Year-Long Microbial Succession on Microplastics in Wastewater: Chaotic Dynamics Outweigh Preferential Growth.

Microplastics are a globally-ubiquitous aquatic pollutant and have been heavily studied over the last decade. Of particular interest are the interactions between microplastics and microorganisms, especially the pursuit to discover a plastic-specific biome, the so-called plastisphere. To follow this up, a year-long microcosm experimental setup was deployed to expose five different microplastic types (and silica beads control) to activated aerobic wastewater in controlled conditions, with microbial communities being measured four times over the course of the year using 16S rDNA (bacterial) and ITS (fungal) amplicon sequencing. The biofilm community shows no evidence of a specific plastisphere, even after a year of incubation. Indeed, the microbial communities (particularly bacterial) show a clear trend of increasing dissimilarity between plastic types as time increases. Despite little evidence for a plastic-specific community, there was a slight grouping observed for polyolefins (PE and PP) in 6-12-month biofilms. Additionally, an OTU assigned to the genus Devosia was identified on many plastics, increasing over time while showing no growth on silicate (natural particle) controls, suggesting this could be either a slow-growing plastic-specific taxon or a symbiont to such. Both substrate-associated findings were only possible to observe in samples incubated for 6-12 months, which highlights the importance of studying long-term microbial community dynamics on plastic surfaces.

The relationship between land cover and microbial community composition in European lakes.

Microbes are essential for element cycling and ecosystem functioning. However, many questions central to understanding the role of microbes in ecology are still open. Here, we analyze the relationship between lake microbiomes and the lakes' land cover. By applying machine learning methods, we quantify the covariance between land cover categories and the microbial community composition recorded in the largest amplicon sequencing dataset of European lakes available to date. Our results show that the aggregation of environmental features or microbial taxa before analysis can obscure ecologically relevant patterns. We observe a comparatively high covariation of the lakes' microbial community with herbaceous and open spaces surrounding the lake; nevertheless, the microbial covariation with land cover categories is generally lower than the covariation with physico-chemical parameters. Combining land cover and physico-chemical bioindicators identified from the same amplicon sequencing dataset, we develop analytical data structures that facilitate insights into the ecology of the lake microbiome. Among these, a list of the environmental parameters sorted by the number of microbial bioindicators we have identified for them points towards apparent environmental drivers of the lake microbial community composition, such as the altitude, conductivity, and area covered herbaceous vegetation surrounding the lake. Furthermore, the response map, a similarity matrix calculated from the Jaccard similarity of the environmental parameters' lists of bioindicators, allows us to study the ecosystem's structure from the standpoint of the microbiome. More specifically, we identify multiple clusters of highly similar and possibly functionally linked ecological parameters, including one that highlights the importance of the calcium-bicarbonate equilibrium for lake ecology. Taken together, we demonstrate the use of machine learning approaches in studying the interplay between microbial diversity and environmental factors and introduce novel approaches to integrate environmental molecular diversity into monitoring and water quality assessments.

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework.

It is known that microorganisms are essential for the functioning of ecosystems, but the extent to which microorganisms respond to different environmental variables in their natural habitats is not clear. In the current study, we present a methodological framework to quantify the covariation of the microbial community of a habitat and environmental variables of this habitat. It is built on theoretical considerations of systems ecology, makes use of state-of-the-art machine learning techniques and can be used to identify bioindicators. We apply the framework to a data set containing operational taxonomic units (OTUs) as well as more than twenty physicochemical and geographic variables measured in a large-scale survey of European lakes. While a large part of variation (up to 61%) in many environmental variables can be explained by microbial community composition, some variables do not show significant covariation with the microbial lake community. Moreover, we have identified OTUs that act as "multitask" bioindicators, i.e., that are indicative for multiple environmental variables, and thus could be candidates for lake water monitoring schemes. Our results represent, for the first time, a quantification of the covariation of the lake microbiome and a wide array of environmental variables for lake ecosystems. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes that are essential for functioning and stability of lake ecosystems.

gammaBOriS: Identification and Taxonomic Classification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning.

The biology of bacterial cells is, in general, based on information encoded on circular chromosomes. Regulation of chromosome replication is an essential process that mostly takes place at the origin of replication (oriC), a locus unique per chromosome. Identification of high numbers of oriC is a prerequisite for systematic studies that could lead to insights into oriC functioning as well as the identification of novel drug targets for antibiotic development. Current methods for identifying oriC sequences rely on chromosome-wide nucleotide disparities and are therefore limited to fully sequenced genomes, leaving a large number of genomic fragments unstudied. Here, we present gammaBOriS (Gammaproteobacterial oriC Searcher), which identifies oriC sequences on gammaproteobacterial chromosomal fragments. It does so by employing motif-based machine learning methods. Using gammaBOriS, we created BOriS DB, which currently contains 25,827 gammaproteobacterial oriC sequences from 1,217 species, thus making it the largest available database for oriC sequences to date. Furthermore, we present gammaBOriTax, a machine-learning based approach for taxonomic classification of oriC sequences, which was trained on the sequences in BOriS DB. Finally, we extracted the motifs relevant for identification and classification decisions of the models. Our results suggest that machine learning sequence classification approaches can offer great support in functional motif identification.

Deep learning on chaos game representation for proteins.

MOTIVATION: Classification of protein sequences is one big task in bioinformatics and has many applications. Different machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF) and neural networks (NN). All of these methods have in common that protein sequences have to be made machine-readable and comparable in the first step, for which different encodings exist. These encodings are typically based on physical or chemical properties of the sequence. However, due to the outstanding performance of deep neural networks (DNN) on image recognition, we used frequency matrix chaos game representation (FCGR) for encoding of protein sequences into images. In this study, we compare the performance of SVMs, RFs and DNNs, trained on FCGR encoded protein sequences. While the original chaos game representation (CGR) has been used mainly for genome sequence encoding and classification, we modified it to work also for protein sequences, resulting in n-flakes representation, an image with several icosagons. RESULTS: We could show that all applied machine learning techniques (RF, SVM and DNN) show promising results compared to the state-of-the-art methods on our benchmark datasets, with DNNs outperforming the other methods and that FCGR is a promising new encoding method for protein sequences. AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of a basal system for unwinding a bacterial chromosome origin.

Genome duplication is essential for cell proliferation, and DNA synthesis is generally initiated by dedicated replication proteins at specific loci termed origins. In bacteria, the master initiator DnaA binds the chromosome origin (oriC) and unwinds the DNA duplex to permit helicase loading. However, despite decades of research it remained unclear how the information encoded within oriC guides DnaA-dependent strand separation. To address this fundamental question, we took a systematic genetic approach in vivo and identified the core set of essential sequence elements within the Bacillus subtilis chromosome origin unwinding region. Using this information, we then show in vitro that the minimal replication origin sequence elements are necessary and sufficient to promote the mechanical functions of DNA duplex unwinding by DnaA. Because the basal DNA unwinding system characterized here appears to be conserved throughout the bacterial domain, this discovery provides a framework for understanding oriC architecture, activity, regulation and diversity.

Stringent response leads to continued cell division and a temporal restart of DNA replication after initial shutdown in Vibrio cholerae.

Vibrio cholerae is an aquatic bacterium with the potential to infect humans and cause the cholera disease. While most bacteria have single chromosomes, the V. cholerae genome is encoded on two replicons of different size. This study focuses on the DNA replication and cell division of this bi-chromosomal bacterium during the stringent response induced by starvation stress. V. cholerae cells were found to initially shut DNA replication initiation down upon stringent response induction by the serine analog serine hydroxamate. Surprisingly, cells temporarily restart their DNA replication before finally reaching a state with fully replicated single chromosome sets. This division-replication pattern is very different to that of the related single chromosome model bacterium Escherichia coli. Within the replication restart phase, both chromosomes of V. cholerae maintained their known order of replication timing to achieve termination synchrony. Using flow cytometry combined with mathematical modeling, we established that a phase of cellular regrowth be the reason for the observed restart of DNA replication after the initial shutdown. Our study shows that although the stringent response induction itself is widely conserved, bacteria developed different ways of how to react to the sensed nutrient limitation, potentially reflecting their individual lifestyle requirements.

SEDE-GPS: socio-economic data enrichment based on GPS information.

BACKGROUND: Microbes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available. RESULTS: In this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity. CONCLUSION: The results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.de.

Establishing a System for Testing Replication Inhibition of the Vibrio cholerae Secondary Chromosome in Escherichia coli.

Regulators of DNA replication in bacteria are an attractive target for new antibiotics, as not only is replication essential for cell viability, but its underlying mechanisms also differ from those operating in eukaryotes. The genetic information of most bacteria is encoded on a single chromosome, but about 10% of species carry a split genome spanning multiple chromosomes. The best studied bacterium in this context is the human pathogen Vibrio cholerae, with a primary chromosome (Chr1) of 3 M bps, and a secondary one (Chr2) of about 1 M bps. Replication of Chr2 is under control of a unique mechanism, presenting a potential target in the development of V. cholerae-specific antibiotics. A common challenge in such endeavors is whether the effects of candidate chemicals can be focused on specific mechanisms, such as DNA replication. To test the specificity of antimicrobial substances independent of other features of the V. cholerae cell for the replication mechanism of the V. cholerae secondary chromosome, we establish the replication machinery in the heterologous E. coli system. We characterize an E. coli strain in which chromosomal replication is driven by the replication origin of V. cholerae Chr2. Surprisingly, the E. coli ori2 strain was not inhibited by vibrepin, previously found to inhibit ori2-based replication.

Design and Assembly of DNA Sequence Libraries for Chromosomal Insertion in Bacteria Based on a Set of Modified MoClo Vectors.

Efficient assembly of large DNA constructs is a key technology in synthetic biology. One of the most popular assembly systems is the MoClo standard in which restriction and ligation of multiple fragments occurs in a one-pot reaction. The system is based on a smart vector design and type IIs restriction enzymes, which cut outside their recognition site. While the initial MoClo vectors had been developed for the assembly of multiple transcription units of plants, some derivatives of the vectors have been developed over the last years. Here we present a new set of MoClo vectors for the assembly of fragment libraries and insertion of constructs into bacterial chromosomes. The vectors are accompanied by a computer program that generates a degenerate synthetic DNA sequence that excludes "forbidden" DNA motifs. We demonstrate the usability of the new approach by construction of a stable fluorescence repressor operator system (FROS).