Biology subjects

Beisser, D.

Publications and source records attributed to Beisser, D..

4 recordsLinked to original sources

bioRxiv2024

Complex compositional and cellular response of river sediment microbiomes to multiple anthropogenic stressors

Rivers face constant anthropogenic stressors, resulting in significant changes in microbial community composition. What remains unclear is whether these changes render the microbiome better adapted to the stressed environment. Here, by subjecting 64 river-connected mesocosms to multiple stressors, we show that sediment microbiomes of small lowland rivers are highly sensitive to lowered flow velocity resulting in substantially altered community compositions not fully capable of compensating for the stressor effect within two weeks albeit having stable functions encoded in metagenomes. Transcriptomics revealed a systematic heat shock response in the community and a highly active, previously unknown anaerobic key stone species with great metabolic versatility. Increases in temperature (+3.5 {degrees}C) or salinity (+0.5 mS/cm) were outcompeted by lowered flow and elicited only minor responses at community or transcriptomic level with, e.g., upregulation of the photosystem of chloroplasts. Following a two-week recovery period, transcriptomic stress responses vanished completely compared to control mesocosms, exemplifying the river microbiomes resilience. We conclude that, given the complex community responses at both the cellular and compositional level, maintaining natural river flow is vital to preventing energy loss and reduced microbiome activity in river sediments.

ecology↗

bioRxiv2021

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

Microbes such as bacteria, archaea, and protists are essential for element cycling and ecosystem functioning, but many questions central to the understanding of the role of microbes in ecology are still open. Here, we analyze the relationship between lake microbiomes and the land cover surrounding the lakes. 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. We identify microbial bioindicators for these land cover categories. Combining land cover and physico-chemical bioindicators identified from the same amplicon sequencing dataset, we develop two novel similarity metrics that facilitate insights into the ecology of the lake microbiome. We show that the bioindicator network, i.e., the graph linking OTUs indicative of the same environmental parameters, corresponds to microbial co-occurrence patterns. Taken together, we demonstrate the strength of machine learning approaches to identify correlations between microbial diversity and environmental factors, potentially opening new approaches to integrate environmental molecular diversity into monitoring and water quality assessments.

ecology↗

bioRxiv2020

Natrix: A Snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads

Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires effcient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an effcient workflow management system. We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix).

bioinformatics↗

bioRxiv2017

TaxMapper: An Analysis Tool, Reference Database and Workflow for Metatranscriptome Analysis of Eukaryotic Microorganisms

BackgroundHigh-throughput sequencing (HTS) technologies are increasingly applied to analyse complex microbial ecosystems by mRNA sequencing of whole communities, also known as metatranscriptome sequencing. This approach is at the moment largely limited to prokaryotic communities and communities of few eukaryotic species with sequenced genomes. For eukaryotes the analysis is hindered mainly by a low and fragmented coverage of the reference databases to infer the community composition, but also by lack of automated workflows for the task.\n\nResultsFrom the databases of the National Center for Biotechnology Information and Marine Microbial Eukaryote Transcriptome Sequencing Project, 142 references were selected in such a way that the taxa represent the main lineages within each of the seven supergroups of eukaryotes and possess predominantly complete transcriptomes or genomes. From these references, we created an annotated microeukaryotic reference database. We developed a tool called TaxMapper for a reliably mapping of sequencing reads against this database and filtering of unreliable assignments. For filtering, a classifier was trained and tested on sequences in the database, sequences of related taxa to those in the database and randomly generated sequences. Additionally, TaxMapper is part of a metatranscriptomic Snakemake workflow developed to perform quality assessment, functional and taxonomic annotation and (multivariate) statistical analysis including environmental data. The workflow is provided and described in detail to empower researchers to easily apply it for metatranscriptome analysis of any environmental sample.\n\nConclusionsTaxMapper shows superior performance compared to standard approaches, resulting in a higher number of true positive taxonomic assignments. Both the TaxMapper tool and the workflow are available as open-source code at Bitbucket under the MIT license: https://bitbucket.org/dbeisser/taxmapper and as a Bioconda package: https://bioconda.github.io/recipes/taxmapper/README.html.

bioinformatics↗