Biology subjects

Pichler, M.

Publications and source records attributed to Pichler, M..

15 recordsLinked to original sources

bioRxiv2025

Urbanisation reshapes community assembly differently across coastal beach and dune habitats

Urbanisation can reduce biodiversity through environmental filtering, but its effects may differ among neighbouring habitats within ecotones. Beach-dune systems provide a useful test because strong environmental gradients occur over short distances and overlap with gradients in human pressure. We collected 660 sediment samples from 55 transects along the Dutch North Sea coast, covering established dunes, incipient dunes, the upper intertidal zone, and the lower intertidal zone. Using environmental DNA metabarcoding and habitat-specific joint species distribution models, we tested how distance-to-city was associated with community composition, operational taxonomic unit (OTU) richness, and OTU-level occurrence. We found compositional differentiation along the urban-proximity gradient in both dune habitats, particularly in incipient dunes, but not in the intertidal habitats. Across all habitats, communities far from cities were more similar to one another than communities near cities, indicating greater among-site compositional variation near urban areas. OTU richness and mean OTU occurrence probabilities increased with distance-to-city in the lower intertidal, upper intertidal, and established dunes. Incipient dunes showed no clear richness gradient or directional OTU-level response despite the strong compositional turnover, suggesting that the main response was community replacement rather than loss of OTUs. Overall, habitat position within the beach-dune ecotone influenced whether biodiversity responses to urban proximity led to directional filtering, compositional turnover, or increased community heterogeneity. Our findings show that biodiversity and impact assessments based on a single habitat may miss important ecological change in coastal habitats.

ecology↗

bioRxiv2025

Graph Neural Networks for Likelihood-Free Inference in Diversification Models

A common approach to infer the processes that gave rise to past speciation and extinction rates across taxa, space and time is to formulate hypotheses in the form of probabilistic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. A drawback of this approach is that likelihoods can easily become computationally intractable, limiting our ability to extend current diversification models with new hypothesized mechanisms. Neural networks have been proposed as a likelihood-free alternative for parameter inference of stochastic models, but so far there is little experience in using this method for diversification models, and the quality of the results is likely to depend on finding the right network architecture and data representation. As phylogenies are essentially graphs, graph neural networks (GNNs) appear to be the most natural architecture but previous results on their performance are conflicting, with some studies reporting poor accuracy of GNNs in practice. Here, we show that this underperformance was likely caused by optimization issues and inappropriate pooling operations that flatten the information along the phylogeny and make it harder to extract relevant information about the diversification parameters. When equipped with PhyloPool, a new time-informed pooling procedure, GNNs show similar or better performance compared to all other architectures and data representations (including Maximum Likelihood Estimation) that we tested for two common diversification models, the Constant Rate Birth-Death and the Binary State Speciation and Extinction. We conclude that GNNs could serve as a generic tool for estimating diversification parameters of complex diversification models with intractable likelihoods.

evolutionary biology↗

bioRxiv2024

Metacommunity theory and metabarcoding reveal the environmental, spatial, and biotic drivers of meiofaunal communities in sandy beaches

Sandy beaches are important ecosystems providing coastal protection and recreation, but they face significant threats from human activities and sea level rise. They are inhabited by meiofauna, small benthic invertebrates that are highly abundant and diverse, but are commonly understudied biotic components of beach ecosystems. Here, we investigate the factors shaping meiofaunal metacommunities by employing Generalised Dissimilarity Modelling (GDM) and Joint Species Distribution Modelling (JSDM) to study community turnover and assembly processes. We analysed over 550 meiofauna samples from a >650 km stretch of the southern North Sea coastline using a metabarcoding approach. Our findings reveal that environmental factors, especially Distance from Low Tide and Sediment Grain Size, are important drivers of meiofauna community turnover. This highlights the influence of the gradient from marine to terrestrial habitats and sediment conditions. Spatial factors, which indicate dispersal limitations, also significantly impact community composition, challenging the view that marine meiofauna have broad geographic distributions. The JSDM results show that species sorting by environmental conditions is the dominant process in community assembly with increasing environmental differences between sampling sites, but that biotic associations, or similar environmental preferences, are a major driver of community assembly at sites with similar environmental conditions. Further, we find that spatial factors also significantly influence community assembly across the study region. By facilitating the inference of ecological niches for a high number of meiofaunal taxa, JSDM provides a powerful framework for understanding the ecology of these animals. Our results highlight the importance of considering environmental gradients and dispersal limitations in meiofauna and beach ecosystem research, and future research should aim at adding information on functional traits and biotic interactions under varying environmental conditions to understand meiofauna community dynamics and resilience.

ecology↗

bioRxiv2023

Environmental DNA captures the internal structure of a pond metacommunity

Technological progress is enabling ecologists to create repeated, large-scale, structured, and standardised community surveys. However, it is unclear how best to extract information from these novel community data. We metabarcoded 48 vertebrate species from their eDNA in 320 ponds in England and applied the internal-structure approach, which uses joint species distribution models to explain community compositions as the outcome of four metacommunity assembly processes: environmental filtering, dispersal, species interactions, and stochasticity. We find that the environment plays an important role in community assembly and that the inferred environmental preferences of species are consistent with their ecologies. We also infer negative biotic covariances between fish and amphibians, which is consistent with predator-prey interactions, and high spatial autocorrelation for the palmate newt, which is consistent with its hypothesised relictual distribution. Comparing sites in the metacommunity, environmentally and spatially distinctive sites are better explained by their environmental covariates and geographic locations, respectively, revealing sites where environmental filtering and dispersal limitation act more strongly. Furthermore, species belonging to different trait groups differ in how well environmental covariates, biotic covariances, and geographical locations explain their distributions. Overall, our results highlight the value of a modern interpretation of metacommunity ecology that embraces the fact that assembly processes differ between individual species and sites. We discuss how novel community data make feasible several study-design improvements that will strengthen the inference of metacommunity assembly processes from observational data.

ecology↗

bioRxiv2023

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into many-row (observation), many-column (species) datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These novel community datasets let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers within a joint species distribution model. With this approach, we generate distribution maps for 76 arthro-pod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualise the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this sideways biodiversity modelling method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres.

ecology↗

bioRxiv2022

A phenotypic screening platform for chronic pain therapeutics using all-optical electrophysiology

Chronic pain associated with osteoarthritis (OA) remains an intractable problem with few effective treatment options. New approaches are needed to model the disease biology and to drive discovery of therapeutics. Here, we present an in vitro model of OA pain, where dorsal root ganglion (DRG) sensory neurons were sensitized by a defined mixture of disease-relevant inflammatory mediators, here called Sensitizing PAin Reagent Composition or SPARC. OA-SPARC components showed synergistic or additive effects when applied in combination and induced pain phenotypes in vivo. To measure the effect of OA-SPARC on neural firing in a scalable format for drug discovery, we used a custom system for high throughput all-optical electrophysiology. This system enabled light-based membrane voltage recordings from hundreds of neurons in parallel with single cell resolution and a throughput of up to 500,000 neurons per day, with patch clamp-like single action potential resolution. A computational framework was developed to construct a multiparameter OA-SPARC neuronal phenotype and to quantitatively assess phenotype reversal by candidate pharmacology with different mechanisms of action. We screened ~3000 approved drugs and mechanistically focused compounds, yielding data from over 1.2 million individual neurons with detailed assessment of both functional OA-SPARC phenotype rescue and orthogonal "off-target" effects. Analysis of confirmed hits revealed diverse potential analgesic mechanisms including well-known ion channel modulators as well as less characterized mechanisms including MEK inhibitors and tyrosine kinase modulators, providing validation of the platform for pain drug discovery.

neuroscience↗

bioRxiv2022

Machine learning algorithms predict soil seed bank persistence from easily available traits

QuestionSoil seed banks (SSB), i.e., pools of viable seeds in the soil and its surface, play a crucial role in plant biology and ecology. Information on seed persistence in soil is of great importance for fundamental and applied research, yet compiling datasets on this trait still requires enormous efforts. We asked whether the machine learning (ML) approach could be used to infer and predict SSB properties of a regional flora based on easily available data. LocationEighteen calcareous grasslands located along an elevational gradient of almost 2000 m in the Bavarian Alps, Germany. MethodsWe compared a commonly used ML model (random forest) with a conventional model (linear regression model) as to their ability to predict SSB presence/absence and density using empirical data on SSB characteristics (environmental, seed traits and phylogenetic predictors). Further, we identified the most important determinants of seed persistence in soil for predicting qualitative and quantitative SSB characteristics using the ML approach. ResultsWe demonstrated that the ML model predicts SSB characteristics significantly better than the linear regression model. A single set of predictors (either environment, or seed traits, or phylogenetic eigenvectors) was sufficient for the ML model to achieve high performance in predicting SSB characteristics. Importantly, we established that a few widely available SSB predictors can achieve high predictive power in the ML approach, suggesting a high flexibility of the developed approach for use in various study systems. ConclusionsOur study provides a novel methodological approach that combines empirical knowledge on the determinants of SSB characteristics with a modern, flexible statistical approach based on ML. It clearly demonstrates that ML can be developed into a key tool to facilitate labor-intensive, costly and time-consuming functional trait research.

ecology↗

bioRxiv2021

Fixed or random? On the reliability of mixed-effect models for a small number of levels in grouping variables

O_LIBiological data are often intrinsically hierarchical. Due to their ability to account for such dependencies, mixed-effects models have become a common analysis technique in ecology and evolution. While many questions around their theoretical foundations and practical applications are solved, one fundamental question is still highly debated: When facing a low number of levels should we model a grouping (blocking, clustering) variable as a random or fixed effect? In such situation, the variance of the random effect is imprecise, but whether this affects the statistical properties of the population effect is unclear. C_LIO_LIHere, we analyzed the consequences of including a grouping variable as fixed or random effect in the correctly specified and other possible miss-specified models (too simple or too complex models) for data with small number of levels (2 - 8). For all these options, we calculated type I error rates and power. Moreover, we show how these statistical properties change with the study design. C_LIO_LIWe found that the model choice does not influence the statistical properties of the population effect when the effect is the same at all levels However, if an ecological effect differs among levels, using a random slope and intercept model, and switching to a fixed-effect model only in case of a singular fit, avoids overconfidence in the results. Additionally, power and type I error are strongly influenced by the number of and difference between levels. C_LIO_LIWe conclude that inferring the correct random effect structure is of high importance to get correct statistical properties. When in doubt, we recommend starting with the simpler model and using model diagnostics to identify missing components. When having identified the correct structure, we encourage to start with a mixed-effects model independent of the number of levels and switch to a fixed-effect model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and thus make ecological inference with mixed-effects models more robust for small number of levels. C_LI

ecology↗

bioRxiv2021

Genome structure of Brachionus asplanchnoidis, a Eukaryote with intrapopulation variation in genome size

Eukaryotic genomes vary greatly in size due to variation in the proportion of non-coding DNA, a pattern that emerges both in comparisons at a larger taxonomic scale and at the level of individuals within a species. The rotifer Brachionus asplanchnoidis represents one of the most extreme cases of intraspecific genome size variation among Eukaryotes, displaying almost 2-fold variation within a geographic population. Here we used a whole-genome sequencing approach to identify the underlying DNA sequence differences by assembling a high-quality reference genome draft for one individual of the population and aligning short-reads of 15 individuals from the same geographic population. We identified large, contiguous copy number variable regions (CNVs), which exhibited significant coverage differences among individuals, and whose coverage overall scaled with genome size. CNVs were mainly composed of tandemly repeated satellite DNA, with only few interspersed genes or other sequences, and were characterized by an elevated GC-content. Judging from their distributions across contigs, some CNVs are fragments of accessory (B-)chromosomes while others resemble large insertions to normal chromosomes. CNV patterns in offspring of two parents with divergent genome size, and CNV patterns in several individuals from an inbred line differing in genome size demonstrated inheritance and accumulation of CNVs across generations. Our study provides unprecedented insights into genome size evolution at microevolutionary time scales and thus paves the way for studying genome size evolution in contemporary populations rather than inferring patterns and processes a posteriori from species comparisons.

evolutionary biology↗

bioRxiv2021

Fasting reverses drug-resistance in hepatocellular carcinoma through p53-dependent metabolic synergism

Cancer cells voraciously consume nutrients to support their growth, exposing a metabolic vulnerability that can be therapeutically exploited. Here we show in hepatocellular carcinoma (HCC) cells, xenografts, and in patient-derived HCC organoids that fasting can synergistically sensitize resistant HCC to sorafenib. Mechanistically, sorafenib acts non-canonically as an inhibitor of mitochondrial respiration, causing resistant cells to depend on glycolysis for survival. Fasting, through reduction in glucose and impeded AKT/mTOR-signaling, prevents this Warburg shift. Regulating glucose transporter and pro-apoptotic protein expression, p53 is necessary and sufficient for the sorafenib-sensitizing effect of fasting. p53 is also crucial for fasting-mediated improvement of sorafenib efficacy in an orthotopic HCC mouse model. Together, our data suggest intermittent fasting and sorafenib as rational combination therapy for HCC with intact p53 signaling. As HCC therapy is currently severely limited by resistance, these results should instigate clinical studies aimed at improving therapy response in advanced-stage, and possibly even early-stage, HCC. HIGHLIGHTSO_LIFasting sensitizes resistant HCC xenografts and patient-derived organoids to sorafenib C_LIO_LISorafenib-mediated Warburg shift is prevented by glucose limitation upon fasting C_LIO_LIFasting synergistically improves sorafenib efficacy in non-resistant models C_LIO_LIp53 is required for synergism by regulating glucose uptake and apoptosis C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/430545v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1c0bf9org.highwire.dtl.DTLVardef@15638b4org.highwire.dtl.DTLVardef@1a4b322org.highwire.dtl.DTLVardef@1ec863a_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Molecular diagnosis and prognosis of cancers of unknown-primary (CUPs): progress from a microRNA-based droplet digital PCR assay

Metastasis is responsible for the majority of cancer-related deaths. Particularly challenging is the management of metastatic cancer of unknown primary site (CUP), whose tissue-of-origin (TOO) remains undetermined even after expensive investigations. CUP therapy is rather unspecific and poorly effective. Molecular approaches developed to identify CUPs potential tissue-of-origin, can overcome some of these issues. In this study, we applied a pre-determined set of microRNAs (miRNAs) to infer the TOO of 53 metastatic cancers of unknown or uncertain origin. We designed a molecular assay to quantify 89 miRNAs at the copy number level, using EvaGreen-based Droplet Digital PCR. We assessed miRNA expression in 159 samples including primary tumors from 17 tumor classes (reference set), metastases of known and unknown origin. We applied two different statistical models for class prediction to obtain CUPs most probable TOOs. Specifically, we used the shrunken centroids using PAMR (Prediction Analysis of Microarrays for R) and the least absolute shrinkage and selection operator (LASSO) models. The molecular test was successfully applied to FFPE samples and provided a site-of-origin identification within one-week from the biopsy procedure. The most frequently predicted origins were gastrointestinal, pancreas, breast and lung. The assay was applied to multiple metastases from the same CUP, collected from different metastatic sites: the molecular prediction revealed an impressive agreement in site-of-origin prediction, intrinsically validating our assay. The final prediction was compared with the clinico-pathological hypothesis of primary site. Moreover, a panel of 14 miRNAs proved to have prognostic value and being associated with overall survival. Our study demonstrated that miRNA expression profiling in CUP samples could be employed as diagnostic and prognostic test. Our molecular analysis can be performed on-request, concomitantly with standard diagnostic workup and in association with genetic profiling, to offer valuable indication about the possible primary site, thereby supporting treatment decisions.

pathology↗

bioRxiv2020

Loss of malate-aspartate shuttle component SLC25A12 induces pulmonary metastasis

BackgroundAspartate biosynthesis and its delivery to the cytosol can be crucial for tumor growth in vivo. However, the impact of aspartate synthesis on metastasis has not been studied. We previously described that loss-of-aspartate glutamate carrier 1 (SLC25A12 or AGC1), an important component of the malate-aspartate shuttle, impairs cytosolic aspartate levels, NAD+/NADH ratio, mitochondrial respiration, and tumor growth. Here, we report the impact of AGC1-knockdown on metastasis. ResultsAGC1 expression is positively correlated with worse patient prognosis in many cancers. AGC1-knockdown in mouse lung carcinoma and melanoma cell lines leads to increased pulmonary metastasis following subcutaneous or intravenous injections, respectively. On the other hand, conventional in vitro metastasis assays show no indication of increased metastasis capacity of AGC1-knockdown cells. ConclusionThis study highlights that certain branches of metabolism impact tumor growth and tumor metastasis differently. In addition, it also argues that commonly known metastasis indicators, including EMT genes, cell migration, or colony formation do not always reflect the metastatic capacity in vivo.

cancer biology↗

bioRxiv2019

Comprehensive characterisation of cell-free tumour DNA in plasma and urine of patients with renal tumours

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of [~]33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to [~]50%, including in patients with early-stage and even benign lesions.\n\nA machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.\n\nOverall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.\n\nOne sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

cancer biology↗

bioRxiv2019

Within-population genome size variation is mediated by multiple genomic elements that segregate independently during meiosis

Within-species variation in genome size has been documented in many animals and plants. Despite its importance for understanding eukaryotic genome diversity, there is only sparse knowledge about how individual-level processes mediate genome size variation in populations. Here we study a natural population of the rotifer Brachionus asplanchnoidis whose members differ up to 1.9-fold in genome size, but were still able to interbreed and produce viable offspring. We show that genome size is highly heritable and can be artificially selected up or down, but not beyond a minimum diploid genome size. Analyses of segregation patterns in haploid males reveal that large genomic elements (several megabases in size) provide the substrate of genome size variation. These elements, and their segregation patterns, explain the generation of new genome size variants, the short-term evolutionary potential of genome size change in populations, and some seemingly paradoxical patterns, like an increase in genome size variation among highly inbred lines. Our study suggests that a conceptual model involving only two variables, (1) a minimum genome size of the population, and (2) a vector containing information on additional elements that may increase genome size in this population (size, number, and meiotic segregation behaviour), can effectively address most scenarios of short-term evolutionary change of genome size in a population.

evolutionary biology↗

bioRxiv2017

A 16S rRNA gene sequencing and analysis protocol for the Illumina MiniSeq platform

High-throughput sequencing of the 16S rRNA gene is widely used in microbial ecology, with Illumina platforms being widely used in recent studies. The MiniSeq, Illuminas latest benchtop sequencer, enables more cost-efficient DNA sequencing relative to larger sequencing platforms (e.g. MiSeq). Here we used a modified custom primer sequencing approach to test the fidelity of the MiniSeq for high-throughput sequencing of the V4 hypervariable region of 16S rRNA genes from complex communities in environmental samples. To this end, we designed an additional sequencing primer that enabled application of a dual-index barcoding method on the MiniSeq. A mock community was sequenced alongside the environmental samples as a quality control benchmark. After careful filtering procedures, we were able to recapture a realistic richness of the mock community, and identify meaningful differences in alpha and beta diversity in the environmental samples. These results show that the MiniSeq can produce similar quantities of high quality V4 reads compared to the MiSeq, yet is a cost-effective option for any laboratory interested in performing high-throughput 16S rRNA gene sequencing.\n\nIMPORTANCEWe modified a custom sequencing approach and used a mock community to test the fidelity of high-throughput sequencing on the Illumina MiniSeq platform. Our results show that the MiniSeq can produce similar quantities of high quality V4 reads compared to the MiSeq. In addition, our protocol increases feasibility for small laboratories to perform their own high-throughput sequencing of the 16S rRNA marker gene.

microbiology↗