Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage

Forecasting the dynamics of a complex microbial community using integrated meta-omics

Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.

High speciation rate of niche specialists in hot springs

Ecological and evolutionary processes simultaneously regulate microbial diversity, but the evolutionary processes and their driving forces remain largely unexplored. Here we investigated the ecological and evolutionary characteristics of microbiota in hot springs spanning a broad temperature range (54.8-80 °C) by sequencing the 16S rRNA genes. Our results demonstrated that niche specialists and niche generalists are embedded in a complex interaction of ecological and evolutionary dynamics. On the thermal tolerance niche axis, thermal (T) sensitive (at a specific temperature) versus T-resistant (at least in five temperatures) species were characterized by different niche breadth, community abundance and dispersal potential, consequently differing in potential evolutionary trajectory. The niche-specialized T-sensitive species experienced strong temperature barriers, leading to completely species shift and high fitness but low abundant communities at each temperature ("home niche"), and such trade-offs thus reinforced peak performance, as evidenced by high speciation across temperatures and increasing diversification potential with temperature. In contrast, T-resistant species are advantageous of niche expansion but with poor local performance, as shown by wide niche breadth with high extinction, indicating these niche generalists are "jack-of-all-trades, master-of-none". Despite of such differences, the T-sensitive and T-resistant species are evolutionarily interacted. Specifically, the continuous transition from T-sensitive to T-resistant species insured the exclusion probability of T-resistant species at a relatively constant level across temperatures. The co-evolution and co-adaptation of T-sensitive and T-resistant species were in line with the red queen theory. Collectively, our findings demonstrate that high speciation of niche specialists could alleviate the environmental-filtering-induced negative effect on diversity.

Revealing the role of microalgae-bacteria niche for boosting wastewater treatment and energy reclamation in response to temperature

Conventional biological treatment usually cannot achieve the same high water quality as advanced treatment when conducted under varied temperatures. Here, satisfactory wastewater treatment efficiency was observed in a microalgae-bacteria consortia (MBC) over a wide temperature range because of the predominance of microalgae. Microalgae contributed more toward wastewater treatment at low temperature because of the unsatisfactory performance of the accompanying bacteria, which experienced cold stress (e.g., bacterial abundance below 3000 sequences) and executed defensive strategies (e.g., enrichment of cold-shock proteins). A low abundance of amoA-C and hao indicated that conventional nitrogen removal was replaced through the involvement of microalgae. Diverse heterotrophic bacteria for nitrogen removal were identified at medium and high temperatures, implying this microbial niche treatment contained diverse flexible consortia with temperature variation. Additionally, pathogenic bacteria were eliminated through microalgal photosynthesis. After fitting the neutral community model and calculating the ecological niche, microalgae achieved a maximum niche breadth of 5.21 and the lowest niche overlap of 0.38, while the accompanying bacterial community in the consortia were shaped through deterministic processes. Finally, the maximum energy yield of 87.4 kJ L^-1 and lipid production of 1.9 g L^-1 were achieved at medium temperature. Altogether, this study demonstrates that advanced treatment and energy reclamation can be achieved through microalgae-bacteria niche strategies.

MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data

The microbiome has been shown to be important for human health because of its influence on disease and the immune response. Mass spectrometry is an important tool for evaluating protein expression and species composition in the microbiome but is technically challenging and time-consuming. Multiplexing has emerged as a way to make spectrometry workflows faster while improving results. Here, we present MetaProD (MetaProteomics in Django) as a highly configurable metaproteomic data analysis pipeline supporting label-free and multiplexed mass spectrometry. The pipeline is open-source, uses fully open-source tools, and is integrated with Django to offer a web-based interface for configuration and data access. Benchmarking of MetaProD using multiple metaproteomics data sets showed that MetaProD achieved fast and efficient identification of peptides and proteins. Application of MetaProD to a multiplexed cancer data set resulted in identification of more differentially expressed human proteins in cancer tissues versus healthy tissues as compared to previous studies; in addition, MetaProD identified bacterial proteins in those samples, some of which are differentially abundant.

Low-Energy Adsorptive Separation by Zeolites

Separation of mixture is always necessarily required in modern industry, especially in fine chemical, petrochemical, coal chemical, and pharmaceutical industries. The challenge of separation process is usually associated with small molecules with very similar physical and chemical properties. Among the separation techniques, the commonly used high-pressure cryogenic distillation process with combination of high-pressure and very low temperature is heavily energy-consumed and accounts for the major production costs as well as 10–15% of the world's energy consumption. To this end, the adsorptive separation process based on zeolite sorbents is a promising lower-energy alternative and the performance is directly determined by the zeolite sorbents. In this review, we surveyed the separation mechanisms based on the steric, equilibrium, kinetic, and ‘trapdoor’ effect, and summarized the recent advances in adsorptive separation via zeolites including CO2, light olefins, C8 aromatics, and hydrogen isotopes. Furthermore, we provided the perspectives on the rational design of zeolite sorbents for the absolute separation of mixtures.

Undervalued Pseudo-nifH Sequences in Public Databases Distort Metagenomic Insights into Biological Nitrogen Fixers

Nitrogen fixation, a distinct process incorporating the inactive atmospheric nitrogen into the active biological processes, has been a major topic in biological and geochemical studies. Currently, insights into diversity and distribution of nitrogen-fixing microbes are dependent upon homology-based analyses of nitrogenase genes, especially the nifH gene, which are broadly conserved in nitrogen-fixing microbes. Here, we report the pitfall of using nifH as a marker of microbial nitrogen fixation. We exhaustively analyzed genomes in RefSeq (231,908 genomes) and KEGG (6,509 genomes) and cooccurrence and gene order patterns of nitrogenase genes (including nifH) therein. Up to 20% of nifH-harboring genomes lacked nifD and nifK, which encode essential subunits of nitrogenase, within 10 coding sequences upstream or downstream of nifH or on the same genome. According to a phenotypic database of prokaryotes, no species and strains harboring only nifH possess nitrogen-fixing activities, which shows that these nifH genes are "pseudo"-nifH genes. Pseudo-nifH sequences mainly belong to anaerobic microbes, including members of the class Clostridia and methanogens. We also detected many pseudo-nifH reads from metagenomic sequences of anaerobic environments such as animal guts, wastewater, paddy soils, and sediments. In some samples, pseudo-nifH overwhelmed the number of "true" nifH reads by 50% or 10 times. Because of the high sequence similarity between pseudo- and true-nifH, pronounced amounts of nifH-like reads were not confidently classified. Overall, our results encourage reconsideration of the conventional use of nifH for detecting nitrogen-fixing microbes, while suggesting that nifD or nifK would be a more reliable marker. IMPORTANCE Nitrogen-fixing microbes affect biogeochemical cycling, agricultural productivity, and microbial ecosystems, and their distributions have been investigated intensively using genomic and metagenomic sequencing. Currently, insights into nitrogen fixers in the environment have been acquired by homology searches against nitrogenase genes, particularly the nifH gene, in public databases. Here, we report that public databases include a significant amount of incorrectly annotated nifH sequences (pseudo-nifH). We exhaustively investigated the genomic structures of nifH-harboring genomes and found hundreds of pseudo-nifH sequences in RefSeq and KEGG. Over half of these pseudo-nifH sequences belonged to members of the class Clostridia, which is supposed to be a prominent nitrogen-fixing clade. We also found that the abundance of nitrogen fixers in metagenomes could be overestimated by 1.5 to >10 times due to pseudo-nifH recorded in public databases. Our results encourage reconsideration of the prevalent use of nifH as a marker of nitrogen-fixing microbes.

Recent advances in understanding the ecology of the filamentous bacteria responsible for activated sludge bulking

Activated sludge bulking caused by filamentous bacteria is still a problem in wastewater treatment plants around the world. Bulking is a microbiological problem, and so its solution on species-specific basis is likely to be reached only after their ecology, physiology and metabolism is better understood. Culture-independent molecular methods have provided much useful information about this group of organisms, and in this review, the methods employed and the information they provide are critically assessed. Their application to understanding bulking caused by the most frequently seen filament in Japan, 'Ca. Kouleothrix', is used here as an example of how these techniques might be used to develop control strategies. Whole genome sequences are now available for some of filamentous bacteria responsible for bulking, and so it is possible to understand why these filaments might thrive in activated sludge plants, and provide clues as to how eventually they might be controlled specifically.

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.

Microbial population genomes from the Amazon River reveal possible modulation of the organic matter degradation process in tropical freshwaters

Rivers connect the carbon cycle in land with that in aquatic ecosystems by transporting and transforming terrestrial organic matter (TeOM). The Amazon River receives huge loads of TeOM from the surrounding rainforest, promoting a substantial microbial heterotrophic activity and consequently, CO₂ outgassing. In the Amazon River, microbes degrade up to 55% of the lignin present in the TeOM. Yet, the main microbial genomes involved in TeOM degradation were unknown. Here, we characterize 51 population genomes (PGs) representing some of the most abundant microbes in the Amazon River deriving from 106 metagenomes. The 51 reconstructed PGs are among the most abundant microbes in the Amazon River, and 53% of them are not able to degrade TeOM. Among the PGs capable of degrading TeOM, 20% were exclusively cellulolytic, while the others could also oxidize lignin. The transport and consumption of lignin oxidation byproducts seemed to be decoupled from the oxidation process, being apparently performed by different groups of microorganisms. By connecting the genomic features of abundant microbes in the Amazon River with the degradation machinery of TeOM, we suggest that a complex microbial consortium could explain the quick turnover of TeOM previously observed in this ecosystem.

Microbial generalist or specialist: Intraspecific variation and dormancy potential matter

Microbial generalists and specialists coexist in the soil environment while having distinctive impacts on microbial community dynamics. In microbial ecology, the underlying mechanisms as to why a species is a generalist or a specialist remain ambiguous. Herein, we collected soils across a national scale and identified bacterial generalists and specialists according to niche breadth at the species level (OTU level), and the single-nucleotide differences in each species were measured to investigate intraspecific variation (at zero-radius OTU level). Compared with that of the specialists, the intraspecific variation of the generalists was much higher, which ensured their wider niche breadth and lower variability. The higher asynchrony and different niche preferences of conspecific individuals and the higher dormancy potential within the generalists further contributed to their stability in varying environments. Besides, generalists were less controlled by environmental filtering, which was indicated by the stronger signature of stochastic processes in their assembly, and had higher diversification and transition rates that allowed them to adapt to environmental changes to a greater extent than specialists. Overall, this study provides a new comprehensive understanding of the rules of assembly and the evolutionary roles of bacterial generalists and specialists. It also highlights the importance of intraspecific variation and the dormancy potential in the stability of species.

Low Global Diversity of Candidatus Microthrix, a Troublesome Filamentous Organism in Full-Scale WWTPs

Candidatus Microthrix is one of the most common bulking filamentous microorganisms found in activated sludge wastewater treatment plants (WWTPs) across the globe. One species, Ca. M. parvicella, is frequently observed, but global genus diversity, as well as important aspects of its ecology and physiology, are still unknown. Here, we use the MiDAS ecosystem-specific 16S rRNA gene database in combination with amplicon sequencing of Danish and global WWTPs to investigate Ca. Microthrix spp. diversity, distribution, and factors affecting their global presence. Only two species were abundant across the world confirming low diversity of the genus: the dominant Ca. M. parvicella and an unknown species typically present along with Ca. M. parvicella, although usually in lower abundances. Both species were mostly found in Europe at low-to-moderate temperatures and their growth was favored in municipal WWTPs with advanced process designs. As no isolate is available for the novel species, we propose the name "Candidatus Microthrix subdominans." Ten high-quality metagenome-assembled genomes recovered from Danish WWTPs, including 6 representing the novel Ca. M. subdominans, demonstrated high genetic similarity between the two species with a likely preference for lipids, a putative capability to reduce nitrate and nitrite, and the potential to store lipids and poly-P. Ca. M. subdominans had a potentially more versatile metabolism including additional sugar transporters, higher oxygen tolerance, and the potential to use carbon monoxide as energy source. Newly designed fluorescence in situ hybridization probes revealed similar filamentous morphology for both species. Raman microspectroscopy was used to quantify the in situ levels of intracellular poly-P. Despite the observed similarities in their physiology (both by genomes and in situ), the two species showed different seasonal dynamics in Danish WWTPs through a 13-years survey, possibly indicating occupation of slightly different niches. The genomic information provides the basis for future research into in situ gene expression and regulation, while the new FISH probes provide a useful tool for further characterization in situ. This study is an important step toward understanding the ecology of Ca. Microthrix in WWTPs, which may eventually lead to optimization of control strategies for its growth in this ecosystem.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

Influence of Extraction Solvent on Nontargeted Metabolomics Analysis of Enrichment Reactor Cultures Performing Enhanced Biological Phosphorus Removal (EBPR)

Metabolome profiling is becoming more commonly used in the study of complex microbial communities and microbiomes; however, to date, little information is available concerning appropriate extraction procedures. We studied the influence of different extraction solvent mixtures on untargeted metabolomics analysis of two continuous culture enrichment communities performing enhanced biological phosphate removal (EBPR), with each enrichment targeting distinct populations of polyphosphate-accumulating organisms (PAOs). We employed one non-polar solvent and up to four polar solvents for extracting metabolites from biomass. In one of the reactor microbial communities, we surveyed both intracellular and extracellular metabolites using the same set of solvents. All samples were analysed using ultra-performance liquid chromatography mass spectrometry (UPLC-MS). UPLC-MS data obtained from polar and non-polar solvents were analysed separately and evaluated using extent of repeatability, overall extraction capacity and the extent of differential abundance between physiological states. Despite both reactors demonstrating the same bioprocess phenotype, the most appropriate extraction method was biomass specific, with methanol: water (50:50 v/v) and methanol: chloroform: water (40:40:20 v/v) being chosen as the most appropriate for each of the two different bioreactors, respectively. Our approach provides new data on the influence of solvent choice on the untargeted surveys of the metabolome of PAO enriched EBPR communities and suggests that metabolome extraction methods need to be carefully tailored to the specific complex microbial community under study.

A multi-omic screening approach for the discovery of thermoactive glycoside hydrolases

Next-generation sequencing and computational biology have facilitated the implementation of new combinatorial screening approaches to discover novel enzymes of biotechnological interest. In this study, we describe the successful establishment of a multi-omic approach for the identification of thermostable hydrolase-encoding genes by determination of gene expression levels. We applied this combinatorial approach using an anaerobic enrichment culture from an Azorean hot spring sample grown on green coffee beans as recalcitrant substrate. An in-depth analysis of the microbial community resulted in microorganisms capable of metabolizing the selected substrate, such as the genera Caloramator, Dictyoglomus and Thermoanaerobacter as active and abundant microorganisms. To discover glycoside hydrolases, 90,342 annotated genes were screened for specific reaction types. A total number of 106 genes encoding cellulases (EC 3.2.1.4), beta-glucosidases (EC 3.2.1.21) and endo-1,4-beta-mannosidases (EC 3.2.1.78) were selected. Mapping of RNA-Seq reads to the related metagenome led to expression levels for each gene. Amongst those, 14 genes, encoding glycoside hydrolases, showed highest expression values, and were used for further cloning. Four proteins were biochemically characterized and were identified as thermoactive glycoside hydrolases with a broad substrate range. This work demonstrated that a combinatory omic approach is a suitable strategy identifying unique thermoactive enzymes from environmental samples.

Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics

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.

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts.

Herold et al. present an integrated meta-omics framework to investigate how mixed microbial communities, such as oleaginous bacterial populations in biological wastewater treatment plants, respond with distinct adaptation strategies to disturbances. They show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity.

The role of probiotics on the roadmap to a healthy microbiota: a symposium report

The ninth International Yakult Symposium was held in Ghent, Belgium in April 2018. Keynote lectures were from Professor Wijmenga on using biobanks to understand the relationship between the gut microbiota and health; and Professor Hill on phage-probiotic interactions. Session one included talks from Professor Plӧsch on epigenetic programming by nutritional and environmental factors; Professor Wilmes on the use of "omics" methodologies in microbiome research and Professor Rescigno on the gut vascular barrier. Session two explored the evidence behind Lactobacillus casei Shirota with Dr Nanno explaining the plasticity in immunomodulation that enables the strain to balance immune functions; Dr Macnaughtan outlining its potential therapeutic use in cirrhosis and Professor Nishida detailing effects in subjects under stress. The third session saw Professor Marchesi describing that both the host genes and the gut microbiota can play a role in cancer; Professor Bergheim highlighting crosstalk between the gut and the liver and Professor Cani describing the relationship between the gut microbiota and the endocrine system. The final session explored probiotic mechanisms, with Professor Lebeer dissecting the challenges in conducting mechanistic studies; Professor Wehkamp describing the mucosal defence system and Professor Van de Wiele detailing methods for modelling the gut microbiota in vitro.

Genome-Resolved Proteomic Stable Isotope Probing of Soil Microbial Communities Using 13CO2 and 13C-Methanol

Stable isotope probing (SIP) enables tracking the nutrient flows from isotopically labeled substrates to specific microorganisms in microbial communities. In proteomic SIP, labeled proteins synthesized by the microbial consumers of labeled substrates are identified with a shotgun proteomics approach. Here, proteomic SIP was combined with targeted metagenomic binning to reconstruct metagenome-assembled genomes (MAGs) of the microorganisms producing labeled proteins. This approach was used to track carbon flows from ¹³CO₂ to the rhizosphere communities of Zea mays, Triticum aestivum, and Arabidopsis thaliana. Rhizosphere microorganisms that assimilated plant-derived ¹³C were capable of metabolic and signaling interactions with their plant hosts, as shown by their MAGs containing genes for phytohormone modulation, quorum sensing, and transport and metabolism of nutrients typical of those found in root exudates. XoxF-type methanol dehydrogenases were among the most abundant proteins identified in the rhizosphere metaproteomes. ¹³C-methanol proteomic SIP was used to test the hypothesis that XoxF was used to metabolize and assimilate methanol in the rhizosphere. We detected 7 ¹³C-labeled XoxF proteins and identified methylotrophic pathways in the MAGs of 8 ¹³C-labeled microorganisms, which supported the hypothesis. These two studies demonstrated the capability of proteomic SIP for functional characterization of active microorganisms in complex microbial communities.

Developmental dynamics of antibiotic resistome in aerobic biofilm microbiota treating wastewater under stepwise increasing tigecycline concentrations

This study aimed to investigate the impact of tigecycline, the third generation tetracycline, on the antibiotic resistance development in environmental microbiota. Two biological contact oxidation reactors containing aerobic biofilm microbiota were constructed, one of which was constantly fed with synthetic wastewater spiked with increasing concentrations of tigecycline (0 to 25 mg/L) under a hydrolytic retention time of 24 h. Over a period of 636 days, chemical oxygen demand removal over 90% and complete nitrification were achieved for both the control and tigecycline-exposed reactors, and effluent tigecycline concentrations in the tigecycline-exposed system were always <0.051 mg/L. Significant increases (p < 0.01) in resistome abundance and resistant bacteria ratio were detected at a tigecycline dose of 10 and 25 mg/L, respectively, revealed by metagenomic sequencing and culture-based method. The increase of resistome in the tigecycline system was mainly attributed to the enrichment of tetX, one cooperative tetracycline degrading gene. Partial canonical correspondence analysis showed that the change of resistome was mainly driven by bacterial community shift (vertical pathway). Network and genome binning analyses further suggested that the proliferation of Flavobacterium harboring tetX contributed to a relatively low community-wide resistance development in the aerobic biofilm microbiota under tigecycline selection by reducing the antibiotic concentration. This work provides scientific bases for the management and evaluation of the resistance risk induced by this novel antibiotic.

A Meta-proteogenomic Approach to Peptide Identification Incorporating Assembly Uncertainty and Genomic Variation

Matching metagenomic and/or metatranscriptomic data, currently often under-used, can be useful reference for metaproteomic tandem mass spectra (MS/MS) data analysis. Here we developed a software pipeline for identification of peptides and proteins from metaproteomic MS/MS data using proteins derived from matching metagenomic (and metatranscriptomic) data as the search database, based on two novel approaches Graph2Pro (published) and Var2Pep (new). Graph2Pro retains and uses uncertainties of metagenome assembly for reference-based MS/MS data analysis. Var2Pep considers the variations found in metagenomic/metatranscriptomic sequencing reads that are not retained in the assemblies (contigs). The new software pipeline provides one stop application of both tools, and it supports the use of metagenome assembly from commonly used assemblers including MegaHit and metaSPAdes. When tested on two collections of multi-omic microbiome data sets, our pipeline significantly improved the identification rate of the metaproteomic MS/MS spectra by about two folds, comparing to conventional contig- or read-based approaches (the Var2Pep alone identified 5.6% to 24.1% more unique peptides, depending on the data set). We also showed that identified variant peptides are important for functional profiling of microbiomes. All results suggested that it is important to take into consideration of the assembly uncertainties and genomic variants to facilitate metaproteomic MS/MS data interpretation.

Extracellular acidosis triggers a senescence-like phenotype in human melanoma cells

Acidosis of the tumor microenvironment is a characteristic of solid tumors such as malignant melanoma. Main causes of the extracellular acidification are metabolic alterations in cancer cells. While numerous studies showed that acidosis promotes tumor invasiveness, metastasis, and neoangiogenesis resulting in malignant progression, contrary data reported that acidosis induces cell apoptosis, inhibits cell proliferation, and mediates cell autophagy. Here, we show that low pH (pH 6.7) induces senescent/quiescent phenotype in melanoma cells after long-time treatment defined by induction of SA-ß-galactosidase, upregulation of p21, G₁ /G₀ cell cycle arrest, and reduction of proliferation. Moreover, we revealed that extracellular acidosis triggers the inhibition of eIF2α and subsequently the activation of ATF4 expression, a key component of the integrated stress response (ISR), indicating an acid-mediated translation reprogramming. Interestingly, we also demonstrated that acidosis represses microphthalmia-associated transcription factor (MITF) and activates the expression of the receptor tyrosine kinase AXL. This MITF^low /AXL^high phenotype is correlated with drug resistance and therapeutic outcome in melanoma. Our results suggest that acidosis is an important microenvironmental factor triggering phenotypic plasticity and promoting tumor progression.

Determining Microbial Niche Breadth in the Environment for Better Ecosystem Fate Predictions

Integrated omics applied to microbial communities offers a great opportunity to analyze the niche breadths (i.e., resource and condition ranges usable by a species) of constituent populations, ranging from generalists, with a broad niche breadth, to specialists, with a narrow one. In this context, extracellular metabolomics measurements describe resource spaces available to microbial populations; dedicated analyses of metagenomics data serve to describe the fundamental niches of constituent populations, and functional meta-omics becomes a proxy to characterize the realized niches of populations and their variations though time or space. Thus, the combination of environmental omics and its thorough interpretation allows us to directly describe niche breadths of constituent populations of a microbial community, precisely and in situ This will greatly facilitate studies of the causes influencing ecosystem stability, resistance, and resilience, as well as generation of the necessary knowledge to model and predict the fate of any ecosystem in the current context of global change.

Mycobacterial metabolic characteristics in a water meter biofilm revealed by metagenomics and metatranscriptomics

Mycobacteria represent one of the most persistent bacterial populations in drinking water distribution system (DWDS) biofilm communities; however, mycobacterial in situ metabolic profiles are largely unknown. In this study, the metabolic characteristics of mycobacteria in a household water meter biofilm were unveiled using a coupled metagenomic/metatranscriptomic approach. The water meter biofilm appeared to express nitrogenase genes (nifDKH) and a full complement of genes coding for several carbon-fixation pathways, especially the Calvin cycle, suggesting the CO₂ sequestration and dinitrogen fixation potential of the biofilm. These findings indicate that it may be difficult to prevent the formation of DWDS biofilms simply by controlling the availability of organic carbon or nitrogen. The composite genome of mycobacteria (CG-M) was reconstructed based on the obtained omics data. CG-M shared similar genome phylogeny and virulence-factor profiles with Mycobacterium avium complex, suggesting that population CG-M might represent a member of mycobacteria with pathogenicity. According to the gene expression patterns, population CG-M showed the metabolic potential to assimilate CO₂ via the Calvin cycle and/or anaplerotic reactions, and even to grow autotrophically with CO as the sole carbon and energy source. This suggests that organic carbon may not be a limiting factor for mycobacterial growth in DWDSs. Moreover, our results suggest that mycobacterial aromatic degradation is primarily achieved through the catechol meta-cleavage pathway, and biofilm mycobacteria could prefer phosphate as the phosphorus source.

A robust, cost-effective method for DNA, RNA and protein co-extraction from soil, other complex microbiomes and pure cultures

The soil microbiome is inherently complex with high biological diversity, and spatial heterogeneity typically occurring on the submillimetre scale. To study the microbial ecology of soils, and other microbiomes, biomolecules, that is, nucleic acids and proteins, must be efficiently and reliably co-recovered from the same biological samples. Commercial kits are currently available for the co-extraction of DNA, RNA and proteins but none has been developed for soil samples. We present a new protocol drawing on existing phenol-chloroform-based methods for nucleic acids co-extraction but incorporating targeted precipitation of proteins from the phenol phase. The protocol is cost-effective and robust, and easily implemented using reagents commonly available in laboratories. The method is estimated to be eight times cheaper than using disparate commercial kits for the isolation of DNA and/or RNA, and proteins, from soil. The method is effective, providing good quality biomolecules from a diverse range of soil types, with clay contents varying from 9.5% to 35.1%, which we successfully used for downstream, high-throughput gene sequencing and metaproteomics. Additionally, we demonstrate that the protocol can also be easily implemented for biomolecule co-extraction from other complex microbiome samples, including cattle slurry and microbial communities recovered from anaerobic bioreactors, as well as from Gram-positive and Gram-negative pure cultures.

An integrated meta-omics approach reveals substrates involved in synergistic interactions in a bisphenol A (BPA)-degrading microbial community

BACKGROUND

Understanding microbial interactions in engineering bioprocesses is important to enhance and optimize performance outcomes and requires dissection of the multi-layer complexities of microbial communities. However, unraveling microbial interactions as well as substrates involved in complex microbial communities is a challenging task. Here, we demonstrate an integrated approach of metagenomics, metatranscriptomics, and targeted metabolite analysis to identify the substrates involved in interspecies interactions from a potential cross-feeding model community-bisphenol A (BPA)-biodegrading community, aiming to establish an identification method of microbial interactions in engineering or environmental bioprocesses.

RESULTS

The community-level BPA-metabolic pathway was constructed using integrated metagenomics and targeted metabolite analyses. The dynamics of active functions and metabolism of major community members were identified using metagenomic and metatranscriptomic analyses in concert. Correlating the community BPA biodegradation performance to the individual bacterial activities enabled the discovery of substrates involved in a synergistic interaction of cross-feeding between BPA-degrading Sphingonomas species and intermediate users, Pseudomonas sp. and Pusillimonas sp. This proposed synergistic interaction was confirmed by the co-culture of a Sphingonomas sp. and Pseudomonas sp. isolates, which demonstrated enhanced BPA biodegradation compared to the isolate of Sphingonomas sp. alone.

CONCLUSION

The three types of integrated meta-omics analyses effectively revealed the metabolic capability at both community-wide and individual bacterial levels. The correlation between these two levels revealed the hidden connection between apparent overall community performance and the contributions of individual community members and their interactions in a BPA-degrading microbial community. In addition, we demonstrated that using integrated multi-omics in conjunction with culture-based confirmation approach is effective to elucidate the microbial interactions affecting the performance outcome. We foresee this approach would contribute the future application and operation of environmental bioprocesses on a knowledge-based control.

The Gut Microbiota and Hematopoietic Stem Cell Transplantation: Challenges and Potentials

The human gut microbiota gained tremendous importance in the last decade as next-generation technologies of sequencing and multiomics analyses linked the role of the microbial communities to host physiology and pathophysiology. A growing number of human pathologies and diseases are linked to the gut microbiota. One of the main mechanisms by which the microbiota influences the host is through its interactions with the host immune system. These interactions with both innate and adaptive host intestinal and extraintestinal immunity, although usually commensalistic even mutualistic with the host, in some cases lead to serious health effects. In the case of allogenic hematopoietic stem cell transplantation (allo-HSCT), the disruption of the intestinal microbiota diversity is associated with acute graft-versus-host disease (GvHD). Causing inflammation of the liver, skin, lungs, and the intestine, GvHD occurs in 40-50% of patients undergoing allo-HSCT and results in significant posttransplantation mortality. In this review, we highlight the impact of the gut microbiota on the host immunity in GvHD and the potential of microbiota in alleviation or even prevention of GvHD.

ProteoStorm: An Ultrafast Metaproteomics Database Search Framework

Shotgun metaproteomics has the potential to reveal the functional landscape of microbial communities but lacks appropriate methods for complex samples with unknown compositions. In the absence of prior taxonomic information, tandem mass spectra would be searched against large pan-microbial databases, which requires heavy computational workload and reduces sensitivity. We present ProteoStorm, an efficient database search framework for large-scale metaproteomics studies, which identifies high-confidence peptide-spectrum matches (PSMs) while achieving a two-to-three orders-of-magnitude speedup over popular tools. A reanalysis of a urinary tract infection (UTI) dataset of 110 individuals revealed a complex pattern of polymicrobial expression, including sub-types of UTIs, cases of bacterial vaginosis, and evidence of no underlying disease. Importantly, compared to the initial UTI study that restricted the search database to a manually curated list of 20 genera, ProteoStorm identified additional genera that were previously unreported, including a case of infection with the rare pathogen Propionimicrobium.

Expressed protein profile of a Tectomicrobium and other microbial symbionts in the marine sponge Aplysina aerophoba as evidenced by metaproteomics

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.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

A year of monitoring 20 mesophilic full-scale bioreactors reveals the existence of stable but different core microbiomes in bio-waste and wastewater anaerobic digestion systems

BACKGROUND

Anaerobic digestion (AD) is a microbe-driven process of biomass decomposition to CH₄ and CO₂. In addition to renewable and cost-effective energy production, AD has emerged in the European Union as an environmentally friendly model of bio-waste valorisation and nutrient recycling. Nevertheless, due to the high diversity of uncharacterised microbes, a typical AD microbiome is still considered as "dark matter".

RESULTS

Using the high-throughput sequencing of small rRNA gene, and a monthly monitoring of the physicochemical parameters for 20 different mesophilic full-scale bioreactors over 1 year, we generated a detailed view of AD microbial ecology towards a better understanding of factors that influence and shape these communities. By studying the broadly distributed OTUs present in over 80% of analysed samples, we identified putatively important core bacteria and archaea to the AD process that accounted for over 70% of the whole microbial community relative abundances. AD reactors localised at the wastewater treatment plants were shown to operate with distinct core microbiomes than the agricultural and bio-waste treating biogas units. We also showed that both the core microbiomes were composed of low (with average community abundance ≤ 1%) and highly abundant microbial populations; the vast majority of which remains yet uncharacterised, e.g. abundant candidate Cloacimonetes. Using non-metric multidimensional scaling, we observed microorganisms grouping into clusters that well reflected the origin of the samples, e.g. wastewater versus agricultural and bio-waste treating biogas units. The calculated diversity patterns differed markedly between the different community clusters, mainly due to the presence of highly diverse and dynamic transient species. Core microbial communities appeared relatively stable over the monitoring period.

CONCLUSIONS

In this study, we characterised microbial communities in different AD systems that were monitored over a 1-year period. Evidences were shown to support the concept of a core community driving the AD process, whereas the vast majority of dominant microorganisms remain yet to be characterised.

Generalist species drive microbial dispersion and evolution

Microbes form fundamental bases of every Earth ecosystem. As their key survival strategies, some microbes adapt to broad ranges of environments, while others specialize to certain habitats. While ecological roles and properties of such “generalists” and “specialists” had been examined in individual ecosystems, general principles that govern their distribution patterns and evolutionary processes have not been characterized. Here, we thoroughly identified microbial generalists and specialists across 61 environments via meta-analysis of community sequencing data sets and reconstructed their evolutionary histories across diverse microbial groups. This revealed that generalist lineages possess 19-fold higher speciation rates and significant persistence advantage over specialists. Yet, we also detected three-fold more frequent generalist-to-specialist transformations than the reverse transformations. These results support a model of microbial evolution in which generalists play key roles in introducing new species and maintaining taxonomic diversity.

Microbes adapting to broad and specialized ranges of environments (generalists and specialists) have distinct ecological roles and properties. Via meta-analysis of community sequencing datasets, Sriswasdi et al. show that generalists have higher speciation rates and persistence advantage over specialists.

First draft genome sequence of a strain belonging to the Zoogloea genus and its gene expression in situ

The Gram-negative beta-proteobacterium Zoogloea sp. LCSB751 (LMG 29444) was newly isolated from foaming activated sludge of a municipal wastewater treatment plant. Here, we describe its draft genome sequence and annotation together with a general physiological and genomic analysis, as the first sequenced representative of the Zoogloea genus. Moreover, Zoogloea sp. gene expression in its environment is described using metatranscriptomic data obtained from the same treatment plant. The presented genomic and transcriptomic information demonstrate a pronounced capacity of this genus to synthesize poly-β-hydroxyalkanoate within wastewater.

Metatranscriptomic and metagenomic description of the bacterial nitrogen metabolism in waste water wet oxidation effluents

Anaerobic digestion is a common method for reducing the amount of sludge solids in used waters and enabling biogas production. The wet oxidation process (WOX) improves anaerobic digestion by converting carbon into methane through oxidation of organic compounds. WOX produces effluents rich in ammonia, which must be removed to maintain the activity of methanogens. Ammonia removal from WOX could be biologically operated by aerobic granules. To this end, granulation experiments were conducted in 2 bioreactors containing an activated sludge (AS). For the first time, the dynamics of the microbial community structure and the expression levels of 7 enzymes of the nitrogen metabolism in such active microbial communities were followed in regard to time by metagenomics and metatranscriptomics. It was shown that bacterial communities adapt to the wet oxidation effluent by increasing the expression level of the nitrogen metabolism, suggesting that these biological activities could be a less costly alternative for the elimination of ammonia, resulting in a reduction of the use of chemicals and energy consumption in sewage plants. This study reached a strong sequencing depth (from 4.4 to 7.6 Gb) and enlightened a yet unknown diversity of the microorganisms involved in the nitrogen pathway. Moreover, this approach revealed the abundance and expression levels of specialised enzymes involved in nitrification, denitrification, ammonification, dissimilatory nitrate reduction to ammonium (DNRA) and nitrogen fixation processes in AS.

Contributions of Composition and Interactions to Bacterial Respiration Are Reliant on the Phylogenetic Similarity of the Measured Community

Bacterial diversity underpins many ecosystem functions; however, the impact of within-species variation on the relationship between diversity and function remains unclear. Processes involving strain differentiation, such as niche radiation, are often overlooked in studies that focus on phylogenetic variation. This study used bacterial isolates assembled in two comparable microcosm experiments to test how species variation affected ecosystem function. We compared the relationship between diversity and activity (CO₂ production) in increasingly diverse multispecies microcosms and with multiple ecotypes of a single species. The bacteria used were isolated from a low-diversity environment and are species of potential clinical significance such as Pseudomonas aeruginosa. All isolates were profiled for single carbon source utilisation. These data showed an increased breadth of resource use in the multiple ecotypes when compared to the mixed-species. The study observed significantly increasing respiration in more complex mixed-species assemblages, which was not observed when ecotypes of a single species were combined. We further demonstrate that the variation observed in the bacterial activity was due to the roles of each of the constituent isolates; between different species, the interactions between the isolates drove the variation in activity, whilst in single species, assemblage variation was due to which isolates were present. We conclude that both between- and within-species variations play different roles in community function, although through different mechanisms, and should be included in models of changing diversity and ecosystem functioning.

Integrated meta-omic analyses of the gastrointestinal tract microbiome in patients undergoing allogeneic hematopoietic stem cell transplantation

In patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), treatment-induced changes to the gastrointestinal tract (GIT) microbiome have been linked to adverse outcomes, most notably graft-versus-host disease (GvHD). However, it is presently unknown whether this relationship is causal or consequential. Here, we performed an integrated meta-omic analysis to probe deeper into the GIT microbiome changes during allo-HSCT and its accompanying treatments. We used 16S and 18S rRNA gene amplicon sequencing to resolve archaea, bacteria, and eukaryotes within the GIT microbiomes of 16 patients undergoing allo-HSCT for the treatment of hematologic malignancies. These results revealed a major shift in the GIT microbiome after allo-HSCT including a marked reduction in bacterial diversity, accompanied by only limited changes in eukaryotes and archaea. An integrated analysis of metagenomic and metatranscriptomic data was performed on samples collected from a patient before and after allo-HSCT for acute myeloid leukemia. This patient developed severe GvHD, leading to death 9 months after allo-HSCT. In addition to drastically decreased bacterial diversity, the post-treatment microbiome showed a higher overall number and higher expression levels of antibiotic resistance genes (ARGs). One specific Escherichia coli strain causing a paravertebral abscess was linked to GIT dysbiosis, suggesting loss of intestinal barrier integrity. The apparent selection for bacteria expressing ARGs suggests that prophylactic antibiotic administration may adversely affect the overall treatment outcome. We therefore assert that such analyses including information about the selection of pathogenic bacteria expressing ARGs may assist clinicians in "personalizing" regimens for individual patients to improve overall outcomes.

Phage-host associations in a full-scale activated sludge plant during sludge bulking

Sludge bulking, a notorious microbial issue in activated sludge plants, is always accompanied by dramatic changes in the bacterial community. Despite large numbers of phages in sludge systems, their responses to sludge bulking and phage-host associations during bulking are unknown. In this study, high-throughput sequencing of viral metagenomes and bacterial 16S rRNA genes were employed to characterize viral and bacterial communities in a sludge plant under different sludge conditions (sludge volume index (SVI) of 180, 132, and 73 ml/g). Bulking sludges (SVI > 125 ml/g) taken about 10 months apart exhibited similar bacterial and viral composition. This reflects ecological resilience of the sludge microbial community and indicates that changes in viral and bacterial populations correlate closely with each other. Overgrowth of "Candidatus Microthrix parvicella" led to filamentous bulking, but few corresponding viral genotypes were identified. In contrast, sludge viromes were characterized by numerous contigs associated with "Candidatus Accumulibacter phosphatis," suggesting an abundance of corresponding phages in the sludge viral community. Notably, while nitrifiers (mainly Nitrosomonadaceae and Nitrospiraceae) declined significantly along with sludge bulking, their corresponding viral contigs were identified more frequently and with greater abundance in the bulking viromes, implying that phage-mediated lysis might contribute to the loss of autotrophic nitrifiers under bulking conditions.

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation

Metagenomics-based studies of mixed microbial communities are impacting biotechnology, life sciences and medicine. Computational binning of metagenomic data is a powerful approach for the culture-independent recovery of population-resolved genomic sequences, i.e. from individual or closely related, constituent microorganisms. Existing binning solutions often require a priori characterized reference genomes and/or dedicated compute resources. Extending currently available reference-independent binning tools, we developed the BusyBee Web server for the automated deconvolution of metagenomic data into population-level genomic bins using assembled contigs (Illumina) or long reads (Pacific Biosciences, Oxford Nanopore Technologies). A reversible compression step as well as bootstrapped supervised binning enable quick turnaround times. The binning results are represented in interactive 2D scatterplots. Moreover, bin quality estimates, taxonomic annotations and annotations of antibiotic resistance genes are computed and visualized. Ground truth-based benchmarks of BusyBee Web demonstrate comparably high performance to state-of-the-art binning solutions for assembled contigs and markedly improved performance for long reads (median F1 scores: 70.02-95.21%). Furthermore, the applicability to real-world metagenomic datasets is shown. In conclusion, our reference-independent approach automatically bins assembled contigs or long reads, exhibits high sensitivity and precision, enables intuitive inspection of the results, and only requires FASTA-formatted input. The web-based application is freely accessible at: https://ccb-microbe.cs.uni-saarland.de/busybee.

Properties of alternative microbial hosts used in synthetic biology: towards the design of a modular chassis

The chassis is the cellular host used as a recipient of engineered biological systems in synthetic biology. They are required to propagate the genetic information and to express the genes encoded in it. Despite being an essential element for the appropriate function of genetic circuits, the chassis is rarely considered in their design phase. Consequently, the circuits are transferred to model organisms commonly used in the laboratory, such as Escherichia coli, that may be suboptimal for a required function. In this review, we discuss some of the properties desirable in a versatile chassis and summarize some examples of alternative hosts for synthetic biology amenable for engineering. These properties include a suitable life style, a robust cell wall, good knowledge of its regulatory network as well as of the interplay of the host components with the exogenous circuits, and the possibility of developing whole-cell models and tuneable metabolic fluxes that could allow a better distribution of cellular resources (metabolites, ATP, nucleotides, amino acids, transcriptional and translational machinery). We highlight Pseudomonas putida, widely used in many different biotechnological applications as a prominent organism for synthetic biology due to its metabolic diversity, robustness and ease of manipulation.

Multi-omics analysis provides insight to the Ignicoccus hospitalis-Nanoarchaeum equitans association

BACKGROUND

Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted 'omics' analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea.

METHODS

We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized using interaction maps generated using STRING.

RESULTS

Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis-N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans.

CONCLUSIONS

This multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis-N. equitans association.

GENERAL SIGNIFICANCE

Our study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies.

IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses

Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).

A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro.

In recent years, meta-omic (including metatranscriptomic and metaproteomic) techniques have been adopted as complementary approaches to metagenomic sequencing to study functional characteristics and dynamics of microbial communities, aiming at a holistic understanding of a community to respond to the changes in the environment. Currently, metaproteomic data are largely analyzed using the bioinformatics tools originally designed in bottom-up proteomics. In particular, recent metaproteomic studies employed a metagenome-guided approach, in which complete or fragmental protein-coding genes were first predicted from metagenomic sequences (i.e., contigs or scaffolds), acquired from the matched community samples, and predicted protein sequences were then used in peptide identification. A key challenge of this approach is that the protein coding genes predicted from assembled metagenomic contigs can be incomplete and fragmented due to the complexity of metagenomic samples and the short reads length in metagenomic sequencing. To address this issue, in this paper, we present a graph-centric approach that exploits the de bruijn graph structure reported by metagenome assembly algorithms to improve metagenome-guided peptide and protein identification in metaproteomics. We show that our method can identify much more peptides and proteins, improving the characterization of the proteins expressed in the microbial communities.

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

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.

Identification, Recovery, and Refinement of Hitherto Undescribed Population-Level Genomes from the Human Gastrointestinal Tract

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 VIZBIN post hoc. The second approach is particularly well-suited for the study of specific taxa and employs VIZBIN de 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.

Genomic and in situ investigations of the novel uncultured Chloroflexi associated with 0092 morphotype filamentous bulking in activated sludge

Overgrowth of filamentous bacteria in activated sludge wastewater treatment plants (WWTPs) leads to impaired sludge settleability, a condition known as bulking, which is a common operational problem worldwide. Filaments with the Eikelboom 0092 morphotype are commonly associated with such bulking episodes. Members of the uncultured B45 phylotype, which is embraced within the phylum Chloroflexi, were recently shown to exhibit this morphology. Although these organisms are among the most abundant populations recorded in activated sludge processes, nothing is known about their metabolic characteristics. In this study, a genome sequence, representing the B45 phylotype, was retrieved from a metagenome generated from an activated sludge WWTP. The genome consisted of two chromosomes and one plasmid, which were 4.0, 1.0 and 0.04 Mbps in size, respectively. A metabolic model was constructed for this organism, based on annotation of its genome, showing its ability to generate energy by respiration, utilizing oxygen, nitrite or nitrous oxide as electron acceptors, or by fermentation of sugars. The ability of B45 members to ferment sugars under anaerobic conditions was validated in situ with microautoradiography-fluorescence in situ hybridization. The provisional name of 'Candidatus Promineofilum breve' is proposed for this species. This study represents the first detailed information on an uncultured genus of filamentous organisms from activated sludge.