Biology subjects

Korte, A.

Publications and source records attributed to Korte, A..

14 recordsLinked to original sources

bioRxiv2025

PERM1 Gene Delivery via AAV Prevents Heart Failure in a Mouse Model of Pressure Overload

Heart failure with reduced ejection fraction (HFrEF) remains a leading cause of mortality worldwide. A hallmark of HFrEF is impaired cardiomyocyte contractility accompanied by disrupted mitochondrial bioenergetics; however, no current therapy targets both pathologies simultaneously. PERM1, a striated muscle-specific regulator of mitochondrial bioenergetics, is downregulated in HFrEF patients. We recently demonstrated that overexpression of PERM1 via adeno-associated virus 9 (AAV9-PERM1) enhances both cardiac contractility and mitochondrial biogenesis in C57BL/6 mice. In this study, we evaluated the therapeutic potential of AAV9-PERM1 in a pressure overload-induced mouse model of HFrEF. C57BL/6 mice were treated with either AAV9-PERM1 or control AAV9-GFP (1x1012 GC/mouse), followed by transverse aortic constriction (TAC) surgery. At 4 weeks post-TAC, control mice receiving AAV-GFP exhibited reduced left ventricular ejection fraction (LVEF), whereas AAV-PERM1 preserved LVEF at baseline levels. This cardioprotective effect was sustained through 8 weeks. Notably, AAV9-PERM1 completely abrogated TAC-induced cardiac hypertrophy and fibrosis. Mitochondrial analysis revealed that AAV9-PERM1 preserved mitochondrial DNA copy number and TFAM protein levels, both of which were reduced by TAC in control hearts. AAV9-PERM1 also improved mitochondrial respiration using pyruvate and octanoylcarnitine as substrates and prevented TAC-induced impairments in oxidative capacity. While PGC-1 expression remained unchanged in control TAC hearts, it was modestly yet significantly upregulated by AAV9-PERM1 in both sham and TAC groups. In addition, AAV9-PERM1 suppressed TAC-induced increases in O-GlcNAcylation, a stress-related posttranslational modification of proteins. Co-immunoprecipitation further revealed interactions of PERM1 with creatine kinase and troponin C, key proteins in ATP transduction and contractility, suggesting a functional coupling between mitochondrial energetics and contractility. In conclusion, AAV-PERM1 gene therapy effectively preserves cardiac function under pressure overload by maintaining mitochondrial biogenesis, respiration capacity and contractility. This study further suggests AAV-PERM1 as a promising therapeutic strategy for HFrEF.

physiology↗

bioRxiv2025

Rapid adaptation and extinction across climates in synchronized outdoor evolution experiments of Arabidopsis thaliana

Climate change is threatening species with extinction, and rapid evolutionary adaptation may be their only option for population rescue over short ecological timescales. However, direct observations of rapid genetic adaptation and population dynamics across climates are rare across species. To fill this gap, we conducted a replicated, globally synchronized evolution experiment with the plant Arabidopsis thaliana for 5 years in over 30 outdoor experimental gardens with distinct climates across Europe, the Levant, and North America. We performed whole-genome sequencing on [~]70,000 surviving reproductive individuals and directly observed rapid and repeatable adaptation across climates. Allele frequency changes over time were parallel in experimental evolution replicates within the same climates, while they diverged across contrasting climates--with some allele frequency shifts best explained by strong selection between -46% to +60%. Screening the genome for signals of rapid climate adaptation identified a polygenic architecture with both known and novel adaptive genetic variants connected to important ecological phenotypes including environmental stress responses, CAM5 and HEAT SHOCK FACTORs, and germination and spring flowering timing, CYTOCHROME P450s and TSF. We found evolutionary adaptation trends were often predictable, but variable across environments. In warm climates, high evolutionary predictability was associated with population survival up to 5 years, while erratic trends were an early warning for population extinction. Together, these results show rapid climate adaptation may be possible, but understanding its limits across species will be key for biodiversity forecasting.

evolutionary biology↗

bioRxiv2024

Natural variation in salt-induced changes in root:shoot ratio reveals SR3G as a negative regulator of root suberization and salt resilience in Arabidopsis

Soil salinity is one of the major threats to agricultural productivity worldwide. Salt stress exposure alters root and shoot growth rates, thereby affecting overall plant performance. While past studies have extensively documented the effect of salt stress on root elongation and shoot development separately, here we take an innovative approach by examining the coordination of root and shoot growth under salt stress conditions. Utilizing a newly developed tool for quantifying the root:shoot ratio in agar-grown Arabidopsis seedlings, we found that salt stress results in a loss of coordination between root and shoot growth rates. We identify a specific gene cluster encoding domain-of-unknown-function 247 (DUF247), and characterize one of these genes as Salt Root:shoot Ratio Regulator Gene (SR3G). Further analysis elucidates the role of SR3G as a negative regulator of salt stress tolerance, revealing its function in regulating shoot growth, root suberization, and sodium accumulation. We further characterize that SR3G expression is modulated by WRKY75 transcription factor, known as a positive regulator of salt stress tolerance. Finally, we show that the salt stress sensitivity of wrky75 mutant is completely diminished when it is combined with sr3g mutation. Together, our results demonstrate that utilizing root:shoot ratio as an architectural feature leads to the discovery of new stress resilience gene. The studys innovative approach and findings not only contribute to our understanding of plant stress tolerance mechanisms but also open new avenues for genetic and agronomic strategies to enhance crop environmental resilience.

plant biology↗

bioRxiv2023

permGWAS2: Enhanced and Accelerated Permutation-based Genome-Wide Association Studies

MotivationPermutation-based significance thresholds have been shown to be a robust alternative to Bonferroni-based significance thresholds in genome-wide association studies (GWAS). However, the implementation of permutation-based thresholds is computationally demanding. The recently published method permGWAS introduced a batch-wise approach using 4D tensors to efficiently compute permutation-based GWAS. However, running multiple univariate tests in parallel leads to many repetitive computations and increased computational resources. More importantly, the previous version of permGWAS does not take into account the population structure when permuting the phenotype. ResultsWe propose permGWAS2, an improved and accelerated version that uses a block matrix decomposition to optimize computations, thereby reducing redundant computations. It also introduces an alternative permutation strategy that takes into account the population structure during permutation. We show that this improved framework provides a more streamlined approach to performing permutation-based GWAS with a lower false discovery rate compared to the previous version and the traditional Bonferroni correction. AvailabilitypermGWAS2 is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS.

bioinformatics↗

bioRxiv2023

Natural variation of warm temperature-induced raffinose accumulation identifies TREHALOSE-6-PHOSPHATE SYNTHASE 1 as a modulator of thermotolerance

High temperature stress limits plant growth and reproduction. Exposure to high temperature, however, also elicits a conserved physiological response, which protects plants from the damage evoked by heat. This response involves a partial reconfiguration of the plant metabolome including the accumulation of the trisaccharide raffinose. In this study, we explored the intra-specific variation of warm temperature-induced raffinose accumulation as a metabolic marker for temperature responsiveness with the aim to identify genes that contribute to plant thermotolerance. By combining raffinose measurements in 250 Arabidopsis thaliana accessions following a mild heat treatment with genome-wide association studies we identified five genomic regions that were associated with the observed trait variation. Subsequent functional analyses confirmed a causal relationship between TREHALOSE-6-PHOSPHATE SYNTHASE 1 (TPS1) and warm temperature-dependent raffinose synthesis. Moreover, complementation of the tps1-1 null mutant with functionally distinct TPS1 isoforms differentially affected carbohydrate metabolism under more severe heat stress. While higher TPS1 activity was associated with reduced endogenous sucrose levels and thermotolerance, disruption of trehalose 6-phosphate signalling resulted in higher accumulation of transitory starch and sucrose and was associated with enhanced heat resistance. Taken together, our findings suggest a role of trehalose 6-phosphate in thermotolerance most likely through its regulatory function in carbon partitioning and sucrose homeostasis.

plant biology↗

bioRxiv2022

Efficient Permutation-based Genome-wide Association Studies for Normal and Skewed Phenotypic Distributions

MotivationGenome-wide Association Studies (GWAS) are an integral tool for studying the architecture of complex genotype and phenotype relationships. Linear Mixed Models (LMMs) are commonly used to detect associations between genetic markers and the trait of interest, while at the same time allowing to account for population structure and cryptic relatedness. Assumptions of LMMs include a normal distribution of the residuals and that the genetic markers are independent and identically distributed - both assumptions are often violated in real data. Permutation-based methods can help to overcome some of these limitations and provide more realistic thresholds for the discovery of true associations. Still, in practice they are rarely implemented due to its high computational complexity. ResultsWe propose permGWAS, an efficient linear mixed model reformulation based on 4D-tensors that can provide permutation-based significance thresholds. We show that our method outperforms current state-of-the-art LMMs with respect to runtime and that a permutation-based threshold has a lower false discovery rate for skewed phenotypes compared to the commonly used Bonferroni threshold. Furthermore, using permGWAS we re-analysed more than 500 Arabidopsis thaliana phenotypes with 100 permutations each in less than eight days on a single GPU. Our re-analyses suggest that applying a permutation-based threshold can improve and refine the interpretation of GWAS results. AvailabilitypermGWAS is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS.

bioinformatics↗

bioRxiv2021

Global genetic heterogeneity in adaptive traits

Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS for a variety of phenotypes in the model plant species Arabididopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, while others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be incredibly sensitive to sampling and population structure.

evolutionary biology↗

bioRxiv2020

GWAS analysis combined with QTL mapping identify CPT3 and ABH as genes underlying dolichol accumulation in Arabidopsis

Dolichols (Dols), ubiquitous components of living organisms, are indispensable for cell survival. In plants, as well as other eukaryotes, Dols are crucial for posttranslational protein glycosylation, aberration of which leads to fatal metabolic disorders in humans. Until now, the regulatory mechanisms underlying Dol accumulation remain elusive. In this report, we have analyzed the natural variation of the accumulation of Dols and six other isoprenoids between 120 Arabidopsis thaliana accessions. Subsequently, by combining QTL and GWAS approaches, we have identified several candidate genes involved in the accumulation of Dols, polyprenols, plastoquinone, and phytosterols. The role of two genes implicated in the accumulation of major Dols in Arabidopsis - the AT2G17570 gene encoding a long searched for cis-prenyltransferase (CPT3) and the AT1G52460 gene encoding an alpha-beta hydrolase (ABH) - is experimentally confirmed. These data will help to generate Dol-enriched plants which might serve as a remedy for Dol-deficiency in humans.

genomics↗

bioRxiv2020

Genetic mapping of the early responses to salt stress in Arabidopsis thaliana

Salt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single setup is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from one hour to seven days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (Fv'/Fm') greatly affected growth maintenance in the early phase of salt stress, while maximum quantum yield (QY max) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QY max and Fv'/Fm' were selected for validation using T-DNA insertion lines. We characterised an unknown protein kinase found in the QY max locus, which reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the identified candidate genes will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses, while identifying associations across different traits and time points, which likely constitute the genetic components of salinity tolerance.

plant biology↗

bioRxiv2020

Using local convolutional neural networks for genomic prediction

The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. With increasing computational power and more and more data to potentially utilize, Machine Learning and especially Deep Learning have risen in popularity over the last few years. In this study, we are proposing the use of local convolutional neural networks for genomic prediction, as a region specific filter corresponds much better with our prior genetic knowledge of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000) and real Arabidopsis data (n = 2,039) for a variety of traits with the local convolutional neural network outperforming both multi layer perceptrons and convolutional neural networks for basically all considered traits. Linear models like the genomic best linear unbiased prediction that are often used for genomic prediction are outperformed by up to 24%. Highest gains in predictive ability was obtained in cases of medium trait complexity with high heritability and large training populations. However, for small dataset with 100 or 250 individuals for the training of the models, the local convolutional neural network is performing slightly worse than the linear models. Nonetheless, this is still 15% better than a traditional convolutional neural network, indicating a better performance and robustness of our proposed model architecture for small training populations. In addition to the baseline model, various other architectures with different windows size and stride in the local convolutional layer, as well as different number of nodes in subsequent fully connected layers are compared against each other. Finally, the usefulness of Deep Learning and in particular local convolutional neural networks in practice is critically discussed, in regard to multi dimensional inputs and outputs, computing times and other potential hazards.

genetics↗

bioRxiv2019

GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

MotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naive Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.\n\nResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.\n\nAvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

bioinformatics↗

bioRxiv2019

The landscape of chloroplast genome assembly tools

BackgroundChloroplasts are intracellular organelles that enable plants to conduct photosynthesis. They arose through the symbiotic integration of a prokaryotic cell into an eukaryotic host cell and still contain their own genomes with distinct genomic information. Plastid genomes accommodate essential genes and are regularly utilized in biotechnology or phylogenetics. Different assemblers that are able to assess the plastid genome have been developed. These assemblers often use data of whole genome sequencing experiments, which usually contain reads from the complete chloroplast genome. ResultsThe performance of different assembly tools has never been systematically compared. Here we present a benchmark of seven chloroplast assembly tools, capable of succeeding in more than 60% of known real data sets. Our results show significant differences between the tested assemblers in terms of generating whole chloroplast genome sequences and computational requirements. The examination of 105 data sets from species with unknown plastid genomes leads to the assembly of 20 novel chloroplast genomes. ConclusionsWe create docker images for each tested tool that are freely available for the scientific community and ensure reproducibility of the analyses. These containers allow the analysis and screening of data sets for chloroplast genomes using standard computational infrastructure. Thus, large scale screening for chloroplasts within genomic sequencing data is feasible.

bioinformatics↗

bioRxiv2018

Systems approaches provide new insights into Arabidopsis thaliana root growth under mineral nutrient limitation

The molecular genetic mechanisms by which plants modulate their root growth rate (RGR) in response to nutrient deficiency are largely unknown. Using a panel of Arabidopsis thaliana natural accessions, we provide a comprehensive combinatorial analysis of RGR variation under macro- and micronutrient deficiency, namely phosphorus (P), iron (Fe), and zinc (Zn), which affect root growth in opposite directions. We found that while -P stimulates early RGR of most accessions, -Fe or -Zn reduces it. The combination of either -P-Fe or -P-Zn leads to suppression of the growth inhibition exerted by -Fe or -Zn alone. Surprisingly, Arabidopsis reference accession Columbia (Col-0) is not representative of the species under -P and -Zn. Using a genome wide association study, we identify candidate genes that control RGR under the assayed nutrient deficiency conditions. By using a network biology driven search using these candidate genes, we further identify a functional module enriched in regulation of cell cycle, DNA replication and chromatin modification that possibly underlies the suppression of root growth reduction in -P-Fe conditions. Collectively, our findings provide a framework for understanding the regulation of RGR under nutrient deficiency, and open new routes for the identification of both large effect genes and favorable allelic variations to improve root growth.

plant biology↗

bioRxiv2018

Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana

Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water-loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination ({delta}13C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and {delta}13C further suggests that this variation has consequences on water-use efficiency. Genome-wide association analyses indicate a complex genetic architecture underlying not only variation in stomata patterning but also to its co-variation with carbon uptake parameters. Yet, we report two novel QTL affecting {delta}13C independently of stomata patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and co-variation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favor the evolution of drought avoidance mechanisms over drought escape strategies.

plant biology↗