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Natural microbial communities are complex ecosystems with myriads of interactions. To deal with this complexity, we can apply lessons learned from the study of model organisms and try to find simpler systems that can shed light on the same questions. Here, microbial model communities are essential, as they can allow us to learn about the metabolic interactions, genetic mechanisms and ecological principles governing and structuring communities. A variety of microbial model communities of varying complexity have already been developed, representing different purposes, environments and phenomena. However, choosing a suitable model community for one's research question is no easy task. This review aims to be a guide in the selection process, which can help the researcher to select a sufficiently well-studied model community that also fulfills other relevant criteria. For example, a good model community should consist of species that are easy to grow, have been evaluated for community behaviors, provide simple readouts and - in some cases - be of relevance for natural ecosystems. Finally, there is a need to standardize growth conditions for microbial model communities and agree on definitions of community-specific phenomena and frameworks for community interactions. Such developments would be the key to harnessing the power of simplicity to start disentangling complex community interactions., Competing Interests: The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Author(s).)

Antibiotic resistance is an emerging global health crisis, driven largely by overuse and misuse of antibiotics. However, there are examples in which the production of these antimicrobial agents has polluted the environment with active antibiotic residues, selecting for antibiotic resistant bacteria and the genes they carry. In this work, we have used shotgun metagenomics to investigate the taxonomic structure and resistance gene composition of sludge communities in a treatment plant in Croatia receiving wastewater from production of the macrolide antibiotic azithromycin. We found that the total abundance of antibiotic resistance genes was three times higher in sludge from the treatment plant receiving wastewater from pharmaceutical production than in municipal sludge from a sewage treatment plant in Zagreb. Surprisingly, macrolide resistance genes did not have higher abundances in the industrial sludge, but genes associated with mobile genetic elements such as integrons had. We conclude that at high concentrations of antibiotics, selection may favor taxonomic shifts towards intrinsically resistant species or strains harboring chromosomal resistance mutations rather than acquisition of mobile resistance determinants. Our results underscore the need for regulatory action also within Europe to avoid release of antibiotics into the environment., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

UNITE (https://unite.ut.ee/) is a web-based database and sequence management environment for the molecular identification of fungi. It targets the formal fungal barcode-the nuclear ribosomal internal transcribed spacer (ITS) region-and offers all ∼1 000 000 public fungal ITS sequences for reference. These are clustered into ∼459 000 species hypotheses and assigned digital object identifiers (DOIs) to promote unambiguous reference across studies. In-house and web-based third-party sequence curation and annotation have resulted in more than 275 000 improvements to the data over the past 15 years. UNITE serves as a data provider for a range of metabarcoding software pipelines and regularly exchanges data with all major fungal sequence databases and other community resources. Recent improvements include redesigned handling of unclassifiable species hypotheses, integration with the taxonomic backbone of the Global Biodiversity Information Facility, and support for an unlimited number of parallel taxonomic classification systems.

Motivation: Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data., Results: We evaluated the Metaxa2 Database Builder on 11 commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality., Availability and Implementation: Freely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/., Supplementary Information: Supplementary data are available at Bioinformatics online.

The infant gut microbiota has a high abundance of antibiotic resistance genes (ARGs) compared to adults, even in the absence of antibiotic exposure. Here we study potential sources of infant gut ARGs by performing metagenomic sequencing of breast milk, as well as infant and maternal gut microbiomes. We find that fecal ARG and mobile genetic element (MGE) profiles of infants are more similar to those of their own mothers than to those of unrelated mothers. MGEs in mothers' breast milk are also shared with their own infants. Termination of breastfeeding and intrapartum antibiotic prophylaxis of mothers, which have the potential to affect microbial community composition, are associated with higher abundances of specific ARGs, the composition of which is largely shaped by bacterial phylogeny in the infant gut. Our results suggest that infants inherit the legacy of past antibiotic consumption of their mothers via transmission of genes, but microbiota composition still strongly impacts the overall resistance load.

Soils harbour some of the most diverse microbiomes on Earth and are essential for both nutrient cycling and carbon storage. To understand soil functioning, it is necessary to model the global distribution patterns and functional gene repertoires of soil microorganisms, as well as the biotic and environmental associations between the diversity and structure of both bacterial and fungal soil communities 1-4 . Here we show, by leveraging metagenomics and metabarcoding of global topsoil samples (189 sites, 7,560 subsamples), that bacterial, but not fungal, genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance. We demonstrate that fungi and bacteria show global niche differentiation that is associated with contrasting diversity responses to precipitation and soil pH. Furthermore, we provide evidence for strong bacterial-fungal antagonism, inferred from antibiotic-resistance genes, in topsoil and ocean habitats, indicating the substantial role of biotic interactions in shaping microbial communities. Our results suggest that both competition and environmental filtering affect the abundance, composition and encoded gene functions of bacterial and fungal communities, indicating that the relative contributions of these microorganisms to global nutrient cycling varies spatially.

Background: Antibiotic resistance is considered one of the most urgent threats to modern healthcare, and the role of the environment in resistance development is increasingly recognized. It is often assumed that the abundance and diversity of known resistance genes are representative also for the non-characterized fraction of the resistome in a given environment, but this assumption has not been verified. In this study, this hypothesis is tested, using the resistance gene profiles of 1109 metagenomes from various environments., Results: This study shows that the diversity and abundance of known antibiotic resistance genes can generally predict the diversity and abundance of undescribed resistance genes. However, the extent of this predictability is dependent on the type of environment investigated. Furthermore, it is shown that carefully selected small sets of resistance genes can describe total resistance gene diversity remarkably well., Conclusions: The results of this study suggest that knowledge gained from large-scale quantifications of known resistance genes can be utilized as a proxy for unknown resistance factors. This is important for current and proposed monitoring efforts for environmental antibiotic resistance and has implications for the design of risk ranking strategies and the choices of measures and methods for describing resistance gene abundance and diversity in the environment.

Many pathogenic bacteria utilise sialic acids as an energy source or use them as an external coating to evade immune detection. As such, bacteria that colonise sialylated environments deploy specific transporters to mediate import of scavenged sialic acids. Here, we report a substrate-bound 1.95 Å resolution structure and subsequent characterisation of SiaT, a sialic acid transporter from Proteus mirabilis. SiaT is a secondary active transporter of the sodium solute symporter (SSS) family, which use Na + gradients to drive the uptake of extracellular substrates. SiaT adopts the LeuT-fold and is in an outward-open conformation in complex with the sialic acid N-acetylneuraminic acid and two Na + ions. One Na + binds to the conserved Na2 site, while the second Na + binds to a new position, termed Na3, which is conserved in many SSS family members. Functional and molecular dynamics studies validate the substrate-binding site and demonstrate that both Na + sites regulate N-acetylneuraminic acid transport.

Antibiotic resistance is a global health concern declared by the WHO as one of the largest threats to modern healthcare. In recent years, metagenomic DNA sequencing has started to be applied as a tool to study antibiotic resistance in different environments, including the human microbiota. However, a multitude of methods exist for metagenomic data analysis, and not all methods are suitable for the investigation of resistance genes, particularly if the desired outcome is an assessment of risks to human health. In this review, we outline the current state of methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. In addition, we provide an overview of important considerations related to the analysis of resistance genes, and recommend some of the currently used tools and methods that are best equipped to inform research and clinical practice related to antibiotic resistance., (© The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)