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Wastewater treatment plants (WWTPs) collect wastewater from various sources for a multi-step treatment process. By mixing a large variety of bacteria and promoting their proximity, WWTPs constitute potential hotspots for the emergence of antibiotic resistant bacteria. Concerns have been expressed regarding the potential of WWTPs to spread antibiotic resistance genes (ARGs) from environmental reservoirs to human pathogens. We utilized epicPCR (Emulsion, Paired Isolation and Concatenation PCR) to detect the bacterial hosts of ARGs in two WWTPs. We identified the host distribution of four resistance-associated genes (tetM, int1, qacEΔ1and blaOXA-58) in influent and effluent. The bacterial hosts of these resistance genes varied between the WWTP influent and effluent, with a generally decreasing host range in the effluent. Through 16S rRNA gene sequencing, it was determined that the resistance gene carrying bacteria include both abundant and rare taxa. Our results suggest that the studied WWTPs mostly succeed in decreasing the host range of the resistance genes during the treatment process. Still, there were instances where effluent contained resistance genes in bacterial groups not carrying these genes in the influent. By permitting exhaustive profiling of resistance-associated gene hosts in WWTP bacterial communities, the application of epicPCR provides a new level of precision to our resistance gene risk estimates.

Antibiotic resistance among bacteria is a growing problem worldwide, and wastewater treatment plants have been considered as one of the major contributors to the dissemination of antibiotic resistance to the environment. There is a lack of comprehensive quantitative molecular data on extensive numbers of antibiotic resistance genes (ARGs) in different seasons with a sampling strategy that would cover both incoming and outgoing water together with the excess sludge that is removed from the process. In order to fill that gap we present a highly parallel quantitative analysis of ARGs and horizontal gene transfer potential over four seasons at an urban wastewater treatment plant using a high-throughput qPCR array. All analysed transposases and two-thirds of primer sets targeting ARGs were detected in the wastewater. The relative abundance of most of the genes was highest in influent and lower in effluent water and sludge. The resistance profiles of the samples cluster by sample location with a shift from raw influent through the final effluents and dried sludge to the sediments. Wastewater discharge enriched only a few genes, namely Tn25 type transposase gene and clinical class 1 integrons, in the sediment near the discharge pipe, but those enriched genes may indicate a potential for horizontal gene transfer. This work gives a broad insight into the antibiotic resistance gene dynamics in an urban wastewater treatment plant and the effect of purified wastewater release on the environment. Graphical Abstract Figure. This work gives a broad insight into the antibiotic resistance gene dynamics in an urban wastewater treatment plant and the effect of purified wastewater release on the environment.

Antibiotics are commonly used in aquaculture and they can change the environmental resistome by increasing antibiotic resistance genes (ARGs). Sediment samples were collected from two fish farms located in the Northern Baltic Sea, Finland, and from a site outside the farms (control). The sediment resistome was assessed by using a highly parallel qPCR array containing 295 primer sets to detect ARGs, mobile genetic elements and the 16S rRNA gene. The fish farm resistomes were enriched in transposon and integron associated genes and in ARGs encoding resistance to antibiotics which had been used to treat fish at the farms. Aminoglycoside resistance genes were also enriched in the farm sediments despite the farms not having used aminoglycosides. In contrast, the total relative abundance values of ARGs were higher in the control sediment resistome and they were mainly genes encoding efflux pumps followed by beta-lactam resistance genes, which are found intrinsically in many bacteria. This suggests that there is a natural Baltic sediment resistome. The resistome associated with fish farms can be from native ARGs enriched by antibiotic use at the farms and/or from ARGs and mobile elements that have been introduced by fish farming. Antibiotic resistome associated with fish farms can be from native antibiotic resistance genes (ARGs) enriched by antibiotic use at the farms and/or from ARGs and mobile elements that have been introduced by fish farming. Graphical Abstract Figure. Antibiotic resistome associated with fish farms can be from native antibiotic resistance genes (ARGs) enriched by antibiotic use at the farms and/or from ARGs and mobile elements that have been introduced by fish farming.

Basic local-alignment search tool (BLAST) is a versatile and commonly used sequence analysis tool in bioinformatics. BLAST permits fast and flexible sequence similarity searches across nucleotide and amino acid sequences, leading to diverse applications such as protein domain identification, orthology searches, and phylogenetic annotation. Most BLAST implementations are command line tools which produce output as comma-separated values files. However, a portable, modular and embeddable implementation of a BLAST-like algorithm, is still missing from our toolbox. Here we present nsearch, a command line tool and C++11 library which provides BLAST-like functionality that can easily be embedded in any application. As an example of this portability we present Blaster which leverages nsearch to provide native BLAST-like functionality for the R programming language, as well as npysearch which provides similar functionality for Python. These packages permit embedding BLAST-like functionality into larger frameworks such as Shiny or Django applications. Benchmarks show that nsearch, npysearch, and Blaster are comparable in speed and accuracy to other commonly used modern BLAST implementations such as VSEARCH and BLAST+. We envision similar implementations of nsearch for other languages commonly used in data science such as Julia to facilitate sequence similarity comparisons. Nsearch, Blaster and npysearch are free to use under the BSD 3.0 license and available on Github Conda, CRAN (Blaster) and PyPi (npysearch).

Background Designing oligonucleotide primers and probes is one of the key steps of various laboratory experiments such as multiplexed PCR or digital multiplexed ligation assays. When designing multiplexed primers and probes to complex, heterogeneous DNA data sets, an optimization problem can arise where the smallest number of oligonucleotides covering the largest diversity of the input dataset needs to be identified. Tools that provide this optimization in an efficient manner for large input data are currently lacking. Results Here we present Prider, an R package for designing primers and probes with a nearly optimal coverage for complex and large sequence sets. Prider initially prepares a full primer coverage of the input sequences, the complexity of which is subsequently reduced by removing components of high redundancy or narrow coverage. The primers from the resulting near-optimal coverage are easily accessible as data frames and their coverage across the input sequences can be visualised as heatmaps using Prider’s plotting function. Prider permits efficient design of primers to large DNA datasets by scaling linearly to increasing sequence data, regardless of the diversity of the dataset. Conclusions Prider solves a recalcitrant problem in molecular diagnostics: how to cover a maximal sequence diversity with a minimal number of oligonucleotide primers or probes. The combination of Prider with highly scalable molecular quantification techniques will permit an unprecedented molecular screening capability with immediate applicability in fields such as clinical microbiology, epidemic virus surveillance or antimicrobial resistance surveillance.

Many microbial communities are characterized by high genetic diversity. 16S ribosomal RNA sequencing can determine community members, and metagenomics can determine the functional diversity, but resolving the functional role of individual cells in high throughput remains an unsolved challenge. Here, we describe epicPCR (Emulsion, Paired Isolation and Concatenation PCR), a new technique that links functional genes and phylogenetic markers in uncultured single cells, providing a throughput of hundreds of thousands of cells with costs comparable to one genomic library preparation. We demonstrate the utility of our technique in a natural environment by profiling a sulfate-reducing community in a freshwater lake, revealing both known sulfate reducers and discovering new putative sulfate reducers. Our method is adaptable to any conserved genetic trait and translates genetic associations from diverse microbial samples into a sequencing library that answers targeted ecological questions. Potential applications include identifying functional community members, tracing horizontal gene transfer networks and mapping ecological interactions between microbial cells.

Persistence and dispersal of antibiotic resistance genes (ARGs) are important factors for assessing ARG risk in aquaculture environments. Here, we quantitatively detected ARGs for sulphonamides (sul1 and sul2) and trimethoprim (dfrA1) and an integrase gene for a class 1 integron (intI1) at aquaculture facilities in the northern Baltic Sea, Finland. The ARGs persisted in sediments below fish farms at very low antibiotic concentrations during the 6-year observation period from 2006 to 2012. Although the ARGs persisted in the farm sediments, they were less prevalent in the surrounding sediments. The copy numbers between the sul1 and intI1 genes were significantly correlated suggesting that class 1 integrons may play a role in the prevalence of sul1 in the farm sediments through horizontal gene transfer. In conclusion, the presence of ARGs may limit the effectiveness of antibiotics in treating fish illnesses, thereby causing a potential risk to the aquaculture industry. However, the restricted presence of ARGs at the farms is unlikely to cause serious effects in the northern Baltic Sea sediment environments around the farms.

Glyphosate-based herbicides (GBHs) are widely used for controlling weeds by inhibiting the shikimate pathway. However, the effects of GBH on non-target organisms, such as shikimate pathway-containing microbes, are understudied. Furthermore, the complex interactions between GBH and fertilizers are difficult to predict. Hence, we experimentally investigated the effects of GBH and phosphate fertilizer on the composition of endophytic bacterial communities of potato, faba bean and oat during early and late summer using 16S rRNA gene sequencing, and on plant growth in late summer. GBH treatments significantly affected bacterial communities of early and late summer potato roots and late summer faba bean roots, while phosphate treatments significantly affected bacterial communities of late summer potato leaves, tubers and early summer faba bean leaves. The treatments reduced bacterial diversity in potato and oat and the abundance of putatively beneficial bacteria in potato and faba bean. However, these treatments increased the aboveground biomass of all crops. Thus, agrochemicals had variable effects across crops, tissues and growth stages. While improved crop yield is often prioritized in chemical-intensive farming, the effects of microbiome shifts on crop health need further investigation.

Low concentrations of antibiotics have numerous effects on bacteria. However, it is unknown whether ecological factors such as trophic interactions and spatial structuring influence the effects of low concentrations of antibiotics on multispecies microbial communities. Here, we address this question by investigating the effects of low antibiotic concentration on community composition and horizontal transfer of an antibiotic resistance plasmid in a 62-strain bacterial community in response to manipulation of the spatial environment and presence of predation. The strong effects of antibiotic treatment on community composition depend on the presence of predation and spatial structuring that have strong community effects on their own. Overall, we find plasmid transfer to diverse recipient taxa. Plasmid transfer is likely to occur to abundant strains, occurs to a higher number of strains in the presence of antibiotic, and also occurs to low-abundance strains in the presence of spatial structures. These results fill knowledge gaps concerning the effects of low antibiotic concentrations in complex ecological settings. Johannes Cairns et al. show that spatial structure and predation shape the ways in which a low concentration of antibiotics alters the composition and diversity of bacterial community. This study suggests that ecological factors influence how antibiotic resistance spreads.

ObjectivesThis study explored self-reported knowledge and interest to learn more about medicines research, development and health technology assessment among Finnish general public. It also aimed to define possible knowledge gaps and needs for public education regarding these topics.DesignOnline survey with 503 participants. The questionnaire was originally developed as a part of the Needs Assessment Work Package of the European Patients’ Academy on Therapeutic Innovation Project. The survey was carried out in Finland in 2019.MethodsThe survey was conducted as an online survey by Kantar TNS Gallup Forum online panel. The data were analysed by using the freely available programming language R. Relationships between the demographic characteristics (such as age, gender and education level) of respondents and their knowledge or interest in medicines research and development were determined using Pearson’s χ2 tests. Statistically significant responses of demographic characteristics in the respondents’ knowledge or interest in medicines research were determined by logistic regression.ResultsOf the 503 respondents (age 16–64) only 12% reported having good or very good knowledge of medicines research and development in general. Regarding health technology assessment, pharmacoeconomics and regulation, the percentage of respondents reporting good or very good knowledge was as low as 8%. Respondents were most interested in learning more about predictive and personalised medicine (47%) and least interested in medicines regulation (30%) and pharmacoeconomics (31%).ConclusionsSelf-reported knowledge about medicines research and development and health technology assessment appears to be very low in Finland. Patient and public participation is recognised as an important and essential element in up-to-date medical research and assessment of new treatments. In order to participate as an active and equal partner in these processes, the public needs more information and education in these topics.