Genome-resolved metagenomics reveals the effect of nutrient availability on bacterial genomic properties across 44 European freshwater lakes.

Understanding intricate microbial interactions in the environment is crucial. This is especially true for the relationships between nutrients and bacteria, as phosphorus, nitrogen and organic carbon availability are known to influence bacterial population dynamics. It has been suggested that low nutrient conditions prompt the evolutionary process of genome streamlining. This process helps conserve scarce nutrients and allows for proliferation. Genome streamlining is associated with genomic properties such as %GC content, genes encoding sigma factors, percent coding regions, gene redundancy, and functional shifts in processes like cell motility and ATP binding cassette transporters, among others. The current study aims to unveil the impact of nutrition on the genome size, %GC content, and functional properties of pelagic freshwater bacteria. We do this at finer taxonomic resolutions for many metagenomically characterized communities. Our study confirms the interplay of trophic level and genomic properties. It also highlights that different nutrient types, particularly phosphorus and nitrogen, impact these properties differently. We observed a covariation of functional traits with genome size. Larger genomes exhibit enriched pathways for motility, environmental interaction, and regulatory genes. ABC transporter genes reflect the availability of nutrients in the environment, with small genomes presumably relying more on metabolites from other organisms. We also discuss the distinct strategies different phyla adopt to adapt to oligotrophic environments. The findings contribute to our understanding of genomic adaptations within complex microbial communities.

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.

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

BACKGROUND: 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 efficient, 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 efficient workflow management system. RESULTS: 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 ) or as a Docker container on DockerHub ( https://hub.docker.com/r/mw55/natrix ). CONCLUSION: Natrix is a user-friendly and highly extensible workflow for processing Illumina amplicon data.

Factors shaping community patterns of protists and bacteria on a European scale.

Factors shaping community patterns of microorganisms are controversially discussed. Physical and chemical factors certainly limit the survival of individual taxa and maintenance of diversity. In recent years, a contribution of geographic distance and dispersal barriers to distribution patterns of protists and bacteria has been demonstrated. Organismic interactions such as competition, predation and mutualism further modify community structure and maintenance of distinct taxa. Here, we address the relative importance of these different factors in shaping protists and bacterial communities on a European scale using high-throughput sequencing data obtained from lentic freshwater ecosystems. We show that community patterns of protists are similar to those of bacteria. Our results indicate that cross-domain organismic factors are important variables with a higher influence on protists as compared with bacteria. Abiotic physical and chemical factors also contributed significantly to community patterns. The contribution of these latter factors was higher for bacteria, which may reflect a stronger biogeochemical coupling. The contribution of geographical distance was similar for both microbial groups.

TaxMapper: an analysis tool, reference database and workflow for metatranscriptome analysis of eukaryotic microorganisms.

BACKGROUND: High-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. RESULTS: From 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 each of the following: sequences of taxa in the database, sequences of taxa related to those in the database, and random 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 apply it for metatranscriptome analysis of any environmental sample. CONCLUSIONS: TaxMapper 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 .

Human TLR8 senses UR/URR motifs in bacterial and mitochondrial RNA.

Toll-like receptor (TLR) 13 and TLR2 are the major sensors of Gram-positive bacteria in mice. TLR13 recognizes Sa19, a specific 23S ribosomal (r) RNA-derived fragment and bacterial modification of Sa19 ablates binding to TLR13, and to antibiotics such as erythromycin. Similarly, RNase A-treated Staphylococcus aureus activate human peripheral blood mononuclear cells (PBMCs) only via TLR2, implying single-stranded (ss) RNA as major stimulant. Here, we identify human TLR8 as functional TLR13 equivalent that promiscuously senses ssRNA. Accordingly, Sa19 and mitochondrial (mt) 16S rRNA sequence-derived oligoribonucleotides (ORNs) stimulate PBMCs in a MyD88-dependent manner. These ORNs, as well as S. aureus-, Escherichia coli-, and mt-RNA, also activate differentiated human monocytoid THP-1 cells, provided they express TLR8. Moreover, Unc93b1(-/-)- and Tlr8(-/-)-THP-1 cells are refractory, while endogenous and ectopically expressed TLR8 confers responsiveness in a UR/URR RNA ligand consensus motif-dependent manner. If TLR8 function is inhibited by suppression of lysosomal function, antibiotic treatment efficiently blocks bacteria-driven inflammatory responses in infected human whole blood cultures. Sepsis therapy might thus benefit from interfering with TLR8 function.

xHeinz: an algorithm for mining cross-species network modules under a flexible conservation model.

MOTIVATION: Integrative network analysis methods provide robust interpretations of differential high-throughput molecular profile measurements. They are often used in a biomedical context-to generate novel hypotheses about the underlying cellular processes or to derive biomarkers for classification and subtyping. The underlying molecular profiles are frequently measured and validated on animal or cellular models. Therefore the results are not immediately transferable to human. In particular, this is also the case in a study of the recently discovered interleukin-17 producing helper T cells (Th17), which are fundamental for anti-microbial immunity but also known to contribute to autoimmune diseases. RESULTS: We propose a mathematical model for finding active subnetwork modules that are conserved between two species. These are sets of genes, one for each species, which (i) induce a connected subnetwork in a species-specific interaction network, (ii) show overall differential behavior and (iii) contain a large number of orthologous genes. We propose a flexible notion of conservation, which turns out to be crucial for the quality of the resulting modules in terms of biological interpretability. We propose an algorithm that finds provably optimal or near-optimal conserved active modules in our model. We apply our algorithm to understand the mechanisms underlying Th17 T cell differentiation in both mouse and human. As a main biological result, we find that the key regulation of Th17 differentiation is conserved between human and mouse. AVAILABILITY AND IMPLEMENTATION: xHeinz, an implementation of our algorithm, as well as all input data and results, are available at http://software.cwi.nl/xheinz and as a Galaxy service at http://services.cbib.u-bordeaux2.fr/galaxy in CBiB Tools. CONTACT: gunnar.klau@cwi.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Trade-off between taxon diversity and functional diversity in European lake ecosystems.

Inferring ecosystem functioning and ecosystem services through inspections of the species inventory is a major aspect of ecological field studies. Ecosystem functions are often stable despite considerable species turnover. Using metatranscriptome analyses, we analyse a thus-far unparalleled freshwater data set which comprises 21 mainland European freshwater lakes from the Sierra Nevada (Spain) to the Carpathian Mountains (Romania) and from northern Germany to the Apennines (Italy) and covers an altitudinal range from 38 m above sea level (a.s.l) to 3110 m a.s.l. The dominant taxa were Chlorophyta and streptophytic algae, Ciliophora, Bacillariophyta and Chrysophyta. Metatranscriptomics provided insights into differences in community composition and into functional diversity via the relative share of taxa to the overall read abundance of distinct functional genes on the ecosystem level. The dominant metabolic pathways in terms of the fraction of expressed sequences in the cDNA libraries were affiliated with primary metabolism, specifically oxidative phosphorylation, photosynthesis and the TCA cycle. Our analyses indicate that community composition is a good first proxy for the analysis of ecosystem functions. However, differential gene regulation modifies the relative importance of taxa in distinct pathways. Whereas taxon composition varies considerably between lakes, the relative importance of distinct metabolic pathways is much more stable, indicating that ecosystem functioning is buffered against shifts in community composition through a functional redundancy of taxa.

Assessing the response of an urban stream ecosystem to salinization under different flow regimes.

Urban streams are exposed to a variety of anthropogenic stressors. Freshwater salinization is a key stressor in these ecosystems that is predicted to be further exacerbated by climate change, which causes simultaneous changes in flow parameters, potentially resulting in non-additive effects on aquatic ecosystems. However, the effects of salinization and flow velocity on urban streams are still poorly understood as multiple-stressor experiments are often conducted at pristine rather than urban sites. Therefore, we conducted a mesocosm experiment at the Boye River, a recently restored stream located in a highly urbanized area in Western Germany, and applied recurrent pulses of salinity along a gradient (NaCl, 9 h daily of +0 to +2.5 mS/cm) in combination with normal and reduced current velocities (20 cm/s vs. 10 cm/s). Using a comprehensive assessment across multiple organism groups (macroinvertebrates, eukaryotic algae, fungi, parasites) and ecosystem functions (primary production, organic-matter decomposition), we show that flow velocity reduction has a pervasive impact, causing community shifts for almost all assessed organism groups (except fungi) and inhibiting organic-matter decomposition. Salinization affected only dynamic components of community assembly by enhancing invertebrate emigration via drift and reducing fungal reproduction. We caution that the comparatively small impact of salt in our study can be due to legacy effects from past salt pollution by coal mining activities >30 years ago. Nevertheless, our results suggest that urban stream management should prioritize the continuity of a minimum discharge to maintain ecosystem integrity. Our study exemplifies a holistic approach for the assessment of multiple-stressor impacts on streams, which is needed to inform the establishment of a salinity threshold above which mitigation actions must be taken.

Robustness and accuracy of functional modules in integrated network analysis.

MOTIVATION: High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions. RESULTS: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia.

Exploring the efficacy of metabarcoding and non-target screening for detecting treated wastewater.

Wastewater treatment processes can eliminate many pollutants, yet remainder pollutants contain organic compounds and microorganisms released into ecosystems. These remainder pollutants have the potential to adversely impact downstream ecosystem processes, but their presence is currently not being monitored. This study was set out with the aim of investigating the effectiveness and sensitivity of non-target screening of chemical compounds, 18S V9 rRNA gene, and full-length 16S rRNA gene metabarcoding techniques for detecting treated wastewater in receiving waters. We aimed at assessing the impact of introducing 33 % treated wastewater into a triplicated large-scale mesocosm setup during a 10-day exposure period. Discharge of treated wastewater significantly altered the chemical signature as well as the microeukaryotic and prokaryotic diversity of the mesocosms. Non-target screening, 18S V9 rRNA gene, and full-length 16S rRNA gene metabarcoding detected these changes with significant covariation of the detected pattern between methods. The 18S V9 rRNA gene metabarcoding exhibited superior sensitivity immediately following the introduction of treated wastewater and remained one of the top-performing methods throughout the study. Full-length 16S rRNA gene metabarcoding demonstrated sensitivity only in the initial hour, but became insignificant thereafter. The non-target screening approach was effective throughout the experiment and in contrast to the metabarcoding methods the signal to noise ratio remained similar during the experiment resulting in an increasing relative strength of this method. Based on our findings, we conclude that all methods employed for monitoring environmental disturbances from various sources are suitable. The distinguishing factor of these methods is their ability to detect unknown pollutants and organisms, which sets them apart from previously utilized approaches and allows for a more comprehensive perspective. Given their diverse strengths, particularly in terms of temporal resolution, these methods are best suited as complementary approaches.

Chemoproteomics Reveals the Pan-HER Kinase Inhibitor Neratinib To Target an Arabidopsis Epoxide Hydrolase Related to Phytohormone Signaling.

Plant phytohormone pathways are regulated by an intricate network of signaling components and modulators, many of which still remain unknown. Here, we report a forward chemical genetics approach for the identification of functional SA agonists in Arabidopsis thaliana that revealed Neratinib (Ner), a covalent pan-HER kinase inhibitor drug in humans, as a modulator of SA signaling. Instead of a protein kinase, chemoproteomics unveiled that Ner covalently modifies a surface-exposed cysteine residue of Arabidopsis epoxide hydrolase isoform 7 (AtEH7), thereby triggering its allosteric inhibition. Physiologically, the Ner application induces jasmonate metabolism in an AtEH7-dependent manner as an early response. In addition, it modulates PATHOGENESIS RELATED 1 (PR1) expression as a hallmark of SA signaling activation as a later effect. AtEH7, however, is not the exclusive target for this physiological readout induced by Ner. Although the underlying molecular mechanisms of AtEH7-dependent modulation of jasmonate signaling and Ner-induced PR1-dependent activation of SA signaling and thus defense response regulation remain unknown, our present work illustrates the powerful combination of forward chemical genetics and chemical proteomics for identifying novel phytohormone signaling modulatory factors. It also suggests that marginally explored metabolic enzymes such as epoxide hydrolases may have further physiological roles in modulating signaling.

The Asymmetric Response Concept explains ecological consequences of multiple stressor exposure and release.

Our capacity to predict trajectories of ecosystem degradation and recovery is limited, especially when impairments are caused by multiple stressors. Recovery may be fast or slow and either complete or partial, sometimes result in novel ecosystem states or even fail completely. Here, we introduce the Asymmetric Response Concept (ARC) that provides a basis for exploring and predicting the pace and magnitude of ecological responses to, and release from, multiple stressors. The ARC holds that three key mechanisms govern population, community and ecosystem trajectories. Stress tolerance is the main mechanism determining responses to increasing stressor intensity, whereas dispersal and biotic interactions predominantly govern responses to the release from stressors. The shifting importance of these mechanisms creates asymmetries between the ecological trajectories that follow increasing and decreasing stressor intensities. This recognition helps to understand multiple stressor impacts and to predict which measures will restore communities that are resistant to restoration.

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.

BioNet: an R-Package for the functional analysis of biological networks.

MOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. RESULTS: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. AVAILABILITY: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.

Nutrient-driven genome evolution revealed by comparative genomics of chrysomonad flagellates.

Phototrophic eukaryotes have evolved mainly by the primary or secondary uptake of photosynthetic organisms. A return to heterotrophy occurred multiple times in various protistan groups such as Chrysophyceae, despite the expected advantage of autotrophy. It is assumed that the evolutionary shift to mixotrophy and further to heterotrophy is triggered by a differential importance of nutrient and carbon limitation. We sequenced the genomes of 16 chrysophyte strains and compared them in terms of size, function, and sequence characteristics in relation to photo-, mixo- and heterotrophic nutrition. All strains were sequenced with Illumina and partly with PacBio. Heterotrophic taxa have reduced genomes and a higher GC content of up to 59% as compared to phototrophic taxa. Heterotrophs have a large pan genome, but a small core genome, indicating a differential specialization of the distinct lineages. The pan genome of mixotrophs and heterotrophs taken together but not the pan genome of the mixotrophs alone covers the complete functionality of the phototrophic strains indicating a random reduction of genes. The observed ploidy ranges from di- to tetraploidy and was found to be independent of taxonomy or trophic mode. Our results substantiate an evolution driven by nutrient and carbon limitation.

A multi-omics study on quantifying antimicrobial resistance in European freshwater lakes.

The surveillance of wastewater for the Covid-19 virus during this unprecedented pandemic and mapped to the distribution and magnitude of the infected in the population near real-time exemplifies the importance of tracking rapidly changing trends of pathogens or public health problems at a large scale. The rising trends of antimicrobial resistance (AMR) with multidrug-resistant pathogens from the environmental water have similarly gained much attention in recent years. Wastewater-based epidemiology from water samples has shown that a wide range of AMR-related genes is frequently detected. Albeit sewage is treated before release and thus, the abundance of pathogens should be significantly reduced or even pathogen-free, several studies indicated the contrary. Pathogens are still measurable in the released water, ultimately entering freshwaters, such as rivers and lakes. Furthermore, socio-economic and environmental factors, such as chemical industries and animal farming nearby, impact the presence of AMR. Many bacterial species from the environment are intrinsically resistant and also contribute to the resistome of freshwater lakes. This study collected the most extensive standardized freshwater data set from hundreds of European lakes and conducted a comprehensive multi-omics analysis on antimicrobial resistance from these freshwater lakes. Our research shows that genes encoding for AMR against tetracyclines, cephalosporins, and quinolones were commonly identified, while for some, such as sulfonamides, resistance was less frequently present. We provide an estimation of the characteristic resistance of AMR in European lakes, which can be used as a comprehensive resistome dataset to facilitate and monitor temporal changes in the development of AMR in European freshwater lakes.

Molecular Data Reveal a Cryptic Diversity in the Genus Urotricha (Alveolata, Ciliophora, Prostomatida), a Key Player in Freshwater Lakes, With Remarks on Morphology, Food Preferences, and Distribution.

Species of the ciliate genus Urotricha are key players in freshwater plankton communities. In the pelagial of lakes, about 20 urotrich species occur throughout an annual cycle, some of which play a pivotal role in aquatic food webs. For example, during the phytoplankton spring bloom, they consume a remarkable proportion of the algal production. In ecological studies, urotrich ciliates are usually merely identified to genus rank and grouped into size classes. This is unsatisfying considering the distinct autecological properties of individual species and their specific spatial and temporal distribution patterns. As a basis for future research, we characterized in detail four common urotrich morphotypes, i.e., specimens identified as U. furcata and tentatively as U. agilis, U. pseudofurcata, and U. castalia, using state-of-the-art methods. We used an integrative polyphasic approach, in which morphological studies (in vivo observation, silver staining methods, scanning electron microscopy) were linked with a molecular approach exploiting four different gene fragments as taxonomic DNA barcodes with different resolution potential (SSU rDNA, ITS-1, ITS-2, hypervariable V4 and V9 regions of the SSU rDNA). We shed light on the diversity of urotrich ciliates as well as on their global distribution patterns, and annual cycles. Additionally, we coupled individual species occurrences and environmental parameters, and subsequently modeled the distribution and occurrence, using logistic regressions. Furthermore, for one strain putatively identified as U. castalia, we ascertained the optimal cultivation media and food preferences. Thereby, our comprehensive view on these important freshwater ciliates that frequently occur in environmental high throughput sequencing datasets worldwide will allow future studies to better exploit protistan plankton data from lakes.

Ecological Differentiation in Two Major Freshwater Bacterial Taxa Along Environmental Gradients.

Polynucleobacter (Burkholderiaceae, Betaproteobacteria) and Limnohabitans (Comamonadaceae, Betaproteobacteria) are abundant freshwater bacteria comprising large genetic and taxonomic diversities, with species adapted to physico-chemically distinct types of freshwater systems. The relative importance of environmental drivers, i.e., physico-chemistry, presence of microeukaryotes and geographic position for the diversity and prevalence has not been investigated for both taxa before. Here, we present the first pan-European study on this topic, comprising 255 freshwater lakes. We investigated Limnohabitans and Polynucleobacter using an amplicon sequencing approach of partial 16S rRNA genes along environmental gradients. We show that physico-chemical factors had the greatest impact on both genera. Analyses on environmental gradients revealed an exceptionally broad ecological spectrum of operational taxonomic units (OTUs). Despite the coarse resolution of the genetic marker, we found OTUs with contrasting environmental preferences within Polynucleobacter and Limnohabitans subclusters. Such an ecological differentiation has been characterized for PnecC and LimC before but was so far unknown for less well studied subclusters such as PnecA and PnecB. Richness and abundance of OTUs are geographically clustered, suggesting that geographic diversity patterns are attributable to region-specific physico-chemical characteristics (e.g., pH and temperature) rather than latitudinal gradients or lake sizes.

Propionate supplementation promotes the expansion of peripheral regulatory T-Cells in patients with end-stage renal disease.

Patients with end-stage renal disease (ESRD) suffer from a progressively increasing low-grade systemic inflammation, which is associated with higher morbidity and mortality. Regulatory T cells (Tregs) play an important role in regulation of the inflammatory process. Previously, it has been demonstrated that short-chain fatty acids reduce inflammation in the central nervous system in a murine model of multiple sclerosis through an increase in tissue infiltrating Tregs. Here, we evaluated the effect of the short-chain fatty acid propionate on the chronic inflammatory state and T-cell composition in ESRD patients. Analyzing ESRD patients and healthy blood donors before, during, and 60 days after the propionate supplementation by multiparametric flow cytometry we observed a gradual and significant expansion in the frequencies of CD25highCD127- Tregs in both groups. Phenotypic characterization suggests that polarization of naïve T cells towards Tregs is responsible for the observed expansion. In line with this, we observed a significant reduction of inflammatory marker CRP under propionate supplementation. Of interest, the observed anti-inflammatory surroundings did not affect the protective pathogen-specific immunity as demonstrated by the stable frequencies of effector/memory T cells specific for tetanus/diphtheria recall antigens. Collectively, our data suggest that dietary supplements with propionate have a beneficial effect on the elevated systemic inflammation of ESRD patients. The effect can be achieved through an expansion of circulating Tregs without affecting the protective pathogen-reactive immunity.