Mobilome-driven segregation of the resistome in biological wastewater treatment

Biological wastewater treatment plants (BWWTP) are considered to be hotspots of evolution and subsequent spread of antimicrobial resistance (AMR). Mobile genetic elements (MGEs) promote the mobilization and dissemination of antimicrobial resistance genes (ARGs) and are thereby critical mediators of AMR within the BWWTP microbial community. At present, it is unclear whether specific AMR categories are differentially disseminated via bacteriophages (phages) or plasmids. To understand the segregation of AMR in relation to MGEs, we analyzed meta-omic (metagenomic, metatranscriptomic and metaproteomic) data systematically collected over 1.5 years from a BWWTP. Our results showed a core group of fifteen AMR categories which were found across all timepoints. Some of these AMR categories were disseminated exclusively (bacitracin) or primarily (aminoglycoside, MLS and sulfonamide) via plasmids or phages (fosfomycin and peptide), whereas others were disseminated equally by both MGEs. Combined and timepoint-specific analyses of gene, transcript and protein abundances further demonstrated that aminoglycoside, bacitracin and sulfonamide resistance genes were expressed more by plasmids, in contrast to fosfomycin and peptide AMR expression by phages, thereby validating our genomic findings. In the analyzed communities, the dominant taxon Candidatus Microthrix parvicella was a major contributor to several AMR categories whereby its plasmids primarily mediated aminoglycoside resistance. Importantly, we also found AMR associated with ESKAPEE pathogens within the BWWTP, for which MGEs also contributed differentially to the dissemination of ARGs. Collectively our findings pave the way towards understanding the segmentation of AMR within MGEs, thereby shedding new light on resistome populations and their mediators, essential elements that are of immediate relevance to human health

Discovery, characterization and engineering of bacterial thermostable cellulose- degrading enzymes

Lignocellulose is the most abundant biomass on Earth, and thus our largest organic carbon reservoir. Enzymatic depolymerization of recalcitrant polysaccharides, notably cellulose, is a major cost driver in accessing the renewable energy stored within lignocellulosic biomass. Natural biodiversities may be explored to discover microbial enzymes that have evolved to conquer this task in various environments. We are studying novel enzymes from various biodiversities for the conversion of lignocellulosic materials, using (meta)genome mining and functional screening of fosmid libraries. Targeted biodiversities include deep-sea hot vents of the Arctic mid-ocean ridge (AMOR), the microbiome of the wood-eating Arctic shipworm, thermophilic enrichment cultures from biogas reactors, the Svalbard reindeer gut microbiome, and publicly available metagenomic data from various hot environments. Bioprospecting of the different biodiversities has so far resulted in the discovery of approximately 20 novel enzymes active on lignocellulosic substrates. The significant differences in the origin of the enzymes is reflected in their properties, both beneficial and challenging, and provide us with interesting engineering targets for improved performance in industrial settings. We will present case studies, including work on a novel thermostable cellulase named mgCel6A, with good activity on sulfite-pulped Norway spruce. This enzyme consists of a glycoside hydrolase family 6 catalytic domain (GH6) connected to a family 2 carbohydrate binding module (CBM2) and both the activity profile and predicted structural similarities to known cellulases suggest that mgCel6A is an endo-acting cellulase. Comparison of the full-length enzyme with the catalytic domain showed that the CBM strongly increases substrate binding, while not affecting thermal stability. However, importantly, in reactions with higher substrate concentrations the full-length enzyme was outperformed by the catalytic domain alone, underpinning previous suggestions that CBMs may be less useful in high-consistency bioprocessing. This enzyme is currently being targeted for rational engineering in an effort to decrease the pH optimum and improve the pH stability. Other case studies include GH48 cellulases and lytic polysaccharide monooxygenases (LPMOs). One important aspect of this work concerns the possible assembly of novel enzyme cocktails for lignocellulose processing that can compete with exiting commercial cocktails, which are primarily composed of fungal enzymes. Thus, comparative studies of our most promising bacterial enzymes with their well-known fungal counterparts are also being conducted

Forecasting of a complex microbial community using meta-omics

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

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

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi-omic analyses

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

Functional meta-omics provide critical insights into long- and short-read assemblies

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artifacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only and hybrid assembly approaches on four different metagenomic samples of varying complexity. We demonstrate how different assembly approaches affect gene and protein inference, which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic and metaproteomic data to assess the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions. We propose a reference-independent solution, which exploits the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data

A Productivity-Based Approach to LAN Topology Design

Over the useful life of a LAN, network downtimes will have a negative impact on organizational productivity not included in current Network Topological Design (NTD) problems. We propose a new approach to LAN topological design that includes the impact of these productivity losses into the network design, minimizing not only the CAPEX but also the expected cost of unproductiveness attributable to network downtimes over a certain period of network operation

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

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

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