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Low field NMR has been successfully used for the evaluation of seed composition and quality, but largely only in crop species. We show here that 1.5T NMR provides a reliable means for analysing the seed lipid fraction present in a wide range of species, where both the seed size and lipid concentration differed by >10 fold. Little use of high field NMR has been made in seed research to date, even though it potentially offers many opportunities for studying seed development, metabolism and storage. Here we demonstrate how 17.5T and 20T NMR can be applied to image seed structure, and analyse lipid and metabolite distribution. We suggest that further technical developments in NMR/MRI will facilitate significant advances in our understanding of seed biology.
The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
The precise interplay between the mRNA codon and the tRNA anticodon is crucial for ensuring efficient and accurate translation by the ribosome. The insertion of RNA nucleobase derivatives in the mRNA allowed us to modulate the stability of the codon-anticodon interaction in the decoding site of bacterial and eukaryotic ribosomes, allowing an in-depth analysis of codon recognition. We found the hydrogen bond between the N1 of purines and the N3 of pyrimidines to be sufficient for decoding of the first two codon nucleotides, whereas adequate stacking between the RNA bases is critical at the wobble position. Inosine, found in eukaryotic mRNAs, is an important example of destabilization of the codon-anticodon interaction. Whereas single inosines are efficiently translated, multiple inosines, e.g., in the serotonin receptor 5-HT2C mRNA, inhibit translation. Thus, our results indicate that despite the robustness of the decoding process, its tolerance toward the weakening of codon-anticodon interactions is limited.