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Microbial communities are complex assemblages whose dynamics are shaped by abiotic and biotic factors. A major challenge concerns correctly forecasting the community behaviour in the future. In this context, communities in biological wastewater treatment plants (BWWTPs) represent excellent model systems, because forecasting them is required to ultimately control and operate the plants in a sustainable manner. Here, we forecast the microbial community from the water-air interface of the anaerobic tank of a BWWTP via longitudinal meta-omics (metagenomics, metatranscriptomics and metaproteomics) data covering 14 months at weekly intervals. We extracted all the available time-dependent information, summarised it in 17 temporal signals (explaining 91.1 of the temporal variance) and linked them over time to rebuild the sequence of ecological phenomena behind the community dynamics. We forecasted the signals over the following five years and tested the predictions with 21 extra samples. We were able to correctly forecast five signals accounting for 22.5 of the time-dependent information in the system and generate mechanistic predictions on the ecological events in the community (e.g. a predation cycle involving bacteria, viruses and amoebas). Through the forecasting of the 17 signals and the environmental variables readings we reconstructed the gene abundance and expression for the following 5 years, showing a nearly perfect trend prediction (coefficient of determination >= 0.97) for the first 2 years. The study demonstrates the maturity of microbial ecology to forecast composition and gene expression of open microbial ecosystems using year-spanning interactions between community cycles and environmental parameters.

Background: Micropollutants in bodies of water represent many challenges. We addressedthese challenges by the application of constructed wetlands, which represent advanced treatmenttechnology for the removal of micropollutants from water. However, which mechanisms specificallycontribute to the removal efficiency often remains unclear. Methods: Here, we focus on the removalof 27 micropollutants by bioremediation. For this, macrophytesPhragmites australis,Iris pseudacorusandLythrum salicariawere taken from established wetlands, and a special experimental set-up wasdesigned. In order to better understand the impact of the rhizosphere microbiome, we determinedthe microbial composition using 16S rRNA gene sequencing and investigated the role of identifiedgenera in the micropollutant removal of micropollutants. Moreover, we studied the colonizationof macrophyte roots by arbuscular mycorrhizal fungi, which are known for their symbiotic rela-tionship with plants. This symbiosis could result in increased removal of present micropollutants.Results: We foundIris pseudacorusto be the most successful bioremediative system, as it removed22 compounds, including persistent ones, with more than 80% efficiency. The most abundant generathat contributed to the removal of micropollutants werePseudomonas, Flavobacterium, Variovorax,Methylotenera, Reyranella, AmaricoccusandHydrogenophaga.Iris pseudacorusexhibited the highest colo-nization rate (56%). Conclusions: Our experiments demonstrate the positive impact of rhizospheremicroorganisms on the removal of micropollutants.

Background: Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain‑resolved, integrated meta‑genomic, transcriptomic, and proteomic analyses of paired saliva and stool samples collected from 35 individuals from eight families with multiple cases of type 1 diabetes mellitus (T1DM). Results: We identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain‑variants from the oral cavity to the gut at the individual omic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract. Conclusions: Alterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of “mouth‑to‑gut” transfer of Streptococcus salivarius. Our results indicate that the observed oral‑cavity‑driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi‑omic analyses, we resolve strain‑variant “mouth‑to‑gut” transfer in a disease context.

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE–host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR–Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid–host and phage–host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant ‘Candidatus Microthrix parvicella’ population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes.

Aplysina aerophoba is an emerging model marine sponge, with a well-characterized microbial community in terms of diversity and structure. However, little is known about the expressed functional capabilities of its associated microbes. Here, we present the first metaproteomics-based study of the microbiome of A. aerophoba. We found that transport and degradation of halogenated and chloroaromatic compounds are common active processes in the sponge microbiomes. Our data further reveal that the highest number of proteins were affiliated to a sponge-associated Tectomicrobium, presumably from the family Entotheonellaceae, as well as to the well-known symbiont "Candidatus Synechococcus spongiarium", suggesting a high metabolic activity of these two microorganisms in situ. Evidence for nitric oxide (NO) conversion to nitrous oxide was consistently observed for Tectomicrobia across replicates, by production of the NorQ protein. Moreover, we found a potential energy-yielding pathway through CO oxidation by putative Chloroflexi bacteria. Finally, we observed expression of enzymes that may be involved in the transformation of chitin, glycoproteins, glycolipids and glucans into smaller molecules, consistent with glycosyl hydrolases predicted from analyses of the genomes of Poribacteria sponge symbionts. Thus, this study provides crucial links between expressed proteins and specific members of the A. aerophoba microbiome.

The gastrointestinal microbiome plays a central role in health and disease. Imbalances in the microbiome, also referred to as dysbiosis, have recently been associated with a number of human idiopathic diseases ranging from metabolic to neurodegenerative. However, to causally link specific microorganisms or dysbiotic communities with tissue-specific and/or systemic disease-associated phenotypes, systematic in vivo studies are fundamental. Gnotobiotic mouse models have proven to be particularly useful for the elucidation of microbiota-associated characteristics as they provide a means to conduct targeted perturbations followed by analyses of induced localized and systemic effects. Here, we describe a methodology in the framework of systems biology which allows the comprehensive isolation of high quality biomolecular fractions (DNA, RNA, proteins and metabolites) from limited and/or heterogeneous sample material derived from murine brain, liver, and colon tissues, as well as from intestinal contents (fecal pellets and fecal masses). The obtained biomolecular fractions are compatible with current high-throughput genomic, transcriptomic, proteomic, and metabolomic analyses. The resulting data fulfills the premise of systematic measurements and allows the detailed study of tissue-specific and/or systemic effects of host-microbiota interactions in relation to health and disease.

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic analyses of host- or environment-derived samples offer the prospect of resolving fundamental and realised niches in situ. In turn, this is considered a prerequisite for niche engineering in order to drive an individual population or a community towards a specific phenotype, e.g., improvement of a biotechnological process. Here, we sampled floating islets on the surface of an activated sludge tank in a time-series spanning 51 time-points over 14 months. Multi-omics datasets (metagenomics, metatranscriptomics, metaproteomics, and (meta-)metabolomics) were generated for all time-points. Leveraging nucleotide sequencing data, we analyzed the community structure and reconstructed genomes for specific populations of interest. Moreover, based on their metabolic potential, three major groups emerged, serving as proxies for their respective fundamental niches . Time-resolved linkage of the proteomic and transcriptomic data to the reconstructed genomes revealed a fine-grained picture of niche realization. In particular, environmental factors (temperature, metabolites, oxygen) were significantly associated with gene expression of individual populations. Furthermore, we subjected the community to controlled oxygen conditions (stable or dynamic) in a bioreactor experiment and measured the transcriptomic response. Our results suggest short-term adaptations of populations of interest with respect to lipid metabolism, among other pathways. In conclusion, our work demonstrates how longitudinal multi-omic datasets can be integrated in order to further our understanding of microbial niche ecology within a biotechnological process with potential applications beyond waste water treatment.

The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra- and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

Linking taxonomic identity and functional potential at the population-level is important for the study of mixed microbial communities and is greatly facilitated by the availability of microbial reference genomes. While the culture-independent recovery of population-level genomes from environmental samples using the binning of metagenomic data has expanded available reference genome catalogs, several microbial lineages remain underrepresented. Here, we present two reference-independent approaches for the identification, recovery, and refinement of hitherto undescribed population-level genomes. The first approach is aimed at genome recovery of varied taxa and involves multi-sample automated binning using CANOPY CLUSTERING complemented by visualization and human-augmented binning using VIZBINpost hoc. The second approach is particularly well-suited for the study of specific taxa and employs VIZBINde novo. Using these approaches, we reconstructed a total of six population-level genomes of distinct and divergent representatives of the Alphaproteobacteria class, the Mollicutes class, the Clostridiales order, and the Melainabacteria class from human gastrointestinal tract-derived metagenomic data. Our results demonstrate that, while automated binning approaches provide great potential for large-scale studies of mixed microbial communities, these approaches should be complemented with informative visualizations because expert-driven inspection and refinements are critical for the recovery of high-quality population-level genomes.

BACKGROUND: Mixed microbial communities underpin important biotechnological processes such as biological wastewater treatment (BWWT). A detailed knowledge of community structure and function relationships is essential for ultimately driving these systems towards desired outcomes, e.g., the enrichment in organisms capable of accumulating valuable resources during BWWT. METHODS: A comparative integrated omic analysis including metagenomics, metatranscriptomics and metaproteomics was carried out to elucidate functional differences between seasonally distinct oleaginous mixed microbial communities (OMMCs) sampled from an anoxic BWWT tank. A computational framework for the reconstruction of community-wide metabolic networks from multi-omic data was developed. These provide an overview of the functional capabilities by incorporating gene copy, transcript and protein abundances. To identify functional genes, which have a disproportionately important role in community function, we define a high relative gene expression and a high betweenness centrality relative to node degree as gene-centric and network topological features, respectively. RESULTS: Genes exhibiting high expression relative to gene copy abundance include genes involved in glycerolipid metabolism, particularly triacylglycerol lipase, encoded by known lipid accumulating populations, e.g., Candidatus Microthrix parvicella. Genes with a high relative gene expression and topologically important positions in the network include genes involved in nitrogen metabolism and fatty acid biosynthesis, encoded by Nitrosomonas spp. and Rhodococcus spp. Such genes may be regarded as ‘keystone genes’ as they are likely to be encoded by keystone species. CONCLUSION: The linking of key functionalities to community members through integrated omics opens up exciting possibilities for devising prediction and control strategies for microbial communities in the future.

Microorganisms in biological wastewater treatment plants require adaptive strategies to deal with rapidly fluctuating environmental conditions. At the population level, the filamentous bacterium Candidatus Microthrix parvicella (Ca. M. parvicella) has been found to fine-tune its gene expression for optimized substrate assimilation. Here we investigated in situ substrate assimilation by single cells of Ca. M. parvicella using nano-scale secondary-ion mass spectrometry (nanoSIMS). NanoSIMS imaging highlighted phenotypic heterogeneity among Ca. M. parvicella cells of the same filament, whereby 13C-oleic acid and 13C-glycerol-3-phosphate assimilation occurred in ≈21–55% of cells, despite non-assimilating cells being intact and alive. In response to alternating aerobic–anoxic regimes, 13C-oleic acid assimilation occurred among subpopulations of Ca. M. parvicella cells (≈3–28% of cells). Furthermore, Ca. M. parvicella cells exhibited two temperature optima for 13C-oleic acid assimilation and associated growth rates. These results suggest that phenotypic heterogeneity among Ca. M. parvicella cells allows the population to adapt rapidly to fluctuating environmental conditions facilitating its widespread occurrence in biological wastewater treatment plants.

BACKGROUND: This is the third in a series of publications presenting formal method validation for biospecimen processing in the context of accreditation in laboratories and biobanks. We report here optimization of a stool processing protocol validated for fitness-for-purpose in terms of downstream DNA-based analyses. METHODS: Stool collection was initially optimized in terms of sample input quantity and supernatant volume using canine stool. Three DNA extraction methods (PerkinElmer MSM I(R), Norgen Biotek All-In-One(R), MoBio PowerMag(R)) and six collection container types were evaluated with human stool in terms of DNA quantity and quality, DNA yield, and its reproducibility by spectrophotometry, spectrofluorometry, and quantitative PCR, DNA purity, SPUD assay, and 16S rRNA gene sequence-based taxonomic signatures. RESULTS: The optimal MSM I protocol involves a 0.2 g stool sample and 1000 muL supernatant. The MSM I extraction was superior in terms of DNA quantity and quality when compared to the other two methods tested. Optimal results were obtained with plain Sarstedt tubes (without stabilizer, requiring immediate freezing and storage at -20 degrees C or -80 degrees C) and Genotek tubes (with stabilizer and RT storage) in terms of DNA yields (total, human, bacterial, and double-stranded) according to spectrophotometry and spectrofluorometry, with low yield variability and good DNA purity. No inhibitors were identified at 25 ng/muL. The protocol was reproducible in terms of DNA yield among different stool aliquots. CONCLUSIONS: We validated a stool collection method suitable for downstream DNA metagenomic analysis. DNA extraction with the MSM I method using Genotek tubes was considered optimal, with simple logistics in terms of collection and shipment and offers the possibility of automation. Laboratories and biobanks should ensure protocol conditions are systematically recorded in the scope of accreditation.

High-throughput sequencing of ribosomal RNA gene (rDNA) amplicons has opened up the door to large-scale comparative studies of microbial community structures. The short reads currently produced by massively parallel sequencing technologies make the choice of sequencing region crucial for accurate phylogenetic assignments. While for 16S rDNA, relevant regions have been well described, no truly systematic design of 18S rDNA primers aimed at resolving eukaryotic diversity has yet been reported. Here we used 31,862 18S rDNA sequences to design a set of broad-taxonomic range degenerate PCR primers. We simulated the phylogenetic information that each candidate primer pair would retrieve using paired-or single-end reads of various lengths, representing different sequencing technologies. Primer pairs targeting the V4 region performed best, allowing discrimination with paired-end reads as short as 150 bp (with 75% accuracy at genus level). The conditions for PCR amplification were optimised for one of these primer pairs and this was used to amplify 18S rDNA sequences from isolates as well as from a range of environmental samples which were then Illumina sequenced and analysed, revealing good concordance between expected and observed results. In summary, the reported primer sets will allow minimally biased assessment of eukaryotic diversity in different microbial ecosystems., QC 20140604

High-throughput sequencing of ribosomal RNA gene (rDNA) amplicons has opened up the door to large-scale comparative studies of microbial community structures. The short reads currently produced by massively parallel sequencing technologies make the choice of sequencing region crucial for accurate phylogenetic assignments. While for 16S rDNA, relevant regions have been well described, no truly systematic design of 18S rDNA primers aimed at resolving eukaryotic diversity has yet been reported. Here we used 31,862 18S rDNA sequences to design a set of broad-taxonomic range degenerate PCR primers. We simulated the phylogenetic information that each candidate primer pair would retrieve using paired- or single-end reads of various lengths, representing different sequencing technologies. Primer pairs targeting the V4 region performed best, allowing discrimination with paired-end reads as short as 150 bp (with 75% accuracy at genus level). The conditions for PCR amplification were optimised for one of these primer pairs and this was used to amplify 18S rDNA sequences from isolates as well as from a range of environmental samples which were then Illumina sequenced and analysed, revealing good concordance between expected and observed results. In summary, the reported primer sets will allow minimally biased assessment of eukaryotic diversity in different microbial ecosystems.

Microbial communities are complex and dynamic systems that are primarily structured according to their members’ ecological niches. To investigate how niche breadth (generalist versus specialist lifestyle strategies) relates to ecological success, we develop and apply an integrative workflow for the multi-omic analysis of oleaginous mixed microbial communities from a biological wastewater treatment plant. Time- and space-resolved coupled metabolomic and taxonomic analyses demonstrate that the community-wide lipid accumulation phenotype is associated with the dominance of the generalist bacterium Candidatus Microthrix spp. By integrating population-level genomic reconstructions (reflecting fundamental niches) with transcriptomic and proteomic data (realised niches), we identify finely tuned gene expression governing resource usage by Candidatus Microthrix parvicella over time. Moreover, our results indicate that the fluctuating environmental conditions constrain the accumulation of genetic variation in Candidatus Microthrix parvicella likely due to fitness trade-offs. Based on our observations, niche breadth has to be considered as an important factor for understanding the evolutionary processes governing (microbial) population sizes and structures in situ.

Integrated omics of microbial consortia, comprising systematized metagenomic, metatranscriptomic, metaproteomic and meta-metabolomic analyses, allows in-depth characterization of organismal and functional diversity in situ. To allow meaningful meta-omic data integration, truly systematic measurements of the typically heterogeneous sample biomass is required. Therefore, there is a need for analyzing biomolecular fractions obtained from single, undivided samples. Here, we share a methodological workflow for the reproducible isolation of concomitant polar and non-polar metabolites, RNA, DNA and proteins from samples obtained from a biological wastewater treatment plant. The methodological framework is applicable to other biological samples [1,2], is compatible with different kits for biomacromolecular isolation [1,2] with minimal tailoring, and represents an important first step in standardization for the emerging field of Molecular Eco-Systems Biology.

Microbial communities are complex and dynamic systems that are influenced by stochastic-neutral processes but are mainly structured by resource availability and usage. High-resolution “meta-omics” offer exciting prospects to investigate microbial populations in their native environment. In particular, integrated meta-omics, by allowing simultaneous resolution of fundamental niches (genomics) and realised niches (transcriptomics, proteomics and metabolomics), can resolve microbial lifestyles (generalist versus specialist lifestyle strategies) in situ. We have recently developed the necessary wet- and dry-lab methodologies to carry out systematic molecular measurements of microbial consortia over space and time, and to integrate and analyse the resulting data at the population-level. We applied these methods to oleaginous mixed microbial communities located on the surface of anoxic biological wastewater treatment tanks to investigate how niche breadth (generalist versus specialist lifestyle strategies) relates to community-level phenotypes and ecological success (i.e. population size). Coupled metabolomics and 16S rRNA gene-based deep sequencing demonstrate that the community-wide lipid accumulation phenotype is associated with the dominance of Candidatus Microthrix parvicella. By integrating population-level genomic reconstructions with transcriptomic and proteomic data, we found that the dominance of this microbial generalist population results from finely tuned resource usage and optimal foraging behaviour. Moreover, the fluctuating environmental conditions constrain the accumulation of variations, leading to a genetically homogeneous population likely due to fitness trade-offs. By integrating metagenomic, metatranscriptomic, metaproteomic and metabolomic information, we demonstrate that natural microbial population sizes and structures are intricately linked to resource usage and that differing microbial lifestyle strategies may explain the varying degrees of with