Biology subjects

Otte, K. A.

Publications and source records attributed to Otte, K. A..

6 recordsLinked to original sources

bioRxiv2026

Phosphoproteomics in Daphnia magna as a tool to decipher molecular mechanisms in ecotoxicological studies

Pollution of aquatic environments poses an increasingly severe threat to ecosystems worldwide, and understanding its molecular consequences for aquatic organisms requires extensive research and the development of advanced analytical tools. Phosphoproteomics can be particularly valuable for this purpose, as shifts in phosphorylation states can serve as early molecular indicators of toxic exposure. The cladoceran Daphnia is a keystone species in aquatic ecosystems, linking lower and higher trophic levels, and is therefore widely used as a model organism in ecotoxicology to study biological consequences of pollution. Here, we present a simple and effective strategy to analyse the phosphoproteome of Daphnia magna, a commonly used Daphnia species in ecotoxicology. Following TiO2-based phosphopeptide enrichment and LC-MS/MS analysis, we identified a comprehensive dataset of 3,532 phosphorylation sites across 1,329 phosphoproteins. These proteins were especially involved in signaling pathways and cellular structure and the vast majority have not yet been demonstrated in other Daphnia species. In conclusion, our results demonstrate that a straightforward phosphoproteomic LC-MS/MS workflow in D. magna can serve as a powerful tool for investigating adverse molecular effects caused by anthropogenic pollution, such as microplastics or pharmaceuticals. Statement of significanceThe dataset presented here demonstrates the feasibility of a simple yet effective strategy to perform phosphoprotemics in Daphnia magna, and it will be particularly valuable for future ecotoxicoproteomics research using this model organism.

pharmacology and toxicology↗

bioRxiv2026

HiReS: A Method for Automated Morphometric Trait Extraction from High-Resolution Plankton Images

Trait-based analyses in plankton ecology require measurements from large numbers of individuals, yet morphometric data are typically collected manually from small subsets. Although deep learning methods enable automated detection and segmentation, extracting quantitative trait data from full-resolution images remains challenging due to memory limitations. We present HiReS (High-Resolution Segmentation), an open-source workflow for automated morphometric trait extraction from large plankton images. HiReS partitions images into overlapping chunks, performs YOLO-based instance segmentation, reconstructs polygon annotations in full-image space, removes truncated and duplicate detections, and computes geometric descriptors. We evaluated the workflow using manually annotated and automated segmentations of Daphnia pulex, Daphnia galeata, and Simocephalus vetulus. Automated measurements reproduced the structure of manual trait distributions and showed strong agreement at both sample and individual levels. A consistent positive bias was observed, reflecting a multiplicative scaling offset rather than distortion of relative trait structure. Subsampling analyses further showed that model-derived medians can outperform manual estimates at low sampling depths. HiReS provides a reproducible and computationally efficient framework for extracting morphometric traits from full-resolution plankton images.

bioinformatics↗

bioRxiv2025

DaphnAI: A Deep Learning Approach for High-Throughput Zooplankton Community Analysis

Zooplankton are critical components of freshwater ecosystems, mediating energy transfer and regulating trophic dynamics. Monitoring their community composition and population structure is essential for understanding ecological responses to environmental change. However, conventional approaches rely on manual processing and classification, which are time-consuming, prone to error, and unsuitable for high-throughput applications. Here, we present a deep learning framework based on the YOLOv12n-seg architecture for the automated identification, instance segmentation, and quantification of zooplankton from high-resolution images. Trained on mesocosm data containing multiple species zooplankton taxa, our model achieved a mean average precision (mAP@50) of 0.899 and demonstrated a 230-fold speed increase over manual annotation, even on standard non-GPU hardware. Unlike previous approaches that focus solely on classification, our model produces pixel-precise segmentation masks, enabling accurate estimation of individual size metrics. This expands the scope of automated zooplankton monitoring from presence/absence data to demographic assessments, including shifts in size distributions and the possibility to be used for unraveling morphological adaptations. Our work demonstrates how deep learning can overcome longstanding limitations in ecological monitoring by enabling scalable, reproducible, and high-resolution quantification of community composition. This approach opens the door to large-scale, long-term studies of freshwater ecosystems.

bioinformatics↗

bioRxiv2020

The adaptive architecture is shaped by population ancestry and not by selection regime

Understanding the genetic architecture of adaptive phenotypes is a key question in evolutionary biology. One particularly promising approach is Evolve and Resequence (E&R), which combines advantages of experimental evolution such as time series, replicate populations and controlled environmental conditions, with whole genome sequencing. The recent analysis of replicate populations from two different Drosophila simulans founder populations, which were adapting to the same novel hot environment, uncovered very different architectures - either many selection targets with large heterogeneity among replicates or fewer selection targets with a consistent response among replicates. Here, we exposed the founder population from Portugal to a cold temperature regime. Although almost no selection targets were shared between the hot and cold selection regime, the adaptive architecture was similar: we identified a moderate number of loci under strong selection (19 selected alleles, mean selection coefficient = 0.072) and very parallel responses in the cold evolved replicates. This similarity across different environments indicates that the adaptive architecture depends more on the ancestry of the founder population than the specific selection regime. These observations have a pronounced impact on our understanding of adaptation in natural populations.Competing Interest StatementThe authors have declared no competing interest.View Full Text

evolutionary biology↗

bioRxiv2019

A generalised approach to detect selected haplotype blocks in Evolve and Resequence experiments

Shifting from the analysis of single nucleotide polymorphisms to the reconstruction of selected haplotypes greatly facilitates the interpretation of Evolve and Resequence (E&R) experiments. Merging highly correlated hitchhiker SNPs into haplotype blocks reduces thousands of candidates to few selected regions. Current methods of haplotype reconstruction from Pool-Seq data need a variety of data-specific parameters that are typically defined ad hoc and require haplotype sequences for validation. Here, we introduce haplovalidate, a tool which detects selected haplotypes in a broad range of Pool-seq time series data without the need of sequenced haplotypes. Haplovalidate makes data-driven choices of two key parameters for the clustering procedure, the minimum correlation between SNPs constituting a cluster and the window size. Applying haplovalidate to simulated and experimental E&R data reliably detects selected haplotype blocks with low false discovery rates - independent if few or many selection targets are included. Our analyses identified an important restriction of the haplotype block-based approach to describe the genomic architecture of adaptation. We detected a substantial fraction of haplotypes containing multiple selection targets. These blocks were considered as one region of selection and therefore led to under-estimation of the number of selection targets. We demonstrate that the separate analysis of earlier time points can significantly increase the separation of selection targets into individual haplotype blocks. We conclude that the analysis of selected haplotype blocks has a large potential for the characterisation of the adaptive architecture with E&R experiments.

evolutionary biology↗

bioRxiv2019

Neuronal function and dopamine signaling evolve at high temperature in Drosophila

Introduction Introduction Main Methods Author contributions Competing interest Materials and Correspondence References Adaptation to local environments is a general principle in nature. In the light of the ongoing climatic change, the mechanisms for temperature adaptation are of particular interest1-2. Many essential biological processes are temperature sensitive, and neuronal activity3-7 is a particularly prominent one as it affects essential, fitness-related traits such as behavior and locomotion8. Here, we investigate if temperature stress triggers adaptation of neuronal signaling. We used replicated laboratory evolution experiments with Drosophila simula ...

evolutionary biology↗