Biology subjects

Noll, M.

Publications and source records attributed to Noll, M..

4 recordsLinked to original sources

bioRxiv2025

Diversity of stomatal and cuticular structures affect microbial colonization in temperate forest tree species

Throughout their life cycle, tree leaves are subject to colonization and degradation by microorganisms, including fungi, bacteria, and algae. These relationships co-evolved with chemical properties, leaf shape, and surface structures. Here we quantified leaf abaxial surface texture features based on variables extracted from scanning electron microscopic images, resulting in an abstract quantitative texture complexity score. This complexity score was used to test functional hypotheses such as growth habitat preferences and microbial colonization patterns. Here we show that the evolution of leaf surface texture complexity traits correlated with anatomical features such as stomatal density and leaf orientation and correlated with Ellenberg temperature habitat indicator. Furthermore, increasing leaf surface texture complexity was found to be negatively correlated with plant pathogen richness (broad-leaved species) or lichenization (conifers), suggesting protection effects. Moreover, we found a negative correlation of leaf surface texture complexity with fungal and bacterial specialists. Our results highlight leaf surface texture complexity as a key, previously underappreciated trait shaping microbial diversity and leaf-microbe interaction patterns. This opens promising avenues for future research on plant-microbe co-evolution, trait-based ecosystem modeling, and the potential use of surface traits in forest management and disease resistance strategies.

plant biology↗

bioRxiv2025

Seasonal and diel environment effects on host-seeking behaviour in the European tick vector, Ixodes ricinus

BackgroundPatterns of arthropod vector abundance and activity are key determinants of biting risk, and thus the risk of vector-borne pathogen transmission. Ticks are important vectors of animal and human pathogens worldwide. Their host-seeking activity (questing) directly influences the risk of exposure to ticks and their pathogens, but the drivers of this behaviour are poorly understood. MethodsThe concurrent seasonal and diel variation in Ixodes ricinus questing behaviour was investigated, for the first time, by blanket dragging every four hours over a 24-hour sampling cycle each month, from January to December 2022. Associations between tick density and environmental factors were evaluated by Generalised Linear Mixed Modelling (GLMM). ResultsA predominantly nocturnal questing activity pattern was observed. Activity was also moderated by seasonal moisture availability, being most evident in nymphs in months with high saturation deficit. The density of questing nymphs was positively associated with darkness, and negatively associated with saturation deficit. ConclusionsThe observed seasonal questing patterns are largely consistent with published data; however this is the first study to highlight the importance of nocturnal feeding and periods of high saturation deficit in the field. These commonly overlooked daily rhythms and environmental moderators of tick behaviour have important implications for assessing the relative hazard and potential exposure to ticks and their pathogens for different hosts, and for estimating abundance in scientific research and surveillance.

ecology↗

bioRxiv2018

An unusual and physiologically vital protein with guanylate cyclase and P-type ATPase like domain in a pathogenic protist

Cyclic GMP is considered as one of the master regulators of diverse functions in eukaryotes; its architecture and functioning in protozoans remain poorly understood however. We characterized an unusual and extra-large guanylate cyclase (477-kDa) containing at least 4 putative P-type ATPase motifs and 21 transmembrane helices in a common parasitic protist, Toxoplasma gondii. This protein, termed as TgATPaseP-GC due to its anticipated multi-functionality, localizes in the plasma membrane at the apical pole, while the corresponding cGMP-dependent protein kinase (TgPKG) is distributed in cytomembranes. Both proteins are expressed constitutively during the entire lytic cycle of the parasite in human cells, which suggests a post-translational control of cGMP signaling. Homology modeling indicated an activation of guanylate cyclase by heterodimerization of its two cyclase domains. TgATPaseP-GC is refractory to genetic deletion, and its CRISPR/Cas9-mediated disruption aborts the lytic cycle. Likewise, Cre/loxP-regulated knockdown of the TgATPaseP-GC by 3 UTR excision inhibited the parasite growth due to impairments in motility-dependent egress and invasion events. Consistently, cGMP-specific phosphodiesterase inhibitors restored the gliding motility of the mutant. A genetic repression of TgPKG, or its pharmacological inhibition phenocopied the defects observed in the TgATPaseP-GC mutant. Our data show a vital function of cGMP signaling, which is inducted by an alveolate-specific guanylate cyclase coupled to P-type like ATPase, and transduced by a dedicated PKG in T. gondii. The presence of TgATPaseP-GC orthologs in many other alveolates with contrasting habitats implies a divergent functional repurposing of cGMP signaling in protozoans. The work also lays an avenue to systematically dissect the cascade and understand its evolution in a model protist.

microbiology↗

bioRxiv2017

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

BackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).\n\nFindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.\n\nConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

plant biology↗