Biology subjects

Walther, D.

Publications and source records attributed to Walther, D..

15 recordsLinked to original sources

bioRxiv2026

Is metabolism spatially optimized? Structural modeling of consecutive enzyme pairs reveals no evidence for spatial optimization of catalytic site proximity.

Metabolic pathways are often hypothesized to benefit from the spatial organization of enzymes, facilitating substrate transfer through mechanisms such as metabolic channeling or metabolon formation. However, it remains unclear whether the spatial proximity of catalytic sites represents a general organizational principle of metabolism or is restricted to specific pathways. Here, we investigate whether consecutive enzymes in metabolic pathways, when physically interacting, exhibit structurally optimized arrangements that minimize distances between their catalytic sites, thereby increasing metabolite transfer efficiency from one enzyme to the next. We first evaluated the ability of current protein-protein interaction prediction methods, including AlphaFold2, AlphaFold3, ESMFold, and HDOCK, to model weak and transient interactions using a benchmark dataset of 112 low-affinity protein dimers from PDBbind. AlphaFold-based approaches performed best in recovering correct interaction geometries, while ESMFold showed limited performance. We further assessed several confidence metrics and identified ipTM, ipSAE, and VoroIF-GNN as the most informative predictors of correct interaction conformations. In addition to simple Euclidean distance metrics, we developed a computational procedure to estimate shortest accessible space paths between catalytic sites in predicted enzyme-enzyme complexes. Applying this framework to 107 consecutive enzyme pairs in E.coli revealed an increased tendency for consecutive enzymes to interact, but no systematic evidence that interacting enzymes position their catalytic sites in spatially optimized configurations. In the predicted complex conformations, catalytic sites tend not to be positioned closer than expected at random. The developed computational workflow provides a general framework for analyzing structural aspects of metabolic organization.

bioinformatics↗

bioRxiv2025

Samplify: A versatile tool for image-based segmentation and annotation of seed abortion phenotypes

Automated seed phenotyping has wide applications in research and agriculture and relies on easy-to-use platforms and pipelines. Seed phenotyping in the model species Arabidopsis thaliana poses a significant challenge due to the large number of tiny seeds produced by individual plants, which are difficult to manually separate and count. Manual counting methods are time-consuming and prone to user bias, particularly for subtle phenotypic changes. To address these limitations, we developed Samplify, a scalable, automated pipeline for seed segmentation and classification. By integrating classical image processing techniques with Metas Segment Anything Model (SAM), Samplify effectively segments Arabidopsis seeds, even in dense clusters where conventional methods fail. To demonstrate its versatility, we quantified the seed abortion occurring in interploidy crossings in Arabidopsis, often referred to as triploid block. Samplify includes a Random Forest classifier trained on a set of computed seed shape features that enables the categorization of seeds into normal, partially aborted, and fully aborted seeds, automating the manual classification process. The tool, designed as a command-line application, significantly reduces manual annotation workload. Our validation across multiple datasets demonstrates high segmentation and classification reliability, making Samplify a valuable resource for the plant research community.

plant biology↗

bioRxiv2025

Multi-Omics Analysis of Heat Stress-Induced Memory in Arabidopsis

In their natural environment, plants experience temperature fluctuations, including intensified heat waves driven by climate change, which pose significant threats to their productivity. To adapt, plants have evolved diverse mechanisms to withstand heat stress (HS), minimizing potential damage and ensuring survival. One such adaptation is acquired thermotolerance (AT), where prior exposure to HS primes plants to withstand subsequent severe HS. AT can persist for several days and involves a recovery period during which plants establish heat stress memory (HSM), reorganize cellular processes, and strengthen stress resilience. The molecular mechanisms underlying HSM remain the subject of active investigation. In this study, we employ a high-throughput comparative multi-omics approach to unravel the transcriptome, metabolome, and proteome of Arabidopsis thaliana seedlings during distinct intervals of the HSM phase. Our findings provide insights into the intricacies of HS recovery and the memory process. Notably, distinct temporal responses emerge at both the transcriptional and protein levels during the early and late recovery phases. Transcripts associated with HSM are upregulated during the early HS recovery phase, indicating a rapid response crucial for initial memory formation, while corresponding protein levels remain elevated throughout the recovery period, supporting memory consolidation. Additionally, metabolite profiles reveal distinctive patterns across the HS memory phase. This marks the first detailed multi-omic analysis of the HSM phase in Arabidopsis seedlings, providing insights into the multifaceted nature of this complex process. These comprehensive datasets hold promise in elucidating regulators of HS resilience, thereby enhancing efforts in breeding HS-tolerant crops

plant biology↗

bioRxiv2025

Rapid, open-source, and automated quantification of the head twitch response in C57BL/6J mice using DeepLabCut and Simple Behavioral Analysis

Serotonergic psychedelics induce the head twitch response (HTR) in mice, an index of serotonin (5-HT) 2A receptor (5-HT2A) agonism and a behavioral proxy for psychedelic effects in humans. Existing methods for detecting HTRs include time-consuming visual scoring, magnetometer-based approaches, and analysis of videos using semi-automated commercial software. Here, we present a new automated approach for quantifying HTRs from experimental videos using the open-source machine learning-based toolkits, DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA). Pose estimation DLC models were trained to predict X,Y coordinates of 13 body parts of C57BL/6J mice using historical experimental videos of HTRs induced by various psychedelic drugs. Next, a non-overlapping set of historical experimental videos was analyzed and used to train SimBA random forest behavioral classifiers to predict the presence of the HTR. The DLC+SimBA approach was then validated using a separate subset of visually scored videos. DLC+SimBA model performance was assessed at different video resolutions (50%, 25%, 12.5%) and frame rates (120, 60, 30 frames per second or fps). Our results indicate that HTRs can be quantified accurately at 50% resolution and 120 fps (precision = 95.45, recall = 95.56, F1 = 95.51) or at lower frame rates and resolutions (i.e., 50% resolution and 60 fps). The best performing DLC+SimBA model combination was deployed to evaluate the effects of bufotenine, a tryptamine derivative with uncharacterized potency and efficacy in the HTR paradigm. Interestingly, bufotenine only induced elevated HTRs (ED50 = 0.99 mg/kg, max counts = 24) when serotonin 1A receptors (5-HT1A) were pharmacologically blocked and activity at other sites of action may also impact its pharmacological effects (e.g., serotonin transporter). HTR counts for a subset of 21 videos from bufotenine experiments were strongly correlated for DLC+SimBA vs. visual scoring and semi-automated software detection methods (r = 0.98 and 0.99). Finally, the DLC+SimBA approach displayed high accuracy when compared to visual scoring of HTRs for three serotonergic psychedelic drugs with variable HTR frequencies (r = 0.99 vs. mean visual scores from 3 blinded raters). In summary, the DLC+SimBA approach represents a modular, noninvasive, and open-source method of HTR detection from experimental videos with accuracy comparable to magnetometer-based approaches and greater speed than visual scoring.

pharmacology and toxicology↗

bioRxiv2025

Deciphering Ibogaines Matrix Pharmacology: Multiple Transporter Modulation at Serotonin Synapses

Ibogaine is the main psychoactive alkaloid produced by the iboga tree (Tabernanthe iboga) that has a unique therapeutic potential across multiple indications, including opioid dependence, substance use disorders, depression, anxiety, posttraumatic stress disorder (PTSD), and traumatic brain injury (TBI). We systematically examined the effects of ibogaine, its main metabolite noribogaine, and a series of iboga analogs at monoamine neurotransmitter transporters, some which have been linked to the oneiric and therapeutic effects of these substances. We report that ibogaine and noribogaine inhibit the transport function of the vesicular monoamine transporter 2 (VMAT2) with sub-micromolar potency in cell-based fluorimetry assays and at individual synaptic vesicle clusters in mouse brain as demonstrated via two-photon microscopy. The iboga compounds also inhibit the plasma membrane monoamine transporters (MATs), prominently including the serotonin transporter (SERT), and a novel iboga target, the organic cation transporter 2 (OCT2). SERT transport inhibition was demonstrated in serotonin axons and soma in the brain and in rat brain synaptosomes, where ibogaine and its analogs did not act as substrate-type serotonin releasers. Noribogaine showed dual inhibition of VMAT2 and SERT with comparable potency, providing an explanatory model for the known neurochemical effects of ibogaine in rodents. Together, the updated profile of the monoamine transporter modulation offers insight into the complexity of the iboga pharmacology, which we termed "matrix pharmacology". The matrix pharmacology concept is outlined and used to explain why ibogaine and noribogaine do not induce catalepsy, as demonstrated in our study, in contrast to other VMAT2 inhibitors. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/641351v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@a1cacaorg.highwire.dtl.DTLVardef@8be27org.highwire.dtl.DTLVardef@1fc320forg.highwire.dtl.DTLVardef@3a8837_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Towards a comprehensive view of the pocketome universe - biological implications and algorithmic challenges.

With the availability of reliably predicted 3D-structures for essentially all known proteins, characterizing the entirety of compound-binding sites (binding pockets on proteins) has become a possibility. The aim of this study was to identify and analyze all compound-binding sites, i.e. the pocketomes, of eleven species from different kingdoms of life to discern evolutionary trends as well as to arrive at a global cross-species view of the pocketome universe. Computational binding site prediction was performed on all protein structures in each species as available from the AlphaFold database. The resulting set of potential binding sites was inspected for overlaps with known pockets and annotated with regard to the protein domains in which they are located. 2D-projection plots of all pockets embedded in a 128-dimensional feature space, and characterizing them with regard to selected physicochemical properties, provide informative, global pocketome maps that unveil differentiating features between pockets. Our study revealed a sub-linear scaling law of the number of unique binding sites relative to the number of unique protein structures per species. Thus, as proteome size increased during evolution and therefore potentially diversified, the number of distinct binding sites, reflecting potentially diversifying functions, grew less than proportionally. We discuss the biological significance of this finding as well as identify critical and unmet algorithmic challenges. Authors summaryThe function of proteins is governed by specific interactions with other molecules, notably small molecules (compounds, such as metabolites). The precise nature of the protein-compound interaction, and thus, the associated function, is determined by the stereochemical and physicochemical properties of the sites at which the interaction occurs (binding pockets). Thus, novel functions (binding of novel compounds) generally require the emergence of new binding sites. With the recent breakthroughs in protein structure prediction, the complete set of protein structures has become available. This allowed us to apply computational binding site predictions and to investigate the entirety of all pockets (the "pocketome") across eleven species from differentkingdoms of life, and to study the relationship between the emergence of novel binding sites in relation to increasing sizes of proteomes, i.e. the set of all protein structures in a given species. Our analysis uncovered a sub-linear relationship between the numbers of unique pockets and unique protein structures, suggesting that during evolution, functional diversity shows signs of saturation, which is consistent with other reports, but approached here from the perspective of compound-binding specificities. Our study constitutes the first large-scale investigation of pocketomes based on the now available high-confidence protein structures.

bioinformatics↗

bioRxiv2024

Regulation of plant metabolism under elevated CO2

Plant responses to changing environments afford complex regulation at transcriptome and proteome level to maintain metabolic homeostasis. Homeostasis itself constitutes a complex and dynamic equilibrium of metabolic reactions and transport processes among cellular compartments. In the present study, we aimed at the highest possible resolution of this network by combining analysis of transcriptome, proteome and subcellular resolved metabolome of plants exposed to rising carbon dioxide concentrations over a time course of one week. To prove suitability of our approach, we included mutants affected in photorespiratory metabolism and, thus, should deviate from the wildtype in their response to elevated CO2. Our multi-omics analysis revealed that the hpr1-1 mutant, defective in peroxisomal hydroxypyruvate reduction, is also affected in cytosolic pyruvate metabolism, reaching out to cysteine synthesis, while the hexokinase mutant hxk1 displays a disturbed redox balance upon changing CO2 levels. For the third mutant, defective in the mitochondrial protein BOU, we found compelling evidence that the function of this transporter is related to lipoic acid metabolism, thus challenging current interpretations. This demonstrates that the combined omics approach introduced here opens new insights into complex metabolic interaction of pathways shared among different cellular compartments.

plant biology↗

bioRxiv2024

Mapping protein-metabolite interactions in E. coli by integrating chromatographic techniques and co-fractionation mass spectrometry.

In our pursuit of understanding the protein-metabolite interactome, we introduced PROMIS, a co-fractionation mass spectrometry (CF-MS) technique focusing on biosynthetic and regulatory processes. However, the challenge lies in distinguishing true interactors from coincidental co-elution when a metabolite co-fractionates with numerous proteins. To address this, we integrated two chromatographic techniques-- size exclusion and ion exchange--to enhance the mapping of protein-metabolite interactions (PMIs) in Escherichia coli. This integration aims to refine the PMI network by considering size and charge characteristics, resulting in 994 interactions involving 51 metabolites and 465 proteins. The PMI network is enriched for known and predicted interactions validating our approachs efficacy. Furthermore, the analysis of protein targets for different metabolites revealed novel functional insights, such as the connection between proteinogenic dipeptides and fatty acid biosynthesis. Notably, we uncovered an inhibitory interaction between the riboflavin degradation product lumichrome and orotate phosphoribosyltransferase (PyrE), a key enzyme in de novo pyrimidine synthesis. Lumichrome supplementation mimicked the biofilm formation inhibition observed in a{Delta} pyrE mutant strain, suggesting lumichrome role in integrating pyrimidine and riboflavin metabolism with quorum sensing and biofilm formation. In summary, our integrated chromatographic approach significantly advances PMI mapping, offering novel insights into functional associations and potential regulatory mechanisms in E. coli.

biochemistry↗

bioRxiv2023

Predictions of DNA mechanical properties at a genomic scale reveal potentially new functional roles of DNA-flexibility.

Mechanical properties of DNA have been implied to influence many its biological functions. Recently, a new high-throughput method, called loop-seq, that allows measuring the intrinsic bendability of DNA fragments, has been developed. Using loop-seq data, we created a deep learning model to explore the biological significance of local DNA flexibility in a range of different species from different kingdoms. Consistently, we observed a characteristic and largely nucleotide-composition-driven change of local flexibility near transcription start sites. No evidence of a generally present region of lowered flexibility upstream of transcription start sites to facilitate transcription factor binding was found. Yet, depending on the actual transcription factor investigated, flanking-sequence-dependent DNA flexibility was identified as a potential factor influencing binding. Compared to randomized genomic sequences, depending on species and taxa, actual genomic sequences were observed both with increased and lowered flexibility. Furthermore, in Arabidopsis thaliana, crossing-over and mutation rates, both de novo and fixed, were found to be linked to rigid sequence regions. Our study presents a range of significant correlations between characteristic DNA mechanical properties and genomic features, the significance of which with regard to detailed molecular relevance awaits further experimental and theoretical exploration.

genomics↗

bioRxiv2023

Updated phylogeny and protein structure predictions revise the hypothesis on the origin of MADS-box transcription factors in land plants

MADS-box transcription factors (TFs) are broadly present in eukaryotes. Varying by domain architecture, MADS-box TFs in land plants are categorized into Type I (M-type) and Type II (MIKC-type). For about twenty years, Type I and II genes were considered orthologous to the SRF and MEF2 genes in animals, respectively, presumably originating from a duplication before the divergence of eukaryotes. Here, we exploited the increasing eukaryotic MADS-box sequences and reassessed their evolution. While supporting the ancient duplication giving rise to SRF- and MEF2-types, we found that Type I and II genes originated from the MEF2-type genes through another duplication in the most recent common ancestor (MRCA) of land plants. Protein structures predicted by AlphaFold2 and OmegaFold support our phylogenetic analyses, with plant Type I and II TFs resembling the MEF2-type structure, rather than SRFs. We hypothesize that the ancestral SRF-type TFs got lost in the MRCA of Archaeplastida (the kingdom Plantae sensu lato). The retained MEF2-type TFs acquired a Keratin-like domain and became MIKC-type upon the evolution of Streptophyta. Subsequently in the MRCA of land plants, M-type TFs evolved from a duplicated MIKC-type precursor through loss of the Keratin-like domain, leading to the Type I clade. Both Type I and II TFs largely expanded and functionally differentiated in concert with the increasing complexity of land plant body architecture. We attribute the adaptation to the terrestrial environment partly to the divergence among MEF2-type MADS-box genes and the repetitive recruitment of these originally stress-responsive TFs into developmental programs, especially those underlying reproduction.

plant biology↗

bioRxiv2022

The K+ exchange antiporter 3 senses the chloroplast energy status to synchronize photosynthesis

Plant photosynthesis contains two functional modules, the light-driven reactions in the thylakoid membrane and the carbon-fixing reactions in the chloroplast stroma. In nature, light availability for photosynthesis often undergoes massive and rapid fluctuations. Efficient and productive use of such variable light supply requires an instant crosstalk and rapid synchronization of both functional modules. Here, we show that this communication involves the stromal exposed regulatory C-terminus (RCT) of the thylakoid K+-exchange antiporter KEA3. RCT-mediated control of KEA3 contributes to the balance between light capture and photoprotection. By combining in silico, in vitro, and in vivo approaches, we demonstrate that the RCT senses the energy state of the chloroplast in form of both, phosphorylation and redox potential, in a pH-dependent manner and regulates KEA3 activity in response. Together our data pinpoint a regulatory feedback loop by which the stromal energy state orchestrates light capture and photoprotection via KEA3.

plant biology↗

bioRxiv2021

Biclique extension as an effective approach to predict novel interaction partners in metabolic compound-protein interaction networks.

MotivationMetabolic networks are complex systems of connected chemical reactions with physical interactions between metabolites and proteins playing a critical role for both metabolic conversion and regulation. In this study, we aimed to predict previously unknown compound-protein interactions (CPI) by transferring the concept of biclique extension, which was developed in the context of drug-target interaction prediction and that is based on the rationale that interactions that readily extend an existing biclique are real, to metabolic CPI networks. ResultsWe developed and tested a workflow to predict CPIs based on the concept of extending existing bicliques and applied it to E. coli and human using their respective known CPI network as input. Depending on the chosen biclique size, for the E. coli network we reached a sensitivity of 39% with an associated precision of 59%. For the larger human CPI network, a sensitivity of 78% with a false-positive rate of less than 5% and an associated precision of 75% was obtained. At more stringent settings, a precision as high as 95% was attainable at the expense of a lowered recall. Prediction performance significantly exceeded that obtained using randomized networks as input. Predicted novel interactions were tested for biomolecular function involvement, with TCA-cycle and ribosomal processes found associated with particularly pronounced statistical significance. As we demonstrate, our approach holds great potential to increase efficiency of experimental testing of CPIs and can readily be transferred to other species of interest. Availability and implementationThe R code and datasets are available at https://github.com/SandraThieme/BiPredict.

bioinformatics↗

bioRxiv2020

Plasticity of the primary metabolome in 241 cold grown Arabidopsis thaliana accessions and its relation to natural habitat temperature

In the present study, 241 natural accessions of Arabidopsis thaliana were grown under two different temperature regimes, 16 {degrees}C and 6 {degrees}C, and growth parameters were recorded together with metabolite profiles to investigate the natural variation in metabolic responses and growth rates. Primary metabolism and growth rates of accessions significantly differed between accessions and both growth conditions. Relative growth rates showed high correlations to specific metabolite pools. Metabolic distances based on whole metabolite profiles were built from principal component centroids between both growth setups. Genomic prediction using ridge-regression best linear unbiased prediction (rrBLUP) revealed a significant prediction accuracy of metabolite profiles in both conditions and metabolic distances, which suggests a tight relationship between genome and primary metabolome. GWAS analysis revealed significantly associated SNPs for a number of metabolites, especially for fumarate metabolism at low temperature. A highly significant correlation was observed between metabolic distances and maximum temperature in the original growth habitat between January and March. Inverse data-driven modelling revealed that metabolic pathway regulation and metabolic reaction elasticities distinguish accessions originating from warm and cold growth habitats.

plant biology↗

bioRxiv2018

Biparental inheritance of chloroplasts is controlled by lipid biosynthesis

In most eukaryotes, organellar genomes are transmitted preferentially by the mother, but molecular mechanisms and evolutionary forces underlying this fundamental biological principle are far from understood. It is believed that biparental inheritance promotes competition between the cytoplasmic organelles and allows the spread of so-called selfish cytoplasmic elements. Those can be, for example, fast replicating or aggressive chloroplasts (plastids) that are incompatible with the hybrid nuclear genome and therefore maladaptive.\n\nHere we show that the ability of plastids to compete against each other is a metabolic phenotype determined by extremely rapidly evolving genes in the plastid genome of the evening primrose Oenothera. Repeats in the regulatory region of accD (the plastid-encoded subunit of the acetyl-CoA carboxylase, which catalyzes the first and rate limiting step of lipid biosynthesis), as well as in ycf2 (a giant reading frame of still unknown function), are responsible for the differences in competitive behavior of plastid genotypes. Polymorphisms in these genes influence lipid synthesis and most likely profiles of the plastid envelope membrane. These in turn determine plastid division and/or turn-over rates and hence competitiveness. This work uncovers cytoplasmic drive loci controlling the outcome of biparental chloroplast transmission. Here, they define the mode of chloroplast inheritance, since plastid competitiveness can result in uniparental inheritance (through elimination of the \"weak\" plastid) or biparental inheritance (when two similarly \"strong\" plastids are transmitted).\n\nSignificance statementPlastids and mitochondria are usually uniparentally inherited, typically maternally. When the DNA-containing organelles are transmitted to the progeny by both parents, evolutionary theory predicts that the maternal and paternal organelles will compete in the hybrid. As their genomes do not undergo sexual recombination, one organelle will \"try\" to outcompete the other, thus favoring the evolution and spread of aggressive cytoplasms. The investigations described here in the evening primrose, a model species for biparental plastid transmission, have discovered that chloroplast competition is a metabolic phenotype. It is conferred by rapidly evolving genes that are encoded on the chloroplast genome and control lipid biosynthesis. Due to their high mutation rate these loci can evolve and become fixed in a population very quickly.

genetics↗

bioRxiv2018

A genome-wide scan for correlated mutations detects macromolecular and chromatin loop interactions in Arabidopsis thaliana.

The concept of exploiting correlated mutations has been introduced and applied successfully to identify interactions within and between biological macromolecules. Its rationale lies in the preservation of physical interactions via compensatory mutations. With the massive increase of available sequence information, approaches based on correlated mutations have regained considerable attention.\n\nWe analyzed a set of 10,707,430 single nucleotide polymorphisms detected in 1,135 accessions of the plant Arabidopsis thaliana. To measure their covariance and to reveal the global genome-wide sequence correlation structure of the Arabidopsis genome, the adjusted mutual information has been estimated for each possible pair of polymorphic sites. We developed a series of filtering steps to account for genetic linkage and lineage relations between Arabidopsis accessions, as well as transitive covariance as possible confounding factors. We show that upon appropriate filtering, correlated mutations prove indeed informative with regard to molecular interactions, and furthermore, appear to reflect on chromosomal interactions.\n\nOur study demonstrates that the concept of correlated mutations can also be applied successfully to within-species sequence variation and establishes a promising approach to help unravel the complex molecular interactions in A. thaliana and other species with broad sequence information.

genomics↗