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Analyzing microbial communities using metagenomes is a powerful approach to understand compositional structures and functional connections in anaerobic digestion (AD) microbiomes. Whereas short-read sequencing approaches based on the Illumina platform result in highly fragmented metagenomes, long-read sequencing leads to more contiguous assemblies. To evaluate the performance of a hybrid approach of these two sequencing approaches we compared the metagenome-assembled genomes (MAGs) resulting from five AD microbiome samples. The samples were taken from reactors fed with short-chain fatty acids at different feeding regimes (continuous and discontinuous) and organic loading rates (OLR). Methanothrix showed a high relative abundance at all feeding regimes but was strongly reduced in abundance at higher OLR, when Methanosarcina took over. The bacterial community composition differed strongly between reactors of different feeding regimes and OLRs. However, the functional potential was similar regardless of feeding regime and OLR. The hybrid sequencing approach using Nanopore long-reads and Illumina MiSeq reads improved assembly statistics, including an increase of the N50 value (on average from 32 to 1740 kbp) and an increased length of the longest contig (on average from 94 to 1898 kbp). The hybrid approach also led to a higher share of high-quality MAGs and generated five potentially circular genomes while none were generated using MiSeq-based contigs only. Finally, 27 hybrid MAGs were reconstructed of which 18 represent potentially new species—15 of them bacterial species. During pathway analysis, selected MAGs revealed similar gene patterns of butyrate degradation and might represent new butyrate-degrading bacteria. The demonstrated advantages of adding long reads to metagenomic analyses make the hybrid approach the preferable option when dealing with complex microbiomes.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

Developing cost-efficient biotechnological processes is a major challenge in replacing fossil-based industrial production processes. The remarkable progress in genetic engineering ensures efficient and fast tailoring of microbial metabolism for a wide range of bioconversions. However, improving intrinsic properties such as tolerance, handling, growth, and substrate consumption rates is still challenging. At the same time, synthetic biology tools are becoming easier applicable and transferable to nonmodel organisms. These trends have resulted in the exploitation of new and unconventional microbial systems with sophisticated properties, which render them promising hosts for the bio-based industry. Here, we highlight the metabolic and cellular capabilities of representative prokaryotic newcomers and discuss the potential and drawbacks of these hosts for industrial application.

Natural microbial communities in soils are highly diverse, allowing for rich networks of microbial interactions to unfold. Identifying key players in these networks is difficult as the distribution of microbial diversity at the local scale is typically non-uniform, and is the outcome of both abiotic environmental factors and microbial interactions. Here, using spatially resolved microbial presence-absence data along an aquifer transect contaminated with hydrocarbons, we combined co-occurrence analysis with association rule mining to identify potential keystone species along the hydrocarbon degradation process. Derived co-occurrence networks were found to be of a modular structure, with modules being associated with specific spatial locations and metabolic activity along the contamination plume. Association rules identify species that never occur without another, hence identifying potential one-sided cross-feeding relationships. We find that hub nodes in the rule network appearing in many rules as targets qualify as potential keystone species that catalyze critical transformation steps and are able to interact with varying partners. By contrasting analysis based on data derived from bulk samples and individual soil particles, we highlight the importance of spatial sample resolution. While individual inferred interactions are hypothetical in nature, requiring experimental verification, the observed global network patterns provide a unique first glimpse at the complex interaction networks at work in the microbial world.

Metagenomics analysis revealing the composition and functional repertoire of complex microbial communities typically relies on large amounts of sequence data. Numerous analysis strategies and computational tools are available for their analysis. Fully integrated automated analysis pipelines such as MG-RAST or MEGAN6 are user-friendly but not designed for integrating specific knowledge on the biological system under study. In order to facilitate the consideration of such knowledge, we introduce a modular, adaptable analysis pipeline combining existing tools. We applied the novel pipeline to simulated mock data sets focusing on anaerobic digestion microbiomes and compare results to those obtained with established automated analysis pipelines. We find that the analysis strategy and choice of tools and parameters have a strong effect on the inferred taxonomic community composition, but not on the inferred functional profile. By including prior knowledge, computational costs can be decreased while improving result accuracy. While automated off-the-shelf analysis pipelines are easy to apply and require no knowledge on the microbial system under study, custom-made pipelines require more preparation time and bioinformatics expertise. This extra effort is minimized by our modular, flexible, custom-made pipeline, which can be adapted to different scenarios and can take available knowledge on the microbial system under study into account.

Medium-chain carboxylates such as n-caproate and n-caprylate are valuable chemicals, which can be produced from renewable feedstock by anaerobic fermentation and lactate-based microbial chain elongation. Acidogenic microbiota involved in lactate-based chain elongation and their interplay with lactic acid bacteria have not been characterized in detail yet. Here, the metabolic and community dynamics were studied in a continuous bioreactor with xylan and lactate as sole carbon sources. Four succession stages were observed during 148 days of operation. After an adaptation period of 36 days, a relatively stable period of 28 days (stage I) was reached with n-butyrate, n-caproate and n-caprylate productivities of 7.2, 8.2 and 1.8 gCOD L–1 d–1, respectively. After a transition period, the process changed to another period (stage II), during which 46% more n-butyrate, 51% less n-caproate and 67% less n-caprylate were produced. Co-occurrence networks of species based on 16S rRNA amplicon sequences and correlations with process parameters were analyzed to infer ecological interactions and potential metabolic functions. Diverse functions including hydrolysis of xylan, primary fermentation of xylose to acids (e.g., to acetate by Syntrophococcus, to n-butyrate by Lachnospiraceae, and to lactate by Lactobacillus) and chain-elongation with lactate (by Ruminiclostridium 5 and Pseudoramibacter) were inferred from the metabolic network. In stage I, the sub-network characterized by strongest positive correlations was mainly related to the production of n-caproate and n-caprylate. Lactic acid bacteria of the genus Olsenella co-occurred with potentially chain-elongating bacteria of the genus Pseudoramibacter, and their abundance was positively correlated with n-caproate and n-caprylate concentrations. A new sub-network appeared in stage II, which was mainly related to n-butyrate production and revealed a network of different lactic acid bacteria (Bifidobacterium) and potential n-butyrate

Die Rechte und Pflichten von Nachwuchsführungskräften an Hochschulen sind häufig sehr unklar geregelt. Die Ansprüche an die Wissenschaftler sind hoch, der Grad ihrer Selbständigkeit wird aber weitgehend durch die jeweiligen Vorgesetzten bestimmt. Das Zukunftsforum der DECHEMA-Fachgemeinschaft Biotechnologie hat in einer Stellungnahme Leitlinien formuliert, um die Verantwortung und Rahmenbedingungen der Arbeit von Nachwuchsführungskräften besser zu regeln.

One major interest in soil systems ecology is to maintain ecosystem functions. As soil is exposed to disturbances of different spatial configurations, identifying disturbance characteristics that still allow for maintaining functions is crucial. In macro-ecology, the influence of fragmentation on ecosystems is continuously debated, especially in terms of extinction thresholds on the landscape scale. Whether this influence is positive or negative depends on the considered type of fragmentation: habitat fragmentation often promotes population extinction, whereas spatially fragmented disturbances reduce extinction probability in many cases. In this study, we make use of these concepts to analyze how spatial disturbance characteristics determine functional resilience on the microscale. We used the numerical model eColony considering bacterial growth, substrate consumption, and dispersal for analyzing the dynamic response of biodegradation as an exemplary important microbial ecosystem function to disturbance events. We systematically varied the frequency of the disturbance events, and the size and fragmentation of the disturbed area. We found that the influence of the disturbance size on functional recovery depends on the spatial fragmentation of the disturbance, indicating that to some extent disturbance size can be compensated for by the spatial configuration of the disturbed area. In general, biodegradation performance decreases as the disturbed area increases in size, and becomes more contiguous. However, if a disturbance is highly fragmented, an increase in disturbance size has no influence on biodegradation performance unless the disturbance is critically large. In this case, the functional performance decreases dramatically. Under recurrent disturbances, this critical disturbance size is shifted toward lower values depending on the disturbance frequency. Our results indicate the importance of spatial disturbance characteristics for functional resilience of soil mi

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be speci

For biogas-producing continuous stirred tank reactors, an increase in dilution rate increases the methane production rate as long as substrate input can be converted fully. However, higher dilution rates necessitate higher specific microbial growth rates, which are assumed to have a strong impact on the apparent microbial biomass yield due to cellular maintenance. To test this, we operated two reactors at 37°C in parallel at dilution rates of 0.18 and 0.07 days-1 (hydraulic retention times of 5.5 and 14 days, doubling times of 3.9 and 9.9 days in steady state) with identical inoculum and a mixture of volatile fatty acids as sole carbon sources. We evaluated the performance of the Anaerobic Digestion Model No. 1 (ADM1), a thermodynamic black box approach (TBA), and dynamic flux balance analysis (dFBA), to describe the experimental observations. All models overestimated the impact of dilution rate on the apparent microbial biomass yield when using default parameter values. Based on our analysis, a maintenance coefficient value below 0.2 kJ per carbon mole of microbial biomass per hour should be used for the TBA, corresponding to 0.12 mmol ATP per gram dry weight per hour for dFBA, which strongly deviates from the value of 9.8 kJ Cmol h-1 that has been suggested to apply to all anaerobic microorganisms at 37°C. We hypothesized that a decrease in dilution rate might select taxa with minimized maintenance expenditure. However, no major differences in the dominating taxa between the reactors were observed based on amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism analysis of mcrA genes. Surprisingly, Methanosaeta dominated over Methanosarcina even at a dilution rate of 0.18 days-1, which contradicts previous model expectations. Furthermore, only 23–49% of the bacterial reads could be assigned to known syntrophic fatty acid oxidizers, indicating that unknown members of this functional group remain to be discovered. In conclusion, m

Ammonia inhibition is an important reason for reactor failures and economic losses in anaerobic digestion. Its impact on acetic acid degradation is well-studied, while its effect on propionic and butyric acid degradation has received little attention and is consequently not considered in the Anaerobic Digestion Model No. 1 (ADM1). To compare ammonia inhibition of the degradation of these three volatile fatty acids (VFAs), we fed a mixture of them as sole carbon source to three continuous stirred tank reactors (CSTRs) and increased ammonium bicarbonate concentrations in the influent from 52 to 277 mM. The use of this synthetic substrate allowed for the determination of degradation efficiencies for the individual acids. While butyric acid degradation was hardly affected by the increase of ammonia concentration, propionic acid degradation turned out to be even more inhibited than acetic acid degradation with degradation efficiencies dropping to 31 and 65% for propionic and acetic acid, respectively. The inhibited reactors acclimatized and approximated pre-disturbance degradation efficiencies toward the end of the experiment, which was accompanied by strong microbial community shifts, as observed by amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism (T-RFLP) of mcrA genes. The acetoclastic methanogen Methanosaeta was completely replaced by Methanosarcina. The propionic acid degrading genus Syntrophobacter was replaced by yet unknown propionic acid degraders. The butyric acid degrading genus Syntrophomonas and hydrogenotrophic Methanomicrobiaceae were hardly affected. We hypothesized that the ammonia sensitivity of the initially dominating taxa Methanosaeta and Syntrophobacter led to a stronger inhibition of the acetic and propionic acid degradation compared to butyric acid degradation and hydrogenotrophic methanogenesis, which were facilitated by the ammonia tolerant taxa Syntrophomonas and Methanomicrobiaceae. We implemented thi

The quantification of relative and absolute taxa-specific abundances in complex microbial communities is crucial for understanding and modeling natural and engineered ecosystems. Many errors inherent to this quantification are, though well-known, still insufficiently addressed and can potentially lead to a completely different interpretation of experimental results. This review provides a critical assessment of next generation sequencing (NGS) of amplicons and quantitative real-time PCR for the quantification of relative and absolute taxa-specific genome abundances. Starting from DNA extraction, the following error sources were considered: DNA extraction efficiency, PCR-associated bias, variance of strain-specific 16S rRNA operon copy number per genome, and analysis of quantitative real-time PCR and NGS data. Tools and methods for estimating and minimizing these errors are presented and demonstrated on published data. In conclusion, amplicon sequencing and qPCR of 16S rRNA genes are valuable tools to determine relative and absolute taxa-specific genome abundances, but results can deviate by several orders of magnitudes from the true values if the reviewed error sources are ignored. Many of these errors can be minimized in a cost-efficient manner and large errors can be easily identified by plausibility checks as shown in this review. Finally, the accurate conversion of genome abundances to cell numbers and microbial biomasses was pointed out as an important future research topic for the integration of PCR-based abundances into mathematical models.

Die Zahl akademischer Nachwuchsführungskräfte ist in den vergangenen zehn Jahren stark gestiegen. Nicht immer geht mit einer Zunahme an Pflichten auch ein Mehr an Rechten einher. Nachwuchsführungskräfte des Zukunftsforums Biotechnologie der DECHEMA unterbreiten Verbesserungsvorschläge.

Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community’s dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes.