Dem IPB wird erneut ein beispielhaftes Handeln im Sinne einer chancengleichheitsorientierten Personal- und Organisationspolitik bescheinigt. Das Institut erhält zum 6. Mal in Folge das TOTAL E-QUALITY…
Die Plant Science Student Conference (PSSC) wird seit 20 Jahren im jährlichen Wechsel von Studierenden der beiden Leibniz-Institute IPK und IPB organisiert. Im Interview erläutern Christina Wäsch…
The HD-ZIP class I transcription factor, HvHOX1 (Homeobox 1) or VRS1 (Vulgare Row-type Spike 1 or Six-rowed Spike 1), regulates lateral spikelet fertility in barley (Hordeum vulgare L.). It was shown that HvHOX1 has a high expression only in lateral spikelets, while its paralog HvHOX2 was found to be expressed in different plant organs. Yet, the mechanistic function of HvHOX1 and HvHOX2 during spikelet development is still fragmentary. Here, we show that compared to HvHOX1, HvHOX2 is more highly conserved across different barley genotypes and Hordeum species, hinting at a possibly vital but still unclarified biological role. Using bimolecular fluorescence complementation, DNA-binding, and transactivation assays, we validate that HvHOX1 and HvHOX2 are bona fide transcriptional activators that may potentially heterodimerize. Accordingly, both genes exhibit similar spatiotemporal expression patterns during spike development and growth, albeit their mRNA levels differ quantitatively. We show that HvHOX1 delays the lateral spikelet meristem differentiation and affects fertility by aborting the reproductive organs. Interestingly, the ancestral relationship of these genes inferred from their co-expressed gene networks suggested that HvHOX1 and HvHOX2 might play a similar role during barley spikelet development. However, CRISPR-derived mutants of HvHOX1 and HvHOX2 demonstrated the suppressive role of HvHOX1 on lateral spikelets, while the loss of HvHOX2 does not influence spikelet development. Collectively, our study shows that through the suppression of reproductive organs, lateral spikelet fertility is regulated by HvHOX1, whereas HvHOX2 is dispensable for spikelet development in barley.
Publikation
Saoud, M.; Grau, J.; Rennert, R.; Mueller, T.; Yousefi, M.; Davari, M. D.; Hause, B.; Csuk, R.; Rashan, L.; Grosse, I.; Tissier, A.; Wessjohann, L. A.; Balcke, G. U.;Advancing anticancer drug discovery: leveraging metabolomics and machine learning for mode of action prediction by pattern recognitionAdvanced Science112404085(2024)DOI: 10.1002/advs.202404085
A bottleneck in the development of new anti‐cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti‐proliferative drug candidates, focusing on human prostate cancer cells (PC‐3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC‐MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC‐3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing\'s sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.