Biology subjects

Labrenz, M.

Publications and source records attributed to Labrenz, M..

10 recordsLinked to original sources

bioRxiv2026

Semi-Annual Cycles in the Biotic Communities of Temperate Aquatic Habitats

Recurring patterns in biosphere dynamics are anchored in daily and seasonal oscillations in abiotic variables driven by Earths obliquity, rotation, and orbit. While circadian and annual biotic cycles are well studied, persistent supra- or subannual cycles in biotic systems are rarely documented globally. Here, we apply a machine learning approach to DNA metabarcoding time series and detect a biotic semi-annual cycle expressed across aquatic communities in temperate regions across taxonomic domains. We propose that this dynamic reflects a semi-annual mode in insolation and is suppressed under conditions of limited nutrients or sunlight. Our results suggest photoautotrophs are central for the aetiology of the biotic SAM, while demonstrating that it is a community-level phenomena not attributable to single species. The regularity of the biotic SAM suggests value for anticipating less predictable ecological events, including phytoplankton blooms. Overall, our results highlight Earth system-scale forcing of local dynamics and reinforce coupling patterns.

ecology↗

bioRxiv2026

Enhancing the Understanding of Environmental Microbiomes through Topic Modeling: A Quantitative and Qualitative Analysis

BackgroundUnderstanding ecosystem dynamics is essential for assessing ecosystem health, yet remains challenging due to complex biotic and abiotic interactions. Microbial communities are valuable indicators of environmental change, but the high dimensionality of microbiome data requires advanced analytical methods. This study explores the use of topic modeling (TM), an unsupervised machine learning approach initially designed for text analysis, to analyze microbiome data from the dynamic Warnow Estuary on the southern Baltic Sea coast. ResultsWe applied TM to estuarine microbiome data and compared its performance to traditional dimensionality reduction methods, Principal Component Analysis (PCA) and Principal Coordinate Analysis (PCoA). Quantitative results indicate that TM performs comparably to conventional approaches in preserving ecological and functional information, and in certain aspects even superior. In addition, we show qualitatively that NNMF, a TM method, captures latent patterns in the data providing an interpretable perspective on the microbiome. In this exploratory framework, NNMF suggested five distinct sub-communities within the estuary that appear to follow a seasonal succession influenced by freshwater inflow. These sub-communities were associated with specific ranges of salinity and temperature and showed distinct taxonomic profiles, with shared characteristics across the estuarine system. ConclusionsOur findings suggest that TM is a useful tool for exploring complex environmental microbiome datasets, offering a complementary perspective that can provide additional ecological insights. TMs ability to highlight coherent microbial community patterns indicates its promise for supporting environmental monitoring and informing targeted ecosystem management in dynamic habitats, though further studies are needed to fully assess its applicability.

ecology↗

bioRxiv2025

Bacterial community adaptation after freshwater and seawater coalescence

Microbial community coalescence, the merging of entire microbial communities, is common across ecosystems, particularly in estuaries where freshwater and seawater mix. The complexity of these habitats makes the in situ study of community dynamics after coalescence challenging, highlighting the need for controlled experiments to unravel the factors influencing the coalescence in the estuary. To study these processes, we combined natural freshwater and seawater bacterial communities at five different mixing ratios and incubated them in parallel microcosms containing freshwater or seawater incubation media. Forty mixed communities were tracked over six passages using Nanopore full-length 16S rRNA gene sequencing. In the original field samples, freshwater hosted more diverse communities than seawater. While the communities were structurally distinct, shared bacterial families accounted for approximately 95% of total reads. Many low-abundance taxa were lost upon laboratory incubation, while potentially faster-growing ones were enriched. We found that the coalescence outcome was strongly shaped by the incubation media, whereas the mixing ratio had a minor influence. Mixed communities converged toward the source community native to the incubation media, with increasing similarity at a higher source community proportion. In freshwater, a 25% inoculum of the freshwater community was sufficient to re-establish a near-native freshwater community, whereas in seawater, similarity to the native seawater community depended on the inoculation ratio. Network analysis showed a tightly connected module of seawater families, reflecting their shared habitat preference or cooperation, whereas freshwater families were more loosely connected. We also observed that most families were unaffected by mixing ratios or temporal dynamics, with only a few showing mixing ratio dependence. For instance, the freshwater family Comamonadaceae and seawater families Marinomonadaceae and Pseudoalteromonadaceae were dominant in their respective native environments, and increased in mixed communities in proportion to their initial source proportion. Overall, environmental filtering had a stronger impact than the mixing ratio of source communities on coalesced communities, with habitat-specific taxa further modulating the outcome. These findings advanced our understanding of microbial responses to coalescence and provided insights into microbial community assembly in dynamic estuarine systems. HighlightsO_LIEnvironmental filtering outweighs the microbial source community ratio in shaping coalescence outcomes. C_LIO_LIAsymmetric resilience: freshwater communities require a lower inoculum to re-establish than seawater communities. C_LIO_LIModularity of the seawater source community was observed during coalescence. C_LIO_LIFull-length 16S rRNA gene profiling with a custom dual-barcoding Nanopore protocol enables cost-effective bacterial community tracking. C_LIO_LIControlled coalescence experiments offer mechanistic insights into estuarine microbial community transitions. C_LI

ecology↗

bioRxiv2025

Predicting Antifouling Paint Particle Contamination based on 16S rRNA Gene Sequencing Data using Random Forest-Based Machine Learning

Antifouling paints often contain biocides designed to inhibit biological growth, and antifouling paint particles (APPs) have been previously shown to affect microbial communities in sediment. Given typical methods for monitoring for APP presence can be specialised and challenging, alternative methods using simple, standardised and universal approaches, such as 16S rRNA amplicon sequencing, would be highly valuable. This study uses a field-based mesocosm approach to train a random forest-based (supervised) machine learning model to predict APPs presence and concentration in sediment based on 16S microbial community data. The model correctly predicted 100% APP-presence samples and 83.3% APP-absence samples in the incubation testset, although the model could not correctly predict APP concentration with sufficient accuracy. To determine real-world applicability of the model, samples from 14 different sites along the Baltic Sea coastline and Warnow estuary in NE Germany were collected and APP-presence was pre-determined using SEM-EDX spectroscopy. The model correctly assigned APP-absence status to all APP-absent sites, and correctly assigned 3 of 5 APP-contaminated sites as having APP presence. As such, these results confirm it is possible to predict APPs in sediment based on the microbial community, and serves as a proof-of-concept for the further development of machine learning-based predictive tools for environmental monitoring.

microbiology↗

bioRxiv2025

Phylogeny-aware comparative genomics of Vibrio vulnificus links genetic traits to pathogenicity

Vibrio vulnificus is a natural inhabitant of coastal brackish waters worldwide and an opportunistic pathogen that can cause severe infections and septicemia through seafood consumption or wound exposure. Due to global warming, its abundance is increasing at high latitudes. While the species harbors diverse virulence factors, its precise disease mechanisms remain unclear. Comparative genomics between clinical and environmental isolates can help identify key virulence genes, but the limited availability of genomes from environmental isolates has hindered progress. In this study, we sequenced the genomes of 82 V. vulnificus isolates from water, sediment, and seagrass along the Baltic Sea coast and complemented with published genomes from 208 clinical and 117 globally distributed isolates for comparative analysis. Phylogenetic reconstruction confirmed four major lineages, with Baltic Sea strains confined to lineage L2 and L4, while clinical and environmental strains were distributed across all lineages. This suggests that the phylogenetic structure of V. vulnificus reflects adaptation to environmental conditions rather than pathogenicity. Using the PhyloBOTL pipeline developed here, we identified 128 orthologs significantly enriched in clinical isolates, grouped into 36 co-localization clusters based on proximity in the genomes. These included genes linked to virulence, such as those for capsular polysaccharide synthesis and biofilm formation, as well as previously unrecognized candidates, including chaperone-usher pilus biosynthesis, spermidine synthesis, Type VI secretion effectors, and an RTX toxin-like protein. Several of the clinically enriched gene clusters have been independently lost in three V. vulnificus clades, suggesting convergent evolution and a distinct ecological niche shared by these claded. Finally, we used the clinically enriched genes to design PCR primers for detecting and monitoring pathogenic V. vulnificus strains, providing a valuable tool for surveillance and public health efforts.

microbiology↗

bioRxiv2024

The ecology of potentially pathogenic Vibrio spp. in a seagrass meadow ecosystem

Seagrass meadow ecosystems offer several valuable ecosystem services in coastal regions around the world. Recent studies have suggested that one such important service is reduction of pathogenic bacteria and specifically Vibrio spp. in adjacent waters. The specific mechanisms of pathogen reduction remain unclear, although increased sedimentation has been suggested as one likely process for pathogens to be quenched from the water column. Whether Vibrio spp. persist in the sediment or in other compartments of the seagrass meadow is currently not known, but it has been shown that marine surface biofilms can function as reservoirs of pathogenic vibrios. This general feature may also apply to seagrass and sediment surfaces. In this study, we investigated the relative abundance and community ecology of Vibrio spp. bacteria in Baltic Sea seagrass meadows using both culturing and culture-independent methods. While we did not detect a significant reduction of Vibrio spp., the highest relative abundances of Vibrio spp. were observed in the water column above unvegetated sites as compared to seagrass meadows. We also detected high relative abundances of Vibrio spp. on seagrass roots, supporting previous observations that marine surfaces are selectively colonized by Vibrio spp., implying that these habitats are important for the persistence and possibly release of Vibrio spp. into the water column. Our results emphasize the need to understand the interactions of pathogenic bacteria with coastal habitats, including interactions with host organisms such as seagrasses that provide biofilm microenvironments, in order to understand how diseases associated with these organisms develop.

ecology↗

bioRxiv2024

Effects of excess phosphate on a coastal plankton community

Eutrophication in the Baltic Sea has caused an imbalance in the inorganic nitrogen (N) to phosphorus (P) ratio, leaving excess phosphate (PO4) after the phytoplankton spring bloom that terminates after N-depletion. Using monitoring data, we demonstrated that the PO4 concentration has continued to increase in the outermost Gulf of Finland during past decades. We further investigated the fate of such excess PO4 in a two-week mesocosm (1.2 m3) experiment. The starting concentration of PO4 was 0.66 {micro}M, and treatments included a non-treated control (control), nitrate addition (N-add; 3.6 {micro}M), glucose addition (C-add; 25 {micro}M) and combined nitrate and glucose addition (N+C-add). The addition of N both in N-add and N+C-add treatments stimulated nano- and microphytoplankton, while the picophytoplankton abundance increased only after N-depletion. Also, the copepod biomass was positively affected by the N-addition. N2-fixing cyanobacteria were present but in low abundance. Carbon addition did not enhance heterotrophic bacterial uptake of PO4 contrary to our expectations, nor did it affect the phyto- or zooplankton community composition. The PO4 concentration was reduced to [~]0.4 {micro}M in the control and C-add treatments and to 0.16 {micro}M in the two N-amended treatments, with an inorganic N:P uptake ratio of 6.7. These results underscore the role of picophytoplankton in reducing the excess PO4 pool after the spring bloom, a function traditionally ascribed to bloom-forming diazotrophic cyanobacteria in the Baltic Sea.

microbiology↗

bioRxiv2024

Microbial remineralization processes during post-spring-bloom excess phosphate in the northern Baltic Sea

In the northern Baltic, post-spring-bloom low dissolved inorganic nitrogen to phosphorus conditions, degradation of N-rich organic matter potentially supports the drawdown of excess phosphate. During a 17-day-long mesocosm experiment in the south-west Finnish archipelago, we examined nitrogen, phosphorus and carbon acquiring extracellular enzyme activities in three size fractions (<0.2 {micro}m, 0.2-3 {micro}m, and >3 {micro}m), bacterial abundance, production, community composition and its predicted metabolic functions. The mesocosms received different carbon and nitrogen amendments to test for the effect of inorganic nutrient stoichiometry on enzymatic degradation processes that ultimately determine the export potential of organic matter. Alkaline phosphatase activity occurred mainly in the dissolved form and likely contributed to the excess phosphate conditions. In the beginning of the experiment, peptidolytic and glycolytic enzymes were predicted to be produced by free-living bacteria identified within the classes Actinobacteria and Alphaproteobacteria, whereas the contribution of picocyanobacteria increased towards the end. Our results imply that heterotrophic bacteria lost the competition to picocyanobacteria due to the lack of suitable energy sources. The high hydrolytic rates in fractions <0.2 {micro}m and 0.2-3 {micro}m, found in this study, could potentially retain inorganic nutrients in the surface layer and suppress downward fluxes of organic matter and hence carbon sequestration.

microbiology↗

bioRxiv2019

The novel shapeshifting bacterial phylum Saltatorellota

Our current understanding of a free-living bacterium - capable of withstanding a variety of environmental stresses-is represented by the image of a peptidoglycan-armored rigid casket. The making and breaking of peptidoglycan greatly determines cell shape. The cytoplasmic membrane follows this shape, pressed towards the cell wall by turgor pressure. Consequently, bacteria are morphologically static organisms, in contrast to eukaryotic cells that can facilitate shape changes. Here we report the discovery of the novel bacterial phylum Saltatorellota, that challenges this concept of a bacterial cell. Members of this phylum can change their shape, are capable of amoeba-like locomotion and trunk-formation through the creation of extensive pseudopodia-like structures. Two independent Saltatorellota cells can fuse, and they employ various forms of cell division from budding to canonical binary fission. Despite their polymorphisms, members of the Saltatorellota do possess a peptidoglycan cell wall. Their genomes encode flagella and type IV pili as well as a bacterial actin homolog, the saltatorellin. This protein is most similar to MamK, a dynamic filament-forming protein, that aligns and segregates magnetosome organelles via treadmilling. We found saltatorellin to form filaments in both, E. coli and Magnetospirillum gryphiswaldense, leading to the hypothesis that shapeshifting and pseudopodia formation might be driven by treadmilling of saltatorellin.

microbiology↗

bioRxiv2019

An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

Artificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.\n\nHighlight bullet pointsO_LIAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing data\nC_LIO_LIDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approach\nC_LIO_LIJust a fraction of present clusters is needed for classification\nC_LIO_LIFiltering of input data improves classification\nC_LI

microbiology↗