Biology subjects

Tiede, T.

Publications and source records attributed to Tiede, T..

6 recordsLinked to original sources

bioRxiv2026

Plasma-derived extracellular vesicles as potential biomarkers and mediators of functional alterations in MELAS

Mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes (MELAS) syndrome is a genetic disorder characterized by progressive neuromuscular and multisystem symptoms. MELAS typically manifests during childhood, can be difficult to diagnose, and has no cure. Extracellular vesicles (EVs) are lipid-enclosed nanoparticles secreted from cells that contain biological cargo and have demonstrated potential as biomarkers. We investigated the potential of plasma-derived EVs as diagnostic biomarkers of MELAS and examined their functional effects on mitochondrial respiration in treated skeletal muscle myotubes. Plasma-derived EVs were isolated from MELAS patients and age- and sex-matched control individuals, and biophysical characteristics and cargo of EVs analyzed. A Mito Stress Test was performed to assess oxygen consumption rate (OCR) in healthy myotubes treated with Control- or MELAS-EVs to determine the functional effects of circulatory EVs. Nine MELAS patients from two families were studied, and the results were categorized by age, sex and mtDNA heteroplasmy level. EV size and zeta potential remained unchanged. However, total EV concentration was higher in MELAS patients, particularly for small-EVs (<200 nm) and in younger patients (<25 years old). Relative protein yield per EV was lower in the MELAS group, especially among female and younger individuals. EV double-stranded DNA (dsDNA) concentration did not differ between MELAS- and Control-EVs overall, but was higher in male MELAS patients. Protein markers typically enriched in small-EVs showed altered expression in MELAS EVs: TSG101 and CD63 were lower, while flotillin-1 was higher compared to Control-EVs. A decrease in basal OCR was shown in cells treated with MELAS-EVs, with a similar response noted in the group treated with EVs from female MELAS patients. Post-treatment analysis showed no differences in oxidative phosphorylation (OXPHOS) subunit levels between cells treated with MELAS- and Control-EVs. In conclusion, plasma-derived EVs show promise as potential biomarkers for MELAS, and circulating EVs in this patient population may contribute to systemic metabolic dysfunction.

Cell Biology↗

bioRxiv2021

The Personalized Cancer Network Explorer (PeCaX) as a visual analytics tool to support molecular tumor boards

BackgroundPersonalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. ResultsThe Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker.

bioinformatics↗

bioRxiv2021

de novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

BackgroundWith a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. ResultsWe present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet is freely available as open-source software. ConclusionThe proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

systems biology↗

bioRxiv2020

Eleven biosynthetic genes explain the majority of natural variation for carotenoid levels in maize grain

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize is a food staple. Extensive natural variation exists for carotenoids in maize grain; to understand its genetic basis, we conducted a joint linkage and genome-wide association study in the U.S. maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percent phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide the most comprehensive genome-level understanding of the genetic and molecular control of carotenoids in any plant system, and a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

genetics↗

bioRxiv2019

Genome-Wide Association Study and Pathway-Level Analysis of Kernel Color in Maize

Rapid development and adoption of biofortified, provitamin A-dense orange maize (Zea mays L.) varieties could be facilitated by a greater understanding of the natural variation underlying kernel color, including as relates to carotenoid biosynthesis and retention in maize grain. Greater abundance of carotenoids in maize kernels is generally accompanied by deeper orange color, useful for distinguishing provitamin A-dense varieties to consumers. While kernel color can be scored and selected with high-throughput, low-cost phenotypic methods within breeding selection programs, it remains to be well established as to what would be the logical genetic loci to target for selection for kernel color. We conducted a genome-wide association study of maize kernel color, as determined by colorimetry, in 1,651 yellow and orange inbreds from the Ames maize inbred panel. Associations were found with y1, encoding the first committed step in carotenoid biosynthesis, and with dxs2, which encodes the enzyme responsible for the first committed step in the biosynthesis of the isoprenoid precursors of carotenoids. These genes logically could contribute to overall carotenoid abundance and thus kernel color. The lcyE and zep1 genes, which can affect carotenoid composition, were also found to be associated with colorimeter values. A pathway-level analysis, focused on genes with a priori evidence of involvement in carotenoid biosynthesis and retention, revealed associations for dxs3 and dmes1, involved in isoprenoid biosynthesis; ps1 and vp5, within the core carotenoid pathway; and vp14, involved in cleavage of carotenoids. Collectively, these identified genes appear relevant to the accumulation of kernel color.

plant biology↗

bioRxiv2016

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci (QTL) and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles, however optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short-term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long-term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller, but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

genetics↗