Biology subjects

Mach, R. L.

Publications and source records attributed to Mach, R. L..

9 recordsLinked to original sources

bioRxiv2025

TU_MyCo-Vision: A Deep Learning Tool for Detection of Cell Morphologies in Fungal Microscopic Images

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observation remains a widely used approach to study these phenotypes. However, variation in sample preparation and operators skill can limit the scale of sample processing or introduce bias. Although several image-based cell detection tools have been developed, most are tailored to specific applications or limited to a particular taxon. To address the need for a tool applicable to the polymorphic, yeast-like fungus Aureobasidium pullulans, and with potential applicability to other taxa, we developed TU_MyCo-Vision, an Ultralytics YOLO (You Only Look Once) based object detection tool for identifying 13 fungal morphotypes in bright-field microscopic images. The tool integrates a YOLOv11m-based object detector trained on a custom dataset of 1,504 annotated images and a standalone graphical user interface that enables downstream data analysis and visualization of results. The best-performing model (Zulu_s3) achieved a mean precision of 73.4%, a recall of 66.5%, a mean average precision at 50% IoU (mAP@50) of 73.5%, and a mean average precision at varying IoU thresholds between 50 to 90% IoU (mAP@50-95) of 54.5% across all 13 classes. The single-group analysis pipeline was validated on a 90-image test set, generating six quantitative summaries, including absolute counts, relative and mean relative abundance plots, stacked bar plots, and clustered heatmaps. Multi-group evaluation on previously unseen datasets comprising Candida albicans, Komagataella phaffii, and Aspergillus niger spores demonstrated the tools potential applicability to other genera. TU_MyCo-Vision is distributed as a fully packaged, cross-platform executable, eliminating the need for environment setup or manual installation of dependencies. Built entirely on open-source frameworks, it provides a foundational and potentially extensible solution for automated fungal morphology detection and analysis. Author SummaryWe developed TU_MyCo-Vision to address challenges in fungal microscopic imaging. Fungi, such as Aureobasidium pullulans, display a remarkable ability to switch cell shapes (up to thirteen in this species alone) depending on their environment. While microscopy remains a popular method for observing these changes, manual analysis is limited by individual expertise and the number of images that can be processed, often making results subjective and difficult to scale. To overcome these challenges, we built an Ultralytics YOLOv11-based cell detector that can automatically detect and categorize thirteen fungal cell shapes from brightfield microscopic images. We designed TU_MyCo-Vision to be accessible, with a simple graphical user interface, integrated data analysis suite, and distribution as a standalone application for both Windows and macOS, so it can be used even by those with limited computational skills. Our tool demonstrated strong performance, achieving over 73% precision. Importantly, it also worked well on images from other fungal species, showing potential to be further developed as a general fungal cell morphology tool. We hope TU_MyCo-Vision will contribute to making standardized, high-throughput phenotyping of fungi accessible to a broader community.

microbiology↗

bioRxiv2025

Dark matter of an orchid: metagenome of the microbiome associated with the rhizosphere of Dactylorhiza traunsteineri

Plant microbiota forms complex co-associations with its host, promoting health in natural environments. Root-associated bacteria (RAB) colonize root compartments and can modulate plant functions by producing bioactive compounds as part of their secondary metabolism. We present an in-depth analysis of the rhizosphere-associated microbiome of the endangered marsh orchid Dactylorhiza traunsteineri. Using deep sequencing of 16S rRNA genes, we identified Proteobacteria, Actinobacteria, Myxococcota, Bacteroidota, and Acidobacteria as predominant phyla associated with the rhizosphere of D. traunsteineri. Using deep shotgun metagenomics and de novo assembly, we extracted high-quality metagenome-assembled genomes (MAGs), revealing significant metabolic and biosynthetic potentials of the D. traunsteineris RABs. Our study offers a comprehensive investigation into the microbial community of the D. traunsteineri rhizosphere, highlighting a potential novel source of critical bioactive substances. Our study offers novel insights and a robust platform for future investigations into D. traunsteineris rhizosphere, crucial for understanding plant-microbe interactions and aiding conservation efforts for endangered orchids.

microbiology↗

bioRxiv2025

Genome sequencing and physiological characterization of three Neoarthrinium moseri strains

Fungi play essential ecological roles and have been utilized by humans for diverse applications such as industrial enzyme production or as sources of bioactive compounds. Recent research has highlighted the Amphisphaeriales order (Ascomycota) as promising producers of secondary metabolites of pharmaceutical importance. Within this family, the recently established genus Neoarthrinium includes species such as N. brasiliensis, N. lithocarpicola, N. moseri, N. trachycarpi, and N. urticae. Existing literature has primarily focused on the taxonomy and phylogeny of Neoarthrinium, leaving its physiology, ecology, and metabolic potential unexplored. This study presents the first investigation of the metabolic and genomic potential of N. moseri. We describe the isolation of two South-Asian N. moseri strains and the genome sequencing of these strains alongside the Colombian ex-type strain for the species. Comparative genome analysis reveals an exceptionally high number of biosynthetic gene clusters (BGCs), surpassing those of many other fungi in the Amphisphaeriales order. Additionally, the genome of N. moseri contains a diverse repertoire of carbohydrate-active enzymes (CAZymes), supporting its hypothesized ecological role as a phyllosphere fungus (putatively an endophyte and/or saprotroph). Ecophysiological assays, including BIOLOG phenotyping, demonstrate its ability to utilize a broad range of carbon sources, emphasizing ecological versatility. This study highlights N. moseri as a promising candidate for secondary metabolite discovery, providing foundational insights into the metabolic and genomic potential of the Neoarthrinium genus and related fungi.

microbiology↗

bioRxiv2025

Bacterial diversity turnover estimates in a continental river system

Understanding bacterial dynamics in large river systems is crucial for predicting continental-scale ecological functioning under anthropogenic pressures. Here, two consecutive surveys 6-years apart along the 2600 km Danube River found that carbon incorporation per cell and hour decreased by 5000 atoms every kilometer and that cells multiplied five times during their travel down the entire river. Resolving these cell turnovers taxonomically revealed taxa with a hundredfold difference from these average numbers. Bacterial community turnover was due to replacement (phylotype turnover) and could be linked to species sorting. This was despite an overall decrease in diversity richness downstream. Using linear models, we were able to relate carbon, cell, phylotype and diversity turnover rates to water residence time and discharge with outliers associated with human impacts. As such the reproduceable macroecological models predict microbial changes from anthropogenic and climate alterations along a continental drainage system providing insights into their ecological consequences.

microbiology↗

bioRxiv2025

In vivo activation of the dia BGC allows consolidation of the biosynthetic pathways of diaporthin, dichlorodiaporthin, diaporthinic acid, and diaporthinol

Fungal secondary metabolites exhibit remarkable chemical diversity and biological activity, making them valuable sources of bioactive compounds. A group of polyketides, with a similar core structure, have been isolated previously from different fungi - the phytotoxins diaporthin and orthosporin, the mediocre antimicrobial dichlorodiaporthin, as well as diaporthinol and diaporthinic acid. Previous studies in Aspergillus oryzae suggest that diaporthin and orthosporin originate from a different biosynthetic gene cluster than dichlorodiaporthin, while the biosynthetic routes for diaporthinic acid and diaporthinol have not yet been identified. In this study, we successfully activated the dia biosynthetic gene cluster in Trichoderma reesei via transcription factor overexpression, leading to the identification of diaporthinic acid as the primary metabolite. Structural elucidation using NMR confirmed its identity, while bioactivity assays revealed no significant antimicrobial effects. Further, diaporthin, orthosporin, dichlorodiaporthin, and diaporthinol, as well as an isomer of alternariol were attributed to the same cluster. Gene deletion experiments demonstrated that Dia1, Dia4, and Dia5 are essential for diaporthinic acid biosynthesis, with Dia4 likely catalyzing the oxidation of dichlorodiaporthin yielding diaporthinic acid. Dia2 and Dia3 were found to be dispensable, challenging previous in vitro findings for the biosynthesis of dichlorodiaporthin. These findings provide new insights into the biosynthetic network of the dia BGC, expanding our understanding of natural product biosynthesis in filamentous fungi.

biochemistry↗

bioRxiv2024

Discovery of a novel antifungal compound, ilicicolin K, through genetic activation of the ilicicolin biosynthetic pathway in Trichoderma reesei

In the quest to discover novel antifungal agents and new antifungal production processes, we investigated the biosynthetic gene cluster (BGC) for ilicicolin H in the fungus Trichoderma reesei. While the BGC is silent under standard cultivation conditions, we achieved to activate it by over-expressing its transcription factor TriliR. Successful BGC activation was confirmed by RT-qPCR, proteomic and metabolomic analyses. Metabolomic profiling upon BGC expression revealed high-yield production of the supposed main product ilicicolin H. To elucidate the functionality of this BGC, we employed a combination of overexpression and deletions of individual biosynthetic gene cluster constituents. Deletion of triliA, encoding for the core polyketide synthase TriliA, completely ceased product formation, as expected. In contrast to previous heterologous expression experiments, we could demonstrate that the epimerase TriliE is necessary for the formation of ilicicolin H in the native host. While we hardly observed any of the previously reported side- or shunt products associated with heterologous ilicicolin H expression, we discovered a novel member of the ilicicolin family using a metabolomic molecular networking approach. This new compound, which we termed ilicicolin K, is expressed in substantial amounts in the genetically engineered Trichoderma reesei, enabling us to elucidate its structure by NMR. The structure of ilicicolin K is similar to that of ilicicolin H but differs by an additional hydroxylation and an intramolecular etherification of the hydroxyl group at the pyridone towards the tyrosine moiety of the molecule. Initial tests of ilicicolin K showed antifungal activity against Saccharomyces cerevisiae and Aspergillus nidulans with a similar minimum inhibitory concentration as ilicicolin H.

biochemistry↗

bioRxiv2022

FunOrder 2.0 - a fully automated method for the identification of co-evolved genes

Coevolution is an important biological process that shapes interacting species or even proteins - may it be physically interacting proteins or consecutive enzymes in a metabolic pathway. The detection of co-evolved proteins will contribute to a better understanding of biological systems. Previously, we developed a semi-automated method, termed FunOrder, for the detection of co-evolved genes from an input gene or protein set. We demonstrated the usability and applicability of FunOrder by identifying essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. To fully automatize the method, we used several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output. Further, we replaced the BLAST with the DIAMOND tool, which enhanced speed and allows the future integration of larger proteome databases. The introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity. Additionally, the changes lay the foundation for future high-throughput applications of FunOrder 2.0 in different phyla to solve different biological problems. AUTHOR SUMMARYCoevolution is a process which arises between different species or even different proteins that interact with each other. Any change occurring in one partner must be met by a corresponding change in the other partner to maintain the interaction throughout evolution. These interactions may occur in symbiotic relationships or between rivaling species. Within an organism, consecutive enzymes of metabolic pathways are also subjected to coevolution. We developed a fully automated method, FunOrder 2.0, for the detection of co-evolved proteins, which will contribute to a better understanding of protein interactions within an organism. We demonstrate that this method can be used to identify essential genes of the secondary metabolism of fungi, but FunOrder 2.0 may also be used to detect pathogenicity factors or remains of horizontal gene transfer next to many other biological systems that were shaped by coevolution.

bioinformatics↗

bioRxiv2021

Fast and efficient CRISPR-mediated genome editing in Aureobasidium pullulans using Cas9 ribonucleoproteins

Aureobasidium pullulans is a ubiquitous, polyextremotolerant, "yeast-like" ascomycete used for the industrial production of pullulan and other products and as biocontrol agent in the agriculture. Its application potential and its wide-spread occurrence make A. pullulans an interesting study object. The availability of a fast and efficient genome editing method is an obvious advantage for future basic and applied research on A. pullulans. In this study, we describe the development of a CRISPR/Cas9-based genome editing method using ribonucleoproteins (RNPs). We demonstrate that this method can be used for single and multiplex genome editing using only RNPs by targeting ura3 (encoding for orotidine-5'-phosphate decarboxylase), praics (encoding for phosphoribosyl aminoimidazole-succinocarboxamide synthase) and asl (encoding for arginine succinate lyase). We demonstrate the applicability of Trichoderma reesei pyr4 and Aspergillus fumigatus pyrG to complement the ura3 deficiency. Further, we show that the usage of RNPs can boost the homologous recombination rate up to nearly 100%, even when using only 20bp long homologous flanks. Therefore, the repair cassettes can be constructed by a single PCR, abolishing the need for laborious and time-consuming cloning. The here presented method allows fast and efficient genome editing for gene deletions, modifications, and insertions in A. pullulans.

bioengineering↗

bioRxiv2021

The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties. Humankind uses SMs as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs but they have problems with distinguishing essential genes from gap genes and defining the borders of a BGC. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or co-expression analyses. In this study, we developed a novel tool that allows automated identification of essential genes in a BGC based solely on genomic data. The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution - searches for co-evolutionary linked genes in the BGCs. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs, including antibiotics and other pharmaceuticals.

bioinformatics↗