Biology subjects

Ludwig, M.

Publications and source records attributed to Ludwig, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Acclimation to enhanced photorespiratory conditions increases the effectiveness of C2 photosynthesis in C3-C4 intermediates

- C2 photosynthesis reduces photorespiratory carbon loss by shuttling glycine from mesophyll (M) to bundle sheath (BS) tissues, where glycine decarboxylase (GDC) releases CO2 that accumulates around rubisco. C2 photosynthesis has been considered a constitutive character, but as a metabolic trait, it may acclimate to the rate of photorespiratory glycine production. - We grew eleven species from two eudicot and two grass lineages spanning C3 to C2 phenotypes at 32 {degrees}C days and 200 or 800 {micro}mol mol-1 CO2. Acclimation was assessed using leaf gas exchange, GDC P-protein immunolocalization, M and BS ultrastructure, photosynthetic enzyme activity, and photosynthesis modelling. - Low relative to high growth CO2 enhanced C2-related phenotypes in sub-C2 and C2 species but not C3 or proto-Kranz species. Sub-C2 species positioned chloroplasts closer to mitochondria and had lower modelled BS conductance. C2 species increased rubisco capacity and GDC allocation to the BS. Aspartate aminotransferase activity increased in both sub-C2 and C2 species. - Acclimation to greater photorespiratory conditions strengthens C2 photosynthesis, but only in species whose BS has crossed a threshold of organelle and GDC investment. We hypothesize that acclimation could expose C2-related traits to selection and facilitate the origin of C4 photosynthesis.

plant biology↗

bioRxiv2026

Outbred Drosophila populations reveal diet-dependent genetic effects on development time

Metabolic effects of genetic variation often depend on diet, yet the loci underlying diet-dependent developmental responses remain incompletely defined. Here we combine multi-trait phenotyping of Drosophila Genetic Reference Panel lines with genome-wide mapping in newly developed Drosophila Recombinant Populations. High sugar causes genotype- and life-stage-dependent changes in metabolic and life-history traits, with development time emerging as a highly heritable, sugar-sensitive phenotype. Mapping in 16 outbred advanced intercross populations reveals distinct association landscapes under low- and high-sugar diets, with a concentrated low-sugar signal, a more distributed high-sugar pattern, and identified genotype-by-diet loci including tap, Eip75B and Cerk. Functional perturbation supports diet-dependent effects for several prioritized candidates. Allele-frequency analyses identify operationally defined thrifty-like variants associated with delayed development under high sugar and relatively earlier development under low sugar; these variants are enriched for cell-adhesion, neurodevelopmental, and morphogenetic processes. Together, these results establish an outbred Drosophila framework for dissecting how dietary sugar remodels the genetic architecture of development time.

genetics↗

bioRxiv2025

Proteomic profiling of extracellular vesicles distinguishes prostate cancer molecular subtypes

Prostate cancer is the most common non-cutaneous cancer among men in the United States. Most prostate cancers are driven by androgen receptor (AR) signaling, but there are an increasing number of cases that lose AR and gain neuroendocrine (NE) features (AR-/NE+) or lack both (AR-/NE-). These latter subtypes are particularly aggressive and lethal. Extracellular vesicles (EVs) have shown great potential as biomarkers for noninvasive liquid biopsy assays, as EVs contain biomolecules from their cells of origin. Here, we used a shotgun proteomics approach with mass spectrometry to interrogate the global proteome of EVs isolated from prostate cancer cell lines reflecting diverse clinical subtypes, including AR-/NE+ and AR-/NE-models. We identified 3,952 EV proteins, which clustered largely by tumor subtype and provided enough proteomic coverage to derive classic gene signatures of AR or NE identity that are of high relevance for prostate cancer prognostication. EVs isolated from AR+ cells displayed high levels of proteins regulated by AR and mTOR signaling. EVs isolated from AR-/NE+ cells contained known NE markers such as SYP and CHGA, whereas EVs from AR-/NE-models were enriched in basal cell markers and proteins that regulate epithelial-to-mesenchymal transition (EMT). We integrated our cell line data with recently published EV proteomics data from 27 advanced prostate cancer patients and found 2,733 overlapping proteins including cell surface markers relevant to prostate cancer, AR activity indicators, and proteins enriched in specific subtypes (AR+, AR-/NE-, AR-/NE+). This approach is especially promising for rare cancer subtypes, such as prostate cancers that lose AR- related features and gain NE features, so as to optimize the use of these liquid biopsy samples for clinical decision making.

cancer biology↗

bioRxiv2024

Evaluating phasic transcutaneous vagus nerve stimulation (taVNS) with pupil dilation: the importance of stimulation intensity and sensory perception

The efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) as a non-invasive method to modulate physiological markers of noradrenergic activity of the Locus Coeruleus (LC), such as pupil dilation, is increasingly more discussed. However, taVNS studies show high heterogeneity of stimulation effects. Therefore, a taVNS setup was established here to test different frequencies (10 Hz and 25 Hz) and intensities (3 mA and 5 mA) during phasic stimulation (3 s) with time-synchronous recording of pupil dilation in younger adults. Specifically, phasic real taVNS and higher intensity led to increased pupil dilation, which is consistent with phasic invasive VNS studies in animals. The results also suggest that the influence of intensity on pupil dilation may be stronger than that of frequency. However, there was an attenuation of taVNS-induced pupil dilation when differences in perception of sensations were considered. Specifically, pupil dilation during phasic stimulation increased with perceived stimulation intensity. The extent to which the effect of taVNS induces pupil dilation and the involvement of sensory perception in the stimulation process are discussed here and require more extensive research. Additionally, it is crucial to strive for comparable stimulation sensations during systematic parameter testing in order to investigate possible effects of phasic taVNS on pupil dilation in more detail.

neuroscience↗

bioRxiv2024

Soil carbon in the world's tidal marshes

Tidal marshes are threatened coastal ecosystems known for their capacity to store large amounts of carbon in their water-logged soils. Accurate quantification and mapping of global tidal marshes soil organic carbon (SOC) stocks is of considerable value to conservation efforts. Here, we used training data from 3,710 unique locations, landscape-level environmental drivers and a newly developed global tidal marsh extent map to produce the first global, spatially-explicit map of SOC storage in tidal marshes at 30 m resolution. We estimate the total global SOC stock to 1 m to be 1.44 Pg C, with a third of this value stored in the United States of America. On average, SOC in tidal marshes 0-30 and 30-100 cm soil layers are estimated at 83.1 Mg C ha-1 (average predicted error 44.8 Mg C ha-1) and 185.3 Mg C ha-1 (average predicted error 105.7 Mg C ha-1), respectively. Our spatially-explicit model is able to capture 59% of the variability in SOC density, with elevation being the strongest driver aside from soil depth. Our study reveals regions with high prediction uncertainty and therefore highlights the need for more targeted sampling to fully capture SOC spatial variability.

ecology↗

bioRxiv2023

iMOUSE - Reforming the Strategy of Refinement and Reduction for indispensable laboratory animal-based studies in translational research

Considering the intricate nature of biological processes within organisms, it is undeniable that relying solely on in vitro-generated primary-cell-like cultures or organ-like products in preclinical and basic research is insufficient to replace animal-based studies fully. This limitation is particularly significant when considering the regulations enforced by legislative assemblies worldwide. The necessity of animal-based studies to approve chemicals and medications. In contradiction, European countries aim to banish animal-based studies. Therefore, we must understand the impact of the data refinement and experiment replacement strategy we will introduce here. This project aimed to revolutionize data acquisition in animal-based studies by transforming manual observation into a reliable digital process. Reliable digital data will be generated by having the potential to reduce human bias by simply reducing human interaction. Additionally, reducing human interaction will reduce the severity levels due to stress reduction, fulfilling the 3R principles. Therefore, the first goal was to develop and implement a scalable, stable, running, and remotely accessible camera-based monitor system (the iMouse solution). At the same time, the target was to develop a retrofit solution (DigiFrame) for existing home-cage systems, not interfering with the regular workflow in animal facilities. As a result, we developed a digital monitoring system, named iMouseTV platform based on existing open-source software, allowing users to observe, record, share, and review animal-based studies within the home cage anytime from anywhere, reducing the stress level for the animals. Our systems first Proof of concept ran for over two years at the LIV in Hamburg. We also investigated an effective way to reduce data generation by setting up specific zones for detecting the motion of choice (e.g., drinking, food intake). The data sets can be stored, shared, and reviewed by users and refined by algorithms aiming to recognize the dedicated motions of the animals automatically. The implementation of the ML algorithms allows the iMouse solution to recognize whether an individual mouse was drinking and for how long and store results in the annotated video file and graph format. However, the identification and continuous tracking of the species is still in progress. In conclusion, we established a scalable human-independent monitoring and recording system, which can be implemented into the existing structures of institutions and companies without changing handling processes, to monitor animals and observe them by getting reliable digital data. Moreover, it is fundamental for automatic recognition within animal-based studies based on Artificial Intelligence.

animal behavior and cognition↗

bioRxiv2023

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

cancer biology↗

bioRxiv2023

Small molecule machine learning: All models are wrong, some may not even be useful

Small molecule machine learning tries to predict chemical, biochemical or biological properties from the structure of a molecule. Applications include prediction of toxicity, ligand binding or retention time. A recent trend is to develop end-to-end models that avoid the explicit integration of domain knowledge via inductive bias. A central assumption in doing so, is that there is no coverage bias in the training and evaluation data, meaning that these data are a representative subset of the true distribution we want to learn. Usually, the domain of applicability is neither considered nor analyzed for such large-scale end-to-end models. Here, we investigate how well certain large-scale datasets from the field cover the space of all known biomolecular structures. Investigation of coverage requires a sensible distance measure between molecular structures. We use a well-known distance measure based on solving the Maximum Common Edge Subgraph (MCES) problem, which agrees well with the chemical intuition of similarity between compounds. Unfortunately, this computational problem is provably hard, severely restricting the use of the corresponding distance measure in large-scale studies. We introduce an exact approach that combines Integer Linear Programming and intricate heuristic bounds to ensure efficient computations and dependable results. We find that several large-scale datasets frequently used in this domain of machine learning are far from a uniform coverage of known biomolecular structures. This severely confines the predictive power of models trained on this data. Next, we propose two further approaches to check if a training dataset differs substantially from the distribution of known biomolecular structures. On the positive side, our methods may allow creators of large-scale datasets to identify regions in molecular structure space where it is advisable to provide additional training data.

bioinformatics↗

bioRxiv2022

MAD HATTER correctly annotates 98% of small molecule tandem mass spectra searching in PubChem

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach correct annotation rates that users would wish for. Here, we present a novel computational method called MO_SCPLOWADC_SCPLOW HO_SCPLOWATTERC_SCPLOW for this task. MO_SCPLOWADC_SCPLOW HO_SCPLOWATTERC_SCPLOW combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number words in the compound description starting with the letter u. We then show that MO_SCPLOWADC_SCPLOW HO_SCPLOWATTERC_SCPLOW reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Finally, we explain what evaluation glitches were necessary for MO_SCPLOWADC_SCPLOW HO_SCPLOWATTERC_SCPLOW to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments.

bioinformatics↗

bioRxiv2022

Interferon receptor gene dosage determines multiple hallmarks of Down syndrome

Trisomy 21 causes Down syndrome, a condition characterized by cognitive impairments, immune dysregulation, and atypical morphogenesis. Using whole blood transcriptome analysis, we demonstrate that specific overexpression of four interferon receptors encoded on chromosome 21 associates with chronic interferon hyperactivity and systemic inflammation in Down syndrome. To define the contribution of interferon receptor overexpression to Down syndrome phenotypes, we used genome editing to correct interferon receptor gene dosage in mice carrying triplication of a large genomic region orthologous to human chromosome 21. Normalization of interferon receptor copy number attenuated lethal antiviral responses, prevented heart malformations, decreased developmental delays, improved cognition and normalized craniofacial anomalies. Therefore, interferon receptor gene dosage determines major hallmarks of Down syndrome, indicating that trisomy 21 elicits an interferonopathy amenable to therapeutic intervention. One-Sentence SummaryCorrection of interferon receptor gene dosage rescues multiple key phenotypes in a mouse model of trisomy 21.

genetics↗

bioRxiv2021

CREB5 reprograms nuclear interactions to promote resistance to androgen receptor targeting therapies

Metastatic castration resistant prostate cancers (mCRPC) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ART). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically-approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins (RIME), we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein-protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found CREB5 was associated with TGF{beta} and Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

cancer biology↗

bioRxiv2021

Monitoring spore dispersal and early infections of Diplocarpon coronariae causing apple blotch using selected spore traps and a new qPCR method

Apple blotch (AB) is a major disease of apple in Asia and recently emerged in Europe and the USA. It is caused by the fungus Diplocarpon coronariae (Dc) (formerly: Marssonina coronaria; teleomorph: Diplocarpon mali) and leads to severe defoliation of apple trees in late summer resulting in reduced yield and fruit quality. To develop effective disease management strategies, a sound knowledge of the pathogens biology is crucial. Data on the early phase of disease development is scarce: no data on spore dispersal in Europe is available. We developed a highly sensitive TaqMan qPCR method to quantify Dc conidia in spore trap samples. We monitored temporal and spatial dispersal of conidia of Dc, and progress of AB in spring and early summer in an extensively managed apple orchard in Switzerland in 2019 and 2020. Our results show that Dc overwinters in leaf litter and spore dispersal and primary infections occur in late April and early May. We provide the first results describing early-season dispersal of conidia of Dc, which, combined with the observed disease progress, helps to understand the disease dynamics and will be a basis for improved disease forecast models. Using the new qPCR method, we detected Dc in buds, on bark and fruit mummies, suggesting that several apple tissues may serve as overwintering habitats for the fungus, in addition to fallen leaves.

microbiology↗

bioRxiv2021

Assigning confidence to structural annotations from mass spectra with COSMIC

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. As biological interpretation relies on accurate structure annotations, the ability to assign confidence to such annotations is a key outstanding problem. We introduce the COSMIC workflow that combines structure database generation, in silico annotation, and a confidence score consisting of kernel density p-value estimation and a Support Vector Machine with enforced directionality of features. In evaluation, COSMIC annotates a substantial number of hits at small false discovery rates, and outperforms spectral library search for this purpose. To demonstrate that COSMIC can annotate structures never reported before, we annotated twelve novel bile acid conjugates; nine structures were confirmed by manual evaluation and two structures using synthetic standards. Second, we annotated and manually evaluated 315 molecular structures in human samples currently absent from the Human Metabolome Database. Third, we applied COSMIC to 17,400 experimental runs and annotated 1,715 structures with high confidence that were absent from spectral libraries.

bioinformatics↗

bioRxiv2020

An unexpected bifurcation in the Pointed transcriptional effector network contributes specificity and robustness to retinal cell fate acquisition

Spatiotemporally specific and robust cell fate transitions are fundamental to the development of appropriately patterned tissues. In the Drosophila retina, receptor tyrosine kinase / mitogen activated protein kinase (MAPK) signaling acts through the transcriptional effector Pointed (Pnt) to direct two distinct rounds of photoreceptor specification. A relay mechanism between two Pnt isoforms, a MAPK responsive form PntP2 and a constitutively active form PntP1, initiates and sustains the transcriptional response. Here, we report an unexpected bifurcation in the Pnt effector network. We show that PntP2 works redundantly with a closely related but previously uncharacterized isoform, PntP3, to activate pntP1 during specification of first round photoreceptors. Intrinsic activity differences between PntP2 and PntP3, combined with positive and negative transcriptional auto- and cross-regulation, buffer first-round fates against conditions of low signaling. In contrast, in a mechanism that may be adaptive to the stronger signaling environment used to specify second round fates, PntP2 uniquely activates pntP1. We propose that differences in expression patterns, transcriptional activities and regulatory interactions between Pnt isoforms together facilitate context-appropriate cell fate specification in different signaling environments.

developmental biology↗

bioRxiv2020

Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.

bioinformatics↗

bioRxiv2020

Classes for the masses: Systematic classification of unknowns using fragmentation spectra

Metabolomics experiments can employ non-targeted tandem mass spectrometry to detect hundreds to thousands of molecules in a biological sample. Structural annotation of molecules is typically carried out by searching their fragmentation spectra in spectral libraries or, recently, in structure databases. Annotations are limited to structures present in the library or database employed, prohibiting a thorough utilization of the experimental data. We present a computational tool for systematic compound class annotation: CANOPUS uses a deep neural network to predict 1,270 compound classes from fragmentation spectra, and explicitly targets compounds where neither spectral nor structural reference data are available. CANOPUS even predicts classes for which no MS/MS training data are available. We demonstrate the broad utility of CANOPUS by investigating the effect of the microbial colonization in the digestive system in mice, and through analysis of the chemodiversity of different Euphorbia plants; both uniquely revealing biological insights at the compound class level.

bioinformatics↗

bioRxiv2020

JAK1 inhibition blocks lethal sterile immune responses:implications for COVID-19 therapy

Cytokine storms are drivers of pathology and mortality in myriad viral infections affecting the human population. In SARS-CoV-2-infected patients, the strength of the cytokine storm has been associated with increased risk of acute respiratory distress syndrome, myocardial damage, and death. However, the therapeutic value of attenuating the cytokine storm in COVID-19 remains to be defined. Here, we report results obtained using a novel mouse model of lethal sterile anti-viral immune responses. Using a mouse model of Down syndrome (DS) with a segmental duplication of a genomic region encoding four of the six interferon receptor genes (Ifnrs), we demonstrate that these animals overexpress Ifnrs and are hypersensitive to IFN stimulation. When challenged with viral mimetics that activate Toll-like receptor signaling and IFN anti-viral responses, these animals overproduce key cytokines, show exacerbated liver pathology, rapidly lose weight, and die. Importantly, the lethal immune hypersensitivity, accompanying cytokine storm, and liver hyperinflammation are blocked by treatment with a JAK1-specific inhibitor. Therefore, these results point to JAK1 inhibition as a potential strategy for attenuating the cytokine storm and consequent organ failure during overdrive immune responses. Additionally, these results indicate that people with DS, who carry an extra copy of the IFNR gene cluster encoded on chromosome 21, should be considered at high risk during the COVID-19 pandemic. One Sentence SummaryInhibition of the JAK1 kinase prevents pathology and mortality caused by a rampant innate immune response in mice.

immunology↗

bioRxiv2019

ZODIAC: database-independent molecular formula annotation using Gibbs sampling reveals unknown small molecules

1The confident high-throughput identification of small molecules remains one of the most challenging tasks in mass spectrometry-based metabolomics. SIRIUS has become a powerful tool for the interpretation of tandem mass spectra, and shows outstanding performance for identifying the molecular formula of a query compound, being the first step of structure identification. Nevertheless, the identification of both molecular formulas for large compounds above 500 Daltons and novel molecular formulas remains highly challenging. Here, we present ZODIAC, a network-based algorithm for the de novo estimation of molecular formulas. ZODIAC reranks SIRIUS molecular formula candidates, combining fragmentation tree computation with Bayesian statistics using Gibbs sampling. Through careful algorithm engineering, ZODIACs Gibbs sampling is very swift in practice. ZODIAC decreases incorrect annotations 16.2-fold on a challenging plant extract dataset with most compounds above 700 Dalton; we then show improvements on four additional, diverse datasets. Our analysis led to the discovery of compounds with novel molecular formulas such as C24H47BrNO8P which, as of today, is not present in any publicly available molecular structure databases.

bioinformatics↗