Biology subjects

Kopp-Schneider, A.

Publications and source records attributed to Kopp-Schneider, A..

4 recordsLinked to original sources

bioRxiv2023

Low level of antioxidant capacity biomarkers but not target overexpression predicts vulnerability to ROS-inducing drugs

Despite a strong rationale for why cancer cells are susceptible to redox-targeting drugs, such drugs often face tumor resistance or dose-limiting toxicity in preclinical and clinical studies. An important reason is the lack of specific biomarkers to better select susceptible cancer entities and stratify patients. Using a large panel of lung cancer cell lines, we identified a set of "antioxidant-capacity" biomarkers (ACB), which were tightly repressed, partly by STAT3 and STAT5A/B in sensitive cells, rendering them susceptible to multiple redox-targeting and ferroptosis-inducing drugs. Contrary to expectation, constitutively low ACB expression was not associated with an increased steady state level of reactive oxygen species (ROS) but a high level of nitric oxide, which is required to sustain high replication rates. Using ACBs, we identified cancer entities with a high percentage of patients with favorable ACB expression pattern, making it likely that more responders to ROS-inducing drugs could be stratified for clinical trials.

cancer biology↗

bioRxiv2022

Analysis of codon usage and allele frequencies reveal the double-edged nature of cross-kingdom RNAi

BackgroundIn recent years, a new class of small 21- to 24-nt-(s)RNAs has been discovered from microbial pathogens that interfere with their hosts gene expression during infection, reducing the hosts defence in a process called cross-kingdom RNA interference (ckRNAi). According to this model, microbial sRNAs should exert selection pressure on plants so that gene sequences that reduce complementarity to sRNAs are preferred. In this paper, we test this consequence of the ckRNA model by analyzing changes to target sequences considering codon usage and allele frequencies in the model system Arabidopsis thaliana (At) - Hyaloperonospora arabidopsidis (Ha) and Hordeum vulgare (Hv) - Fusarium graminearum (Fg). In both pathosystems, some selected sRNA and their corresponding target have been described and experimentally validated, while the lengthy methodology prevents the analysis of all discovered sRNAs. To expand the understanding of ckRNAi, we apply a new in silico approach that integrates the majority of sRNAs. ResultsWe calculated the probability (PCHS) that synonymous host plant codons in a predicted sRNA target region would show the same or stronger complementarity as actually observed and compared this probability to sets of virtual analogous sRNAs. For the sets of Ha and Fg sRNAs, there was a significant difference in codon usage in their plant gene target regions (for Ha: PCHS 24.9% lower than in the virtual sets; for Fg: PCHS 19.3% lower than in the virtual sets), but unexpectedly for both sets of microbial sRNA we found a tendency towards codons with an unexpectedly high complementarity. To distinguish between complementarity caused by balancing sRNA-gene coevolution and directional selection we estimated Wrights F-statistic (FST), a measurement of population structure, in which positive deviations from the background indicate directional and negative deviations balancing selection at the respective loci. We found a negative correlation between PCHS and FST (p=0.03) in the At-Ha system indicating deviations from codon usage favoring complementarity are generally directionally selected. ConclusionThe directional selection of complementary codons in host plants suggests an evolutionary pressure to facilitate silencing by exogenous microbial sRNAs, which is not consistent with the anticipated biological role of pathogen sRNAs as exclusively effectors in cross-kingdom RNAi. To resolve this conflict, we propose an extended model in which microbial sRNAs are perceived by plants via RNA interference and, via coevolution, primarily help to fine-tune plant gene expression.

plant biology↗

bioRxiv2022

Competition for cysteine acylation by C16:0 and C18:0 derived lipids is a global phenomenon in the proteome

S-acylation is a reversible posttranslational protein modification consisting of attachment of a fatty acid to a cysteine via a thioester bond. Research over the last few years has shown that a variety of different fatty acids, such as C16:0, C18:0 or C18:1, are used in cells to S-acylate proteins. We recently showed that GNAI proteins can be acylated on a single residue, Cys3, with either C16:0 or C18:1 and that the relative proportion of acylation with these fatty acids depends on the level of the respective fatty acid in the cells environment. This has functional consequences for GNAI proteins, with the identity of the acylating fatty acid affecting the subcellular localization of GNAIs. Unclear is whether this competitive acylation is specific to GNAI proteins or a more general phenomenon in the proteome. We perform here a proteome screen to identify proteins acylated with different fatty acids. We identify 218 proteins acylated with C16:0 and 308 proteins acylated with C18-lipids, thereby uncovering novel targets of acylation. We find that most proteins that can be acylated by palmitic acid (C16:0) can also be acylated with C18-fatty acids. For proteins with more than one acylation site, we find that this competitive acylation occurs on each individual cysteine residue. This raises the possibility that the function of many different proteins can be regulated by the lipid environment via differential S-acylation.

molecular biology↗

bioRxiv2021

Spectral organ fingerprints for intraoperative tissue classification with hyperspectral imaging

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the methods current lack of robustness and generalizability. Specifically, it had been unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9,059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95 %). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decision making and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

bioinformatics↗