ecosystem. Frugivorous bats are important seed dispersing agents that help in maintaining forest tree diversity, while insectivorous bats are natural insect pest control agents. Several previous reports suggest that bats are reservoir of viruses; nonetheless their bacterial counterparts are relatively less explored. The present study describes the microbial diversity associated with the intestine of bats from different regions of India. Our observations stipulate that there is substantial sharing of bacterial communities between the insectivorous and frugivorous bats, which signifies fairly large dietary overlap. We also observed the presence of higher abundance of Mycoplasma in Cynopterus species of bats, indicating possible Mycoplasma infection. Considering the scarcity of literature related to microbial communities of bat intestinal tract, this study can direct future microbial diversity studies in bats with reference to their dietary habits, host-bacteria interaction and zoonosis.

The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity

By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure.

Planting increases the abundance and structure complexity of soil core functional genes relevant to carbon and nitrogen cycling

Plants have an important impact on soil microbial communities and their functions. However, how plants determine the microbial composition and network interactions is still poorly understood. During a four-year field experiment, we investigated the functional gene composition of three types of soils (Phaeozem, Cambisols and Acrisol) under maize planting and bare fallow regimes located in cold temperate, warm temperate and subtropical regions, respectively. The core genes were identified using high-throughput functional gene microarray (GeoChip 3.0), and functional molecular ecological networks (fMENs) were subsequently developed with the random matrix theory (RMT)-based conceptual framework. Our results demonstrated that planting significantly (P < 0.05) increased the gene alpha-diversity in terms of richness and Shannon - Simpson's indexes for all three types of soils and 83.5% of microbial alpha-diversity can be explained by the plant factor. Moreover, planting had significant impacts on the microbial community structure and the network interactions of the microbial communities. The calculated network complexity was higher under maize planting than under bare fallow regimes. The increase of the functional genes led to an increase in both soil respiration and nitrification potential with maize planting, indicating that changes in the soil microbial communities and network interactions influenced ecological functioning.

An integrated insight into the response of sedimentary microbial communities to heavy metal contamination

Response of biological communities to environmental stresses is a critical issue in ecology, but how microbial communities shift across heavy metal gradients remain unclear. To explore the microbial response to heavy metal contamination (e.g., Cr, Mn, Zn), the composition, structure and functional potential of sedimentary microbial community were investigated by sequencing of 16S rRNA gene amplicons and a functional gene microarray. Analysis of 16S rRNA sequences revealed that the composition and structure of sedimentary microbial communities changed significantly across a gradient of heavy metal contamination, and the relative abundances were higher for Firmicutes, Chloroflexi and Crenarchaeota, but lower for Proteobacteria and Actinobacteria in highly contaminated samples. Also, molecular ecological network analysis of sequencing data indicated that their possible interactions might be enhanced in highly contaminated communities. Correspondently, key functional genes involved in metal homeostasis (e.g., chrR, metC, merB), carbon metabolism, and organic remediation showed a higher abundance in highly contaminated samples, indicating that bacterial communities in contaminated areas may modulate their energy consumption and organic remediation ability. This study indicated that the sedimentary indigenous microbial community may shift the composition and structure as well as function priority and interaction network to increase their adaptability and/or resistance to environmental contamination.

Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design

Targeted manipulation of the gut flora is increasingly being recognized as a means to improve human health. Yet, the temporal dynamics and intra- and interindividual heterogeneity of the microbiome represent experimental limitations, especially in human cross-sectional studies. Therefore, rodent models represent an invaluable tool to study the host–microbiota interface. Progress in technical and computational tools to investigate the composition and function of the microbiome has opened a new era of research and we gradually begin to understand the parameters that influence variation of host-associated microbial communities. To isolate true effects from confounding factors, it is essential to include such parameters in model intervention studies. Also, explicit journal instructions to include essential information on animal experiments are mandatory. The purpose of this review is to summarize the factors that influence microbiota composition in mice and to provide guidelines to improve the reproducibility of animal experiments.

Given the unmet need for standardizing the experimental work flow related to gut microbial research in animals, guidelines are required to isolate true effects from confounding factors.

Towards an Ecosystem Approach to Cheese Microbiology

Cheese is an ideal environment to serve as a model for the behavior of microbes in complex communities and at the same time allow detailed genetic analysis. Linking organisms, and their genes, to their role in the environment becomes possible in the case of cheese since cheese microbial communities have been "in culture" for thousands of years, with the knowledge of how to grow these organisms passed down by generations of cheesemakers. Recent reviews have described several emerging approaches to link molecular systems biology to ecosystem-scale processes, known as ecosystems biology. These approaches integrate massive datasets now available through high-throughput sequencing technologies with measurements of ecosystem properties. High-throughput datasets uncover the "parts list" (e.g., the species and all the genes within each species) of an ecosystem as well as the molecular basis of interactions within this parts list. Novel computational frameworks make it possible to link species and their interactions to ecosystem properties. Applying these approaches across multiple temporal and spatial scales makes it possible to understand how changes in the parts lists over space and time lead to changes in ecosystems processes. By manipulating the species present within model systems, we can test hypotheses related to the role of microbes in ecosystem function. Due to the tractability of cheese microbial communities, we have the opportunity to use an ecosystems biology approach from the scale of individual microbial cells within a cheese to replicated cheese microbial communities across continents. Using cheese as a model microbial ecosystem can provide a way to answer important questions concerning the form, function, and evolution of microbial communities.

Microbiology Meets Big Data: The Case of Gut Microbiota-Derived Trimethylamine

During the past decade, meta-omics approaches have revolutionized microbiology, allowing for a cultivation-free assessment of the composition and functional properties of entire microbial ecosystems. On the one hand, a phylogenetic and functional interpretation of such data relies on accumulated genetic, biochemical, metabolic, and phenotypic characterization of microbial variation. On the other hand, the increasing availability of extensive microbiome data sets and corresponding metadata provides a vast, underused resource for the microbiology field as a whole. To demonstrate the potential for integrating big data into a functional microbiology workflow, we review literature on trimethylamine (TMA), a microbiota-generated metabolite linked to atherosclerosis development. Translating recently elucidated microbial pathways resulting in TMA production into genomic orthologs, we demonstrate how to mine for their presence in public (meta-) genomic databases and link findings to associated metadata. Reviewing pathway abundance in public data sets shows that TMA production potential is associated with symptomatic atherosclerosis and allows identification of currently uncharacterized TMA-producing bacteria.

Temporal Study of the Microbial Diversity of the North Arm of Great Salt Lake, Utah, U.S

We employed a temporal sampling approach to understand how the microbial diversity may shift in the north arm of Great Salt Lake, Utah, U.S. To determine how variations in seasonal environmental factors affect microbial communities, length heterogeneity PCR fingerprinting was performed using consensus primers for the domain Bacteria, and the haloarchaea. The archaeal fingerprints showed similarities during 2003 and 2004, but this diversity changed during the remaining two years of the study, 2005 and 2006. We also performed molecular phylogenetic analysis of the 16S rRNA genes of the whole microbial community to characterize the taxa in the samples. Our results indicated that in the domain, Bacteria, the Salinibacter group dominated the populations in all samplings. However, in the case of Archaea, as noted by LIBSHUFF for phylogenetic relatedness analysis, many of the temporal communities were distinct from each other, and changes in community composition did not track with environmental parameters. Around 20-23 different phylotypes, as revealed by rarefaction, predominated at different periods of the year. Some phylotypes, such as Haloquadradum, were present year-round although they changed in their abundance in different samplings, which may indicate that these species are affected by biotic factors, such as nutrients or viruses, that are independent of seasonal temperature dynamics.

Profiling of Oral and Nasal Microbiome in Children With Cleft Palate

OBJECTIVE

To identify the oral and nasal microbial profile of cleft palate children and control children and to reveal interrelationships between the microbiome and the high prevalence of infectious diseases.

DESIGN

Saliva and nasal samples of 10 cleft palate children and 10 age-matched control children were analyzed. Total microbial genomic DNA was isolated, polymerase chain reaction-denaturing gradient gel electrophoresis was applied to obtain fingerprints, and selected bands on fingerprints were sequenced.

RESULTS

The results revealed a significantly lower saliva microbial diversity in cleft children and a different microbial component in both saliva and nares in children with cleft palate. A higher component similarity between the oral and nasal samples was found in the cleft group than in the control group. Lautropia species and Bacillus species were significantly less present among the saliva samples of cleft group. Dolosigranulum species and Bacillus species were significantly fewer in the nasal cavity of cleft group. Streptococcus species became much more predominant in the nasal cavity of the cleft group than in that of the control group.

CONCLUSIONS

A disturbed ecological ecosystem is found in oral and nasal microbiome of children with cleft palate as a consequence of the abnormal communication between the two cavities. Further studies are needed to explore the relationship between the disturbed microbiome and diseases.

Antagonistic interactions are sufficient to explain self-assemblage of bacterial communities in a homogeneous environment: a computational modeling approach

Most of the studies in Ecology have been devoted to analyzing the effects the environment has on individuals, populations, and communities, thus neglecting the effects of biotic interactions on the system dynamics. In the present work we study the structure of bacterial communities in the oligotrophic shallow water system of Churince, Cuatro Cienegas, Mexico. Since the physicochemical conditions of this water system are homogeneous and quite stable in time, it is an excellent candidate to study how biotic factors influence the structure of bacterial communities. In a previous study, the binary antagonistic interactions of 78 bacterial strains, isolated from Churince, were experimentally determined. We employ these data to develop a computer algorithm to simulate growth experiments in a cellular grid representing the pond. Remarkably, in our model, the dynamics of all the simulated bacterial populations is determined solely by antagonistic interactions. Our results indicate that all bacterial strains (even those that are antagonized by many other bacteria) survive in the long term, and that the underlying mechanism is the formation of bacterial community patches. Patches corresponding to less antagonistic and highly susceptible strains are consistently isolated from the highly-antagonistic bacterial colonies by patches of neutral strains. These results concur with the observed features of the bacterial community structure previously reported. Finally, we study how our findings depend on factors like initial population size, differential population growth rates, homogeneous population death rates, and enhanced bacterial diffusion.

Analyses of soil microbial community compositions and functional genes reveal potential consequences of natural forest succession

The succession of microbial community structure and function is a central ecological topic, as microbes drive the Earth’s biogeochemical cycles. To elucidate the response and mechanistic underpinnings of soil microbial community structure and metabolic potential relevant to natural forest succession, we compared soil microbial communities from three adjacent natural forests: a coniferous forest (CF), a mixed broadleaf forest (MBF) and a deciduous broadleaf forest (DBF) on Shennongjia Mountain in central China. In contrary to plant communities, the microbial taxonomic diversity of the DBF was significantly (P < 0.05) higher than those of CF and MBF, rendering their microbial community compositions markedly different. Consistently, microbial functional diversity was also highest in the DBF. Furthermore, a network analysis of microbial carbon and nitrogen cycling genes showed the network for the DBF samples was relatively large and tight, revealing strong couplings between microbes. Soil temperature, reflective of climate regimes, was important in shaping microbial communities at both taxonomic and functional gene levels. As a first glimpse of both the taxonomic and functional compositions of soil microbial communities, our results suggest that microbial community structure and function potentials will be altered by future environmental changes, which have implications for forest succession.

Metagenomic and lipid analyses reveal a diel cycle in a hypersaline microbial ecosystem

Marine microbial communities experience daily fluctuations in light and temperature that can have important ramifications for carbon and nutrient cycling. Elucidation of such short time scale community-wide dynamics is hindered by system complexity. Hypersaline aquatic environments have lower species richness than marine environments and can be well-defined spatially, hence they provide a model system for diel cycle analysis. We conducted a 3-day time series experiment in a well-defined pool in hypersaline Lake Tyrrell, Australia. Microbial communities were tracked by combining cultivation-independent lipidomic, metagenomic and microscopy methods. The ratio of total bacterial to archaeal core lipids in the planktonic community increased by up to 58% during daylight hours and decreased by up to 32% overnight. However, total organism abundances remained relatively consistent over 3 days. Metagenomic analysis of the planktonic community composition, resolved at the genome level, showed dominance by Haloquadratum species and six uncultured members of the Halobacteriaceae. The post 0.8 μm filtrate contained six different nanohaloarchaeal types, three of which have not been identified previously, and cryo-transmission electron microscopy imaging confirmed the presence of small cells. Notably, these nano-sized archaea showed a strong diel cycle, with a pronounced increase in relative abundance over the night periods. We detected no eukaryotic algae or other photosynthetic primary producers, suggesting that carbon resources may derive from patchily distributed microbial mats at the sediment-water interface or from surrounding land. Results show the operation of a strong community-level diel cycle, probably driven by interconnected temperature, light abundance, dissolved oxygen concentration and nutrient flux effects.

Deciphering microbial community robustness through synthetic ecology and molecular systems synecology

Microbial ecosystems exhibit specific robustness attributes arising from the assembly and interaction networks of diverse, heterogeneous communities challenged by fluctuating environmental conditions. Synthetic ecology provides new insights into key biodiversity-stability relationships and robustness determinants of host-associated or environmental microbiomes. Driven by the advances of meta-omics technologies and bioinformatics, community-centered approaches (defined as molecular systems synecology) combined with the development of dynamic and mechanistic mathematical models make it possible to decipher and predict the outcomes of microbial ecosystems under disturbances. Beyond discriminating the normal operating range and natural, intrinsic dynamics of microbial processes from systems-level responses to environmental forcing, predictive modeling is poised to be integrated within prescriptive analytical frameworks and thus provide guidance in decision-making and proactive microbial resource management.

Molecular ecological tools to decipher the role of our microbial mass in obesity

After birth, our gastrointestinal (GI) tract is colonised by a highly complex assemblage of microbes, collectively termed the GI microbiota, that develops intimate interactions with our body. Recent evidence indicates that the GI microbiota and its products may contribute to the development of obesity and related diseases. This, coupled with the current worldwide epidemic of obesity, has moved microbiome research into the spotlight of attention. Although the main cause of obesity and its associated metabolic complications is excess caloric intake compared with expenditure, differences in GI tract microbial ecology between individuals might be an important biomarker, mediator or new therapeutic target. This can be investigated using a diverse set of complementary so called -omics technologies, such as 16S ribosomal RNA gene-targeted composition profiling, metabolomics, metagenomics, metatranscriptomics and metaproteomics. This review aims to describe the different molecular approaches and their contributions to our understanding of the role of the GI microbiota in host energy homeostasis. Correspondingly, we highlight their respective strengths, but also try to create awareness for their specific limitations. However, it is currently still unclear which bacterial groups play a role in the development of obesity in humans. This might partly be explained by the heterogeneity in genotype, lifestyle, diet and the complex ethology of obesity and its associated metabolic disorders (OAMD). Nevertheless, recent research on this matter has shown a conceptual shift by focusing on more homogenous subpopulations, through the use of both anthropometric (weight, total body fat) as well as biochemical variables (insulin resistance, hyperlipidaemia) to define categories. Combined with technological advances, recent data suggests that an OAMD associated microbiota can be characterised by a potential pro-inflammatory composition, with less potential for the production of short chain fatty acids and butyrate in particular.

Sampling of intestinal microbiota and targeted amplification of bacterial 16S rRNA genes for microbial ecologic analysis

Dysbiosis of host-associated commensal microbiota is emerging as an important factor in risk and phenotype of immunologic, metabolic, and behavioral diseases. Accurate analysis of microbial composition and functional state in humans or mice requires appropriate collection and pre-processing of biospecimens. Methods to sample luminal and mucosal microbiota from human or mouse intestines and to profile microbial phylogenetic composition using 16S rRNA sequencing are presented here. Data generated using the methods in this unit can be used for downstream quantitative analysis of microbial ecology.

Reprograming of gut microbiome energy metabolism by the FUT2 Crohn's disease risk polymorphism

Fucosyltransferase 2 (FUT2) is an enzyme that is responsible for the synthesis of the H antigen in body fluids and on the intestinal mucosa. The H antigen is an oligosaccharide moiety that acts as both an attachment site and carbon source for intestinal bacteria. Non-secretors, who are homozygous for the loss-of-function alleles of FUT2 gene (sese), have increased susceptibility to Crohn's disease (CD). To characterize the effect of FUT2 polymorphism on the mucosal ecosystem, we profiled the microbiome, meta-proteome and meta-metabolome of 75 endoscopic lavage samples from the cecum and sigmoid of 39 healthy subjects (12 SeSe, 18 Sese and 9 sese). Imputed metagenomic analysis revealed perturbations of energy metabolism in the microbiome of non-secretor and heterozygote individuals, notably the enrichment of carbohydrate and lipid metabolism, cofactor and vitamin metabolism and glycan biosynthesis and metabolism-related pathways, and the depletion of amino-acid biosynthesis and metabolism. Similar changes were observed in mice bearing the FUT2(-/-) genotype. Metabolomic analysis of human specimens revealed concordant as well as novel changes in the levels of several metabolites. Human metaproteomic analysis indicated that these functional changes were accompanied by sub-clinical levels of inflammation in the local intestinal mucosa. Therefore, the colonic microbiota of non-secretors is altered at both the compositional and functional levels, affecting the host mucosal state and potentially explaining the association of FUT2 genotype and CD susceptibility.

The early impact of genomics and metagenomics on ruminal microbiology

Knowledge gained from early and recent studies that define the functions of microbial populations within the rumen microbiome is essential to allow for directed rumen manipulation strategies. A large number of omic studies have focused on carbohydrate active enzymes either for improved fiber digestion within the animal or for use in industries such as biofuels. Studies of the rumen microbiome with respect to methane production and abatement strategies have led to initiatives for defining the microbiome of low- and high-methane-emitting animals while ensuring optimal feed conversion. With advances in omic technologies, the ability to link host genetics and the rumen microbiome by studying all the biological components (holobiont) through the use of hologenomics has begun. However, a program to culture and isolate microbial species for the purpose of standard microbial characterization to aid in assigning function to genomic data remains critical, especially for genes of unknown function.

Towards an integrated understanding of gut microbiota using insects as model systems

Metazoans form symbioses with microorganisms that synthesize essential nutritional compounds and increase their efficiency to digest and absorb nutrients. Despite the growing awareness that microbes within the gut play key roles in metabolism, health and development of metazoans, symbiotic relationships within the gut are far from fully understood. Insects, which generally harbor a lower microbial diversity than vertebrates, have recently emerged as potential model systems to study these interactions. In this review, we give a brief overview of the characteristics of the gut microbiota in insects in terms of low diversity but high variability at intra- and interspecific levels and we investigate some of the ecological and methodological factors that might explain such variability. We then emphasize how studies integrating an array of techniques and disciplines have the potential to provide new understanding of the biology of this micro eco-system.

Novel tools integrating metabolic and gene function to study the impact of the environment on coral symbiosis

The symbiotic dinoflagellates (genus Symbiodinium) inhabiting coral endodermal tissues are well known for their role as keystone symbiotic partners, providing corals with enormous amounts of energy acquired via photosynthesis and the absorption of dissolved nutrients. In the past few decades, corals reefs worldwide have been increasingly affected by coral bleaching (i.e., the breakdown of the symbiosis between corals and their dinoflagellate symbionts), which carries important socio-economic implications. Consequently, the number of studies focusing on the molecular and cellular processes underlying this biological phenomenon has grown rapidly, and symbiosis is now widely recognized as a major topic in coral biology. However, obtaining a clear image of the interplay between the environment and this mutualistic symbiosis remains challenging. Here, we review the potential of recent technological advances in molecular biology and approaches using stable isotopes to fill critical knowledge gaps regarding coral symbiotic function. Finally, we emphasize that the largest opportunity to achieve the full potential in this field arises from the integration of these technological advances.

Pros and cons of ion-torrent next generation sequencing versus terminal restriction fragment length polymorphism T-RFLP for studying the rumen bacterial community

The development of next generation sequencing has challenged the use of other molecular fingerprinting methods used to study microbial diversity. We analysed the bacterial diversity in the rumen of defaunated sheep following the introduction of different protozoal populations, using both next generation sequencing (NGS: Ion Torrent PGM) and terminal restriction fragment length polymorphism (T-RFLP). Although absolute number differed, there was a high correlation between NGS and T-RFLP in terms of richness and diversity with R values of 0.836 and 0.781 for richness and Shannon-Wiener index, respectively. Dendrograms for both datasets were also highly correlated (Mantel test = 0.742). Eighteen OTUs and ten genera were significantly impacted by the addition of rumen protozoa, with an increase in the relative abundance of Prevotella, Bacteroides and Ruminobacter, related to an increase in free ammonia levels in the rumen. Our findings suggest that classic fingerprinting methods are still valuable tools to study microbial diversity and structure in complex environments but that NGS techniques now provide cost effect alternatives that provide a far greater level of information on the individual members of the microbial population.

Demonstrating microbial co-occurrence pattern analyses within and between ecosystems

Co-occurrence patterns are used in ecology to explore interactions between organisms and environmental effects on coexistence within biological communities. Analysis of co-occurrence patterns among microbial communities has ranged from simple pairwise comparisons between all community members to direct hypothesis testing between focal species. However, co-occurrence patterns are rarely studied across multiple ecosystems or multiple scales of biological organization within the same study. Here we outline an approach to produce co-occurrence analyses that are focused at three different scales: co-occurrence patterns between ecosystems at the community scale, modules of co-occurring microorganisms within communities, and co-occurring pairs within modules that are nested within microbial communities. To demonstrate our co-occurrence analysis approach, we gathered publicly available 16S rRNA amplicon datasets to compare and contrast microbial co-occurrence at different taxonomic levels across different ecosystems. We found differences in community composition and co-occurrence that reflect environmental filtering at the community scale and consistent pairwise occurrences that may be used to infer ecological traits about poorly understood microbial taxa. However, we also found that conclusions derived from applying network statistics to microbial relationships can vary depending on the taxonomic level chosen and criteria used to build co-occurrence networks. We present our statistical analysis and code for public use in analysis of co-occurrence patterns across microbial communities.

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Learning from agriculture: understanding low-dose antimicrobials as drivers of resistome expansion

Antimicrobial resistance is a growing public health challenge worldwide, with agricultural use of antimicrobials being one major contributor to the emergence and dissemination of antimicrobial resistance (AMR). Globally, most antimicrobials are used in industrial food animal production, a major context for microbiomes encountering low-doses or subtherapeutic-levels of antimicrobial agents from all mechanistic classes. This modern practice exerts broad eco-evolutionary effects on the gut microbiome of food animals, which is subsequently transferred to animal waste. This waste contains complex constituents that are challenging to treat, including AMR determinants and low-dose antimicrobials. Unconfined storage or land deposition of a large volume of animal waste causes its wide contact with the environment and drives the expansion of the environmental resistome through mobilome facilitated horizontal genet transfer. The expanded environmental resistome, which encompasses both natural constituents and anthropogenic inputs, can persist under multiple stressors from agriculture and may re-enter humans, thus posing a public health risk to humans. For these reasons, this review focuses on agricultural antimicrobial use as a laboratory for understanding low-dose antimicrobials as drivers of resistome expansion, briefly summarizes current knowledge on this topic, highlights the importance of research specifically on environmental microbial ecosystems considering AMR as environmental pollution, and calls attention to the needs for longitudinal studies at the systems level.

Metabolomic analysis of cooperative adaptation between co-cultured Bacillus cereus and Ketogulonicigenium vulgare

The cooperative adaptation of subcultivated Bacillus cereus and Ketogulonicigenium vulgare significantly increased the productivity of 2-keto-L-gulonic acid, the precursor of vitamin C. The mechanism of cooperative adaptation of the serial subcultivated B. cereus and K. vulgare was investigated in this study by culturing the two strains orthogonally on agar plates. It was found that the swarming distance of B. cereus along the trace of K. vulgare on the plate decreased after 150 days' subcultivation. Metabolomic analysis on these co-cultured B. cereus and K. vulgare strains showed that their cooperative adaptation was accomplished by three key events: (i) the ability of nutrients (e.g., amino acids and purines) searching and intaking, and proteins biosynthesis is increased in the evolved B. cereus; (ii) the capability of protein degradation and amino acids transportation is enhanced in evolved K. vulgare; (iii) the evolved B. cereus was found to provide more nutrients (mostly amino acids and purines) to K. vulgare, thus strengthening the oxidation and energy generation of K. vulgare. Our results provided novel insights into the systems-level understanding of the cooperative adaptation between strains in synergistic consortium.

Some like it cold: understanding the survival strategies of psychrophiles

Much of the Earth's surface, both marine and terrestrial, is either periodically or permanently cold. Although habitats that are largely or continuously frozen are generally considered to be inhospitable to life, psychrophilic organisms have managed to survive in these environments. This is attributed to their innate adaptive capacity to cope with cold and its associated stresses. Here, we review the various environmental, physiological and molecular adaptations that psychrophilic microorganisms use to thrive under adverse conditions. We also discuss the impact of modern "omic" technologies in developing an improved understanding of these adaptations, highlighting recent work in this growing field.

Toward quantitative understanding on microbial community structure and functioning: a modeling-centered approach using degradation of marine oil spills as example

Molecular ecology approaches are rapidly advancing our insights into the microorganisms involved in the degradation of marine oil spills and their metabolic potentials. Yet, many questions remain open: how do oil-degrading microbial communities assemble in terms of functional diversity, species abundances and organization and what are the drivers? How do the functional properties of microorganisms scale to processes at the ecosystem level? How does mass flow among species, and which factors and species control and regulate fluxes, stability and other ecosystem functions? Can generic rules on oil-degradation be derived, and what drivers underlie these rules? How can we engineer oil-degrading microbial communities such that toxic polycyclic aromatic hydrocarbons are degraded faster? These types of questions apply to the field of microbial ecology in general. We outline how recent advances in single-species systems biology might be extended to help answer these questions. We argue that bottom-up mechanistic modeling allows deciphering the respective roles and interactions among microorganisms. In particular constraint-based, metagenome-derived community-scale flux balance analysis appears suited for this goal as it allows calculating degradation-related fluxes based on physiological constraints and growth strategies, without needing detailed kinetic information. We subsequently discuss what is required to make these approaches successful, and identify a need to better understand microbial physiology in order to advance microbial ecology. We advocate the development of databases containing microbial physiological data. Answering the posed questions is far from trivial. Oil-degrading communities are, however, an attractive setting to start testing systems biology-derived models and hypotheses as they are relatively simple in diversity and key activities, with several key players being isolated and a high availability of experimental data and approaches.

Synthetic microbial communities

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Microbial interactions and system function are two ways to study communities.

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Natural microbial communities are difficult to define and to study.

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Synthetic microbial communities are comprehensible systems of reduced complexity.

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Synthetic communities keep key features of natural ones and are amenable to modelling.

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Synthetic microbial communities are gaining importance in biotechnology.

While natural microbial communities are composed of a mix of microbes with often unknown functions, the construction of synthetic microbial communities allows for the generation of defined systems with reduced complexity. Used in a top-down approach, synthetic communities serve as model systems to ask questions about the performance and stability of microbial communities. In a second, bottom-up approach, synthetic microbial communities are used to study which conditions are necessary to generate interaction patterns like symbiosis or competition, and how higher order community structure can emerge from these. Besides their obvious value as model systems to understand the structure, function and evolution of microbial communities as complex dynamical systems, synthetic communities can also open up new avenues for biotechnological applications.

Ecological perspectives on microbes involved in N-cycling

Nitrogen (N) cycles have been directly linked to the functional stability of ecosystems because N is an essential element for life. Furthermore, the supply of N to organisms regulates primary productivity in many natural ecosystems. Microbial communities have been shown to significantly contribute to N cycles because many N-cycling processes are microbially mediated. Only particular groups of microbes were implicated in N-cycling processes, such as nitrogen fixation, nitrification, and denitrification, until a few decades ago. However, recent advances in high-throughput sequencing technologies and sophisticated isolation techniques have enabled microbiologists to discover that N-cycling microbes are unexpectedly diverse in their functions and phylogenies. Therefore, elucidating the link between biogeochemical N-cycling processes and microbial community dynamics can provide a more mechanistic understanding of N cycles than the direct observation of N dynamics. In this review, we summarized recent findings that characterized the microbes governing novel N-cycling processes. We also discussed the ecological role of N-cycling microbial community dynamics, which is essential for advancing our understanding of the functional stability of ecosystems.

Age-correlated stress resistance improves fitness of yeast: support from agent-based simulations

BACKGROUND

Resistance to stress is often heterogeneous among individuals within a population, which helps protect against intermittent stress (bet hedging). This is also the case for heat shock resistance in the budding yeast Saccharomyces cerevisiae. Interestingly, the resistance appears to be continuously distributed (vs. binary, switch-like) and correlated with replicative age (vs. random). Older, slower-growing cells are more resistant than younger, faster-growing ones. Is there a fitness benefit to age-correlated stress resistance?

RESULTS

Here this hypothesis is explored using a simple agent-based model, which simulates a population of individual cells that grow and replicate. Cells age by accumulating damage, which lowers their growth rate. They synthesize trehalose at a metabolic cost, which helps protect against heat shock. Proteins Tsl1 and Tps3 (trehalose synthase complex regulatory subunit TSL1 and TPS3) represent the trehalose synthesis complex and they are expressed using constant, age-dependent and stochastic terms. The model was constrained by calibration and comparison to data from the literature, including individual-based observations obtained using high-throughput microscopy and flow cytometry. A heterogeneity network was developed, which highlights the predominant sources and pathways of resistance heterogeneity. To determine the best trehalose synthesis strategy, model strains with different Tsl1/Tps3 expression parameters were placed in competition in an environment with intermittent heat shocks.

CONCLUSIONS

For high severities and low frequencies of heat shock, the winning strain used an age-dependent bet hedging strategy, which shows that there can be a benefit to age-correlated stress resistance. The study also illustrates the utility of combining individual-based observations and modeling to understand mechanisms underlying population heterogeneity, and the effect on fitness.