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Metabolic exchange is widespread in natural microbial communities and an important driver of ecosystem structure and diversity, yet it remains unclear what determines whether microbes evolve division of labor or maintain metabolic autonomy. Here we use a mechanistic model to study how metabolic strategies evolve in a constant, one resource environment, when metabolic networks are allowed to freely evolve. We find that initially identical ancestral communities of digital organisms follow different evolutionary trajectories, as some communities become dominated by a single, autonomous lineage, while others are formed by stably coexisting lineages that cross-feed on essential building blocks. Our results show how without presupposed cellular trade-offs or external drivers such as temporal niches, diverse metabolic strategies spontaneously emerge from the interplay between ecology, spatial structure, and metabolic constraints that arise during the evolution of metabolic networks. Thus, in the long term, whether microbes remain autonomous or evolve metabolic division of labour is an evolutionary contingency. Using a mechanistic individual-based model, Meijer et al. show that diverse metabolic strategies emerge through the interplay between evolved metabolic constraints and ecology, without preconditions that assume cellular trade-offs or external drivers for metabolic division of labour. [ABSTRACT FROM AUTHOR]

• CECscreen is an annotation database for CECs in human biological samples. • CECscreen includes 70,397 structures, 306,071 simulated metabolites, and metadata. • CECscreen is openly accessible and is incorporated into Metfrag. • CECscreen facilitates large-scale detection of chemicals in exposome research. Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans. This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples. The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard. All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag. The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g. , MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features. [ABSTRACT FROM AUTHOR]

Background: Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe "wild types" (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms. Results: We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode. Conclusions: All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes. [ABSTRACT FROM AUTHOR]

Microbial evolution is driven by rapid changes in gene content mediated by horizontal gene transfer (HGT). While mobile genetic elements (MGEs) are important drivers of gene flux, the nanobiome—the zoo of Darwinian replicators that depend on microbial hosts—remains poorly characterised. New approaches are necessary to increase our understanding beyond MGEs shaping individual populations, towards their impacts on complex microbial communities. A bioinformatic pipeline (xenoseq) was developed to cross-compare metagenomic samples from microbial consortia evolving in parallel, aimed at identifying MGE dissemination, which was applied to compost communities which underwent periodic mixing of MGEs. We show that xenoseq can distinguish movement of MGEs from demographic changes in community composition that otherwise confounds identification, and furthermore demonstrate the discovery of various unexpected entities. Of particular interest was a nanobacterium of the candidate phylum radiation (CPR) which is closely related to a species identified in groundwater ecosystems (Candidatus Saccharibacterium), and appears to have a parasitic lifestyle. We also highlight another prolific mobile element, a 313 kb plasmid hosted by a Cellvibrio lineage. The host was predicted to be capable of nitrogen fixation, and acquisition of the plasmid coincides with increased ammonia production. Taken together, our data show that new experimental strategies combined with bioinformatic analyses of metagenomic data stand to provide insight into the nanobiome as a driver of microbial community evolution. [ABSTRACT FROM AUTHOR]

This study aimed to identify antimicrobial contaminants in the aquatic environment with effect-directed analysis. Wastewater influent, effluent, and surface water (up- and downstream of the discharge location) were sampled at two study sites. The samples were enriched, subjected to high-resolution fractionation, and the resulting 80 fractions were tested in an antibiotics bioassay. The resulting bioactive fractions guided the suspect and nontargeted identification strategy in the high-resolution mass spectrometry data that was recorded in parallel. Chemical features were annotated with reference databases, assessed on annotation quality, and assigned identification confidence levels. To identify antibiotic metabolites, Phase I metabolites were predicted in silico for over 500 antibiotics and included as a suspect list. Predicted retention times and fragmentation patterns reduced the number of annotations to consider for confirmation testing. Overall, the bioactivity of three fractions could be explained by the identified antibiotics (clarithromycin and azithromycin) and an antibiotic metabolite (14-OH(R) clarithromycin), explaining 78% of the bioactivity measured at one study site. The applied identification strategy successfully identified antibiotic metabolites in the aquatic environment, emphasizing the need to include the toxic effects of bioactive metabolites in environmental risk assessments. [Display omitted] • An antibiotic bioassay confirmed bioactive fractions in fractionated water samples. • Chemical features related to bioactivity were prioritized for identification. • Phase I metabolites were predicted in silico for over 500 antibiotics. • A bioactive metabolite was identified as 14-OH(R) clarithromycin. • 78% of the measured bioactivity was explained by the identified compounds. [ABSTRACT FROM AUTHOR]

It has been suggested that domestic animals can serve as sentinels for human exposures. In this study our objectives were to demonstrate that i) silicone collars can be used to measure environmental exposures of (domestic) animals, and that ii) domestic animals can be used as sentinels for human residential exposure. For this, we simultaneously measured polycyclic aromatic hydrocarbons (PAHs) using silicone bands worn by 30 pet cats (collar) and their owner (wristband). Collars and wristbands were worn for 7 days and analyzed via targeted Gas Chromatography-Mass Spectrometry (GC-MS). Demographics and daily routines were collected for humans and cats. Out of 16 PAHs, 9 were frequently detected (>50% of samples) in both wristbands and collars, of which Phenanthrene and Fluorene were detected in all samples. Concentrations of wristbands and collars were moderately correlated for these 9 PAHs (Median Spearman's r = 0.51 (range 0.16–0.68)). Determinants of PAH concentrations of cats and humans showed considerable overlap, with vacuum cleaning resulting in higher exposures and frequent changing of bed sheets in lower exposures. This study adds proof-of-principle data for the use of silicone collars to measure (domestic) animal exposure and shows that cats can be used as sentinels for human residential exposure. [Display omitted] • 16 PAHs were analyzed in wristbands worn by humans and their pet cats. • PAH concentrations were moderately correlated between cat collars and human wristbands. • Several determinants of exposure were similar between cats and humans. • Fluoranthene, phenanthrene, fluorene and naphthalene were detected in all 60 samples. • Cats can be used as sentinels for residential exposure. [ABSTRACT FROM AUTHOR]

• Chemicals of Emerging Concern (CECs) are a growing concern in HBM. • Suspect and non-targeted screening (NTS) approaches offer new capabilities for capturing CECs in human matrices. • These new approaches applied to HBM come with a number of technical and scientific issues to be addressed. • Harmonisation is required at international level to reinforce NTS methods comparability and performance assessment. Large-scale suspect and non-targeted screening approaches based on high-resolution mass spectrometry (HRMS) are today available for chemical profiling and holistic characterisation of biological samples. These advanced techniques allow the simultaneous detection of a large number of chemical features, including markers of human chemical exposure. Such markers are of interest for biomonitoring, environmental health studies and support to risk assessment. Furthermore, these screening approaches have the promising capability to detect chemicals of emerging concern (CECs), document the extent of human chemical exposure, generate new research hypotheses and provide early warning support to policy. Whilst of growing importance in the environment and food safety areas, respectively, CECs remain poorly addressed in the field of human biomonitoring. This shortfall is due to several scientific and methodological reasons, including a global lack of harmonisation. In this context, the main aim of this paper is to present an overview of the basic principles, promises and challenges of suspect and non-targeted screening approaches applied to human samples as this specific field introduce major specificities compared to other fields. Focused on liquid chromatography coupled to HRMS-based data acquisition methods, this overview addresses all steps of these new analytical workflows. Beyond this general picture, the main activities carried out on this topic within the particular framework of the European Human Biomonitoring initiative (project HBM4EU, 2017–2021) are described, with an emphasis on harmonisation measures. [ABSTRACT FROM AUTHOR]