symbol picture fatty acids
Image: Jan-Peter Kasper (University of Jena)
Room 3013
Inselplatz 5
07743 Jena
Room 2008
Inselplatz 5
07743 Jena
Room 2008
Inselplatz 5
07743 Jena
Biological systems can be characterised by their ability to store and transmit information. This is made possible, amongst other things, by the arrangement of nucleotides and amino acids, as well as by their composition within genomes and proteomes. More specifically, these properties influence how genetic information is encoded and passed on to the next generation, how genes are translated into proteins, and how molecular functions arise at various biological levels.
A central aspect of this research area is the question of how GC content shapes the statistical properties of genetic and proteomic sequences. In pseudo-random and natural sequences, the GC content influences the frequency of synonymous codons (i.e. triplets that code for the same amino acid), the occurrence of stop codons, the frequency and prediction of open reading frames, amino acid distributions, and the information potential stored in genetic codes.
Overall, this project examines how sequence structure shapes biological information at various molecular levels: from DNA, through the coding of triplets in the genetic code, to proteins. By applying concepts from statistics, bioinformatics and comparative approaches, it demonstrates how patterns in sequence composition can reveal evolutionary influences, structural relationships and biologically relevant signals. In a broader sense, this research area contributes to a sequence-centred understanding of all aspects of life in which information is shaped by both chemical conditions and evolutionary history.
The focus of the project lies on age-related protein damage that leads to aggregates. These cannot be degraded anymore and therefore accumulate until the cell suffers from major functional inhibitions and finally triggers apoptosis. The goal is to determine what are the most or also least susceptible targets in terms of oxydative damage. Also information from structure and prominent aggregate proteins will be included. Furthermore functional connections and domains will be examined in this context.
Antibiotic resistance genes (ARGs) and biosynthetic gene clusters (BGCs) play crucial roles in microbial communities by influencing antimicrobial production and adaptation to selective pressures. In this project, we investigate how environmental disturbance, microbial competition, and genetic mobility drive ARG-BGC coevolution across ecosystems. We will analyze metagenomic datasets from pristine and anthropogenically impacted sites using a custom Nextflow pipeline that integrates BGC detection tools (antiSMASH) with ARG detection frameworks (AMRFinderPlus, MEGARes, ResFinder). After validation, we'll expand to other datasets to assess genomic trait dissemination under different environmental conditions. Initially focused on metagenomics, we plan to eventually incorporate metatranscriptomics and metabolomics approaches. Our findings could improve antibiotic stewardship strategies and highlight the value of pristine habitats as reservoirs for next-generation antimicrobials.
The first part of this project consists of investigating the combinatorial possibilities of aliphatic amino acids and fatty acids. For this purpose molecular classes with certain constrictions are chosen because an exhausting listing is not feasible. Mathematical computations as for example recursions on the resulting number sequences are conducted. One possible question is how many unbranched, unmodificated fatty acids with n C-atoms principally can exist. This leads to the fibonacci numbers. Additionally literature research is conducted to verify which combinations are actually synthesized by real organisms. The second part of the project deals with the development of novel classification attempts for amino acids to improve or supplement existing approaches. As we hope, this leads to novel insights into key questions of biochemical evolution.
As in higher eukaryotes, fungal species can splice their mRNA in an alternative (differential) manner. But how wide-spread is this cellular process in the fungal kingdom and which processes of the microbial lifestyle are affected? Does alternative splicing facilitate multicellularity? Does it affect the switch from peaceful mutualism to pathogenic behavior?
To address these issues, we analyze RNA-Seq data of various fungi, such as Candida albicans, Aspergillus fumigatus, and Cryptococcus neoformans. Several analysis tools are applied in order to identify (unknown) alternative splicing events. Detected alternatively spliced genes are viewed in a cell biological context. We study conservation of these genes and investigate phylogenetic distribution.