Biology subjects

Schweiger, R.

Publications and source records attributed to Schweiger, R..

15 recordsLinked to original sources

bioRxiv2026

Effects of Drought, Followed by Waterlogging, on Biomass, Leaf Physiology and Chemistry of Wheat Partly Depend on Fertilisation

Drought and waterlogging are prominent climate change challenges that, alone or in sequence, pronouncedly constrain agricultural crop production. A better understanding of relationships between water availability and nutrition regarding plant functioning is needed to optimise fertilisation under these challenges. We investigated impacts of drought, waterlogging and drought followed by waterlogging under two fertilisation regimes (low, normal) on various traits of wheat (Triticum aestivum). Next to biomass and photosynthesis measurements, cross-platform metabolomics was applied for in-depth metabolic analyses of flag leaves. We found detrimental effects of drought and drought-waterlogging on shoot and root biomass, especially at normal fertilisation, while photosynthetic traits were not or only slightly affected. Foliar nitrogen contents were higher in plants exposed to drought or to drought-waterlogging. Numerous primary and specialised metabolites were modulated by fertilisation and/ or watering. In particular, amino acids, benzoxazinoids and flavonoids were affected by fertilisation. Due to watering especially several amino acids changed in concentrations, with the induction pattern partly depending on fertilisation. Our study deepens the understanding of the interplay between water and nutrient availabilities as well as nutritional demands, growth and metabolism of wheat. This is highly relevant to balance sustainable agricultural nutrient management under the challenges of ongoing climate change.

ecology↗

bioRxiv2026

Evolutionary history and polyploidization lead to rapid shifts in chemodiversity of Hypericum

Polyploidization can profoundly affect plant metabolite biosynthesis, yet its influence on chemodiversity remains poorly understood, despite the central role of chemodiversity in mediating plant interactions with the environment. The coexistence of facultative apomictic and sexual reproductive systems across ploidy levels in Hypericum provides an excellent model for investigating the evolution of chemodiversity following polyploidization. We analyzed ploidy levels and leaf metabolic fingerprints across selected populations of three Hypericum taxa, H. maculatum, H. perforatum subsp. perforatum and H. perforatum subsp. veronense. Polyploidization was common across all three taxa. Leaf metabolic fingerprints were more pronouncedly differentiated by the ploidy level of the mother plant (F0) than that of the offspring (F1). Although unique metabolic features emerged in plants of most ploidy levels, diploid plants exhibited fewer metabolic features than polyploid plants. Higher Shannon diversity, functional Hill diversity, and intensities of features belonging to specific chemical families were associated with higher F0 ploidy levels in H. perforatum subsp. perforatum, but not in H. maculatum and H. perforatum subsp. veronense. Our findings demonstrate that polyploidization can lead to rapid shifts in chemodiversity across generations in Hypericum. The fast divergence in chemodiversity associated with polyploidization in H. perforatum may contribute to its remarkable invasive potential.

plant biology↗

bioRxiv2025

Cultivar- and field site-specific protein and metabolite patterns of faba bean (Vicia faba L.) seeds

Sustainable agriculture is an important component of current and future food production, particularly in the face of the rising human world population. In this context, faba bean (Vicia faba L.) is highly valuable due to its capability to enrich soils with nitrogen and because its seeds have a high nutritional value. In this study, we compared the seed chemistry of 11 faba bean cultivars grown at 13 field sites in Germany. Nitrogen and sulfur contents were determined with the Dumas method and protein contents were calculated based on the obtained nitrogen data. SDS-PAGE was used to analyze proteins, while metabolic fingerprinting was applied to screen (semi-)polar metabolites, mainly comprising specialized metabolites. The protein content differed predominantly between cultivars, but also between field sites. The sulfur content strongly depended on the field site. The patterns of proteins and metabolites were largely cultivar-specific. Additionally, the number of metabolic features varied somewhat between field sites. Our study suggests that breeding led to comprehensive changes in the seed chemistry of V. faba bean, going far beyond protein content and certain metabolites targeted in breeding. The rather high consistency when comparing field sites indicates that breeding has led to more or less stable cultivars. Future studies should place more emphasis on far-reaching effects of breeding on the seed proteome and metabolome of faba bean, as various proteins and metabolites in concert determine the nutritional quality of seeds of this important crop species.

plant biology↗

bioRxiv2024

Deep coalescent history of the hominin lineage

Coalescent-based methods are widely used to infer population size histories, but existing analyses have limited resolution at deep time scales (>2 million years ago). Here, using new high-quality telomere-to-telomere genome assemblies, we extend the scope of such inference and reanalyse an ancient peak in human effective population size around 3-6 million years ago, showing that coalescent-based inference can reach much further into the past than previously thought. Furthermore, peaks at approximately the same time as in humans are also observed in chimpanzee and bonobo, but not in gorilla or orangutans. We show that this pattern is unlikely to be an artefact of model violations and discuss its potential implications for understanding hominin evolutionary history. It is known that such peaks can arise from ancestral genetic structure, and consequently we suggest that a parsimonious explanation for the contemporaneous peaks in humans, chimpanzees and bonobos would be a period of complex speciation before final separation of Homo from Pan, in contrast to a scenario where these lineages diverge cleanly from a panmictic common ancestor.

genetics↗

bioRxiv2024

Insights into non-crossover recombination from long-read sperm sequencing

Meiotic recombination is a fundamental process that generates genetic diversity by creating new combinations of existing alleles. Although human crossovers have been studied at the pedigree, population and single-cell level, the more frequent non-crossover events that lead to gene conversion are harder to study, particularly at the individual level. Here we show that single high-fidelity long sequencing reads from sperm can capture both crossovers and non-crossovers, allowing effectively arbitrary sample sizes for analysis from one male. Using fifteen sperm samples from thirteen donors we demonstrate variation between and within donors for the rates of different types of recombination. Intriguingly, we observe a tendency for non-crossover gene conversions to occur upstream of nearby PRDM9 binding sites, whereas crossover locations have a slight downstream bias. We further provide evidence for two distinct non-crossover processes. One gives rise to the vast majority of non-crossovers with mean conversion tract length under 50bp, which we suggest is an outcome of standard PRDM9-induced meiotic recombination. In contrast [~]2% of non-crossovers have much longer mean tract length, and potentially originate from the same process as complex events with more than two haplotype switches, which is not associated with PRDM9 binding sites and is also seen in somatic cells.

genetics↗

bioRxiv2024

Avoidable false PSMC population size peaks occur across numerous studies

Inferring historical population sizes is key to identify drivers of ecological and evolutionary change, and crucial to predict the future of species on our rapidly changing planet. The pairwise sequentially Markovian coalescent (PSMC) method provided a revolutionary framework to reconstruct species demographic histories over millions of years based on the genome sequence of a single individual 1. Here, we detected and solved a common artifact in PSMC and related methods: recent population peaks followed by population collapses. Combining real and simulated genomes, we show that these peaks do not represent true population dynamics. Instead, ill-set default parameters cause false peaks in our own and published data, which can be avoided by adjusted parameter settings. Furthermore, we show that certain population structure changes can cause similar patterns. Newer methods like Beta-PSMC perform better, but do not always avoid this artifact. Our results suggest testing multiple parameters before interpreting recent population peaks followed by collapses, and call for the development of robust methods.

evolutionary biology↗

bioRxiv2024

Metabolic fingerprinting reveals roles of Arabidopsis thaliana BGLU1, BGLU3 and BGLU4 in glycosylation of various flavonoids

Flavonoids are specialized metabolites that play important roles in plants, including interactions with the environment. The high structural diversity of this metabolite group is largely due to enzyme-mediated modifications of flavonoid core skeletons. In particular, glycosylation with different sugars is very common. In this study, the functions of the Arabidopsis thaliana glycoside hydrolase family 1-type glycosyltransferase proteins BGLU1, BGLU3 and BGLU4 were investigated, using a reverse genetics approach and untargeted metabolic fingerprinting. We screened for metabolic differences between A. thaliana wild type, loss-of-function mutants and overexpression lines and partially identified differentially accumulating metabolites, which are putative products and/or substrates of the BGLU enzymes. Our study revealed that the investigated BGLU proteins are glycosyltransferases involved in the glycosylation of already glycosylated flavonoids using different substrates. While BGLU1 appears to be involved in the rhamnosylation of a kaempferol diglycoside in leaves, BGLU3 and BGLU4 are likely involved in the glycosylation of quercetin glycosides in A. thaliana seeds. In addition, we present evidence that BGLU3 is a multifunctional enzyme that catalyzes other metabolic reactions with more complex substrates. This study deepens our understanding of the metabolic pathways and enzymes that contribute to the high structural diversity of flavonoids. HighlightThe proteins BGLU1, BGLU3 and BGLU4 are involved in glycosylations of different, already glycosylated flavonoids in Arabidopsis thaliana. BGLU3 appears to be multifunctional, acting on several complex substrates.

plant biology↗

bioRxiv2023

Influences of chemotype and parental genotype on metabolic fingerprints of tansy plants uncovered by predictive metabolomics.

Intraspecific plant chemodiversity shapes plant-environment interactions. Within species, chemotypes can be defined according to variation in dominant specialised metabolites belonging to certain classes. Different ecological functions could be assigned to these distinct chemotypes. However, the roles of other metabolic variations and the parental genotype of the chemotypes remain poorly explored. Here, we first compared the capacity of terpenoid profiles and metabolic fingerprints to distinguish five chemotypes of common tansy (Tanacetum vulgare) and depict satellite metabolic differences. Metabolic fingerprints captured higher satellite variation while preserving the ability to define chemotypes. These satellite differences might influence plant performance and interactions with the environment. Next, to characterise the influence of the maternal genotype on chemodiversity, we performed variation partitioning and generalised linear modelling. Our findings revealed that maternal genotype was a higher source of chemical variation than chemotype. Predictive metabolomics unveiled 184 markers predicting maternal genotype with 89% accuracy. These markers included, among others, phenolics, whose functions in plant-environment interactions are well established. Hence, these findings place parental genotype at the forefront of intraspecific chemodiversity. We thus recommend considering this factor when comparing the ecology of various chemotypes. Besides, the combined inclusion of inherited and satellite metabolic variation in computational models may help connecting chemodiversity and evolutionary principles.

ecology↗

bioRxiv2023

Ultra-fast genome-wide inference of pairwise coalescence times

The pairwise sequentially Markovian coalescent (PSMC) algorithm and its extensions infer the coalescence time of two homologous chromosomes at each genomic position. This inference is utilized in reconstructing demographic histories, detecting selection signatures, genome-wide association studies, constructing ancestral recombination graphs and more. Inference of coalescence times between each pair of haplotypes in a large dataset is of great interest, as they may provide rich information about the population structure and history of the sample. We introduce a new method, Gamma-SMC, which is >14 times faster than current methods. To obtain this speed up, we represent the posterior coalescence time distributions succinctly as a Gamma distribution with just two parameters; while in PSMC and its extensions, these are held as a vector over discrete intervals of time. Thus, Gamma-SMC has constant time complexity per site, without dependence on a number of discrete time states. Additionally, due to this continuous representation, our method is able to infer times spanning many orders of magnitude, and as such is robust to parameter misspecification. We describe how this approach works, illustrate its performance on simulated and real data, and use it to study recent positive selection in the 1000 Genomes Project dataset.

genetics↗

bioRxiv2021

Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

We benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions by Jing and colleagues with modelling cell-type-specificity and the application of TCA, we further discuss best practices for performing association studies at cell-type resolution. The scripts for reproducing all of our results and figures are publicly available at github.com/cozygene/CellTypeSpecificMethylationAnalysis.

genomics↗

bioRxiv2020

Relative matching using low coverage sequencing

Finding familial relatives using DNA has multiple applications, in genetic genealogy, population genetics, and forensics. So far, most relative matching algorithms rely on detecting identity-by-descent (IBD) segments with high quality genotype data. Recently, low coverage sequencing (LCS) has received growing attention as a promising cost-effective method to ascertain genomic information. However, with higher error rates, it is unclear whether existing IBD detection can work on LCS datasets. Here, we developed and tested a framework for relative matching using sequencing with 1x coverage (1xLCS). We started by exploring the error characteristics of this method compared to array data. Our results show that after some optimization 1xLCS can exhibit the same genotyping discordance rates as the discordance between two array platforms. Using this observation, we developed a hybrid framework for relative matching and tuned this framework with >2,700 pairs of confirmed genealogical relatives that were genotyped using heterogenous datasets. We then obtained array and 1xLCS on 19 samples and use our framework to find relatives in a database of over 3 million individuals. The total length of shared segments obtained by 1xLCS was virtually indistinguishable to genotyping arrays for matches with a total sharing >200cM (second cousins or closer). For more distant relatives, as long as those were detected by both technologies, the total length obtained by LCS and by genotyping arrays was highly correlated, with no evidence of over- or underestimation. Taken together, our results show that 1xLCS can be a valid alternative to arrays for relative matching, opening the possibility for further democratization of genomic data.

bioinformatics↗

bioRxiv2018

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types. Corresponding software is available from: https://github.com/cozygene/TCA.

bioinformatics↗

bioRxiv2018

FactorialHMM: Fast and exact inference in factorial hidden Markov models

MotivationHidden Markov models (HMMs) are powerful tools for modeling processes along the genome. In a standard genomic HMM, observations are drawn, at each genomic position, from a distribution whose parameters depend on a hidden state; the hidden states evolve along the genome as a Markov chain. Often, the hidden state is the Cartesian product of multiple processes, each evolving independently along the genome. Inference in these so-called Factorial HMMs has a naive running time that scales as the square of the number of possible states, which by itself increases exponentially with the number of subchains; such a running time scaling is impractical for many applications. While faster algorithms exist, there is no available implementation suitable for developing bioinformatics applications.\n\nResultsWe developed FactorialHMM, a Python package for fast exact inference in Factorial HMMs. Our package allows simulating either directly from the model or from the posterior distribution of states given the observations. Additionally, we allow the inference of all key quantities related to HMMs: (1) the (Viterbi) sequence of states with the highest posterior probability; (2) the likelihood of the data; and (3) the posterior probability (given all observations) of the marginal and pairwise state probabilities. The running time and space requirement of all procedures is linearithmic in the number of possible states. Our package is highly modular, providing the user with maximal flexibility for developing downstream applications.\n\nAvailabilityhttps://github.com/regevs/factorialhmm

bioinformatics↗

bioRxiv2017

An exact and efficient score test for variance components models

Testing for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of p-values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 (n=13,950) study, and in particular when the individuals in the sample are unrelated. In these cases the SKAT approximation tends to be highly over-conservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact p-values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT.

genetics↗

bioRxiv2017

A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer explicit cell counts without methylation reference can only capture linear combinations of cell counts rather than provide one component per cell type. Our approach, which allows the construction of a set of components such that each component corresponds to a single cell type, therefore provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

bioinformatics↗