No abstract

Coevolution is an important biological process that shapes interacting proteins – may it be physically interacting proteins or consecutive enzymes in a metabolic pathway, such as the biosynthetic pathways for secondary metabolites. Previously, we developed FunOrder, a semi-automated method for the detection of co-evolved genes, and demonstrated that FunOrder can be used to identify essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. In the improved version, we use several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output to automatically detect co-evolved genes. Further, we replaced the BLAST tool with the DIAMOND tool as a prerequisite for using larger proteome databases. Potentially, FunOrder 2.0 may be used for the assessment of complete genomes, which has not been attempted yet. However, the introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity.

Background The yeast Komagataella phaffii (Pichia pastoris) is routinely used for heterologous protein expression and is suggested as a model organism for yeast. Despite its importance and application potential, no reference gene for transcript analysis via RT-qPCR assays has been evaluated to date. In this study, we searched publicly available RNASeq data for stably expressed genes to find potential reference genes for relative transcript analysis by RT-qPCR in K. phaffii. To evaluate the applicability of these genes, we used a diverse set of samples from three different strains and a broad range of cultivation conditions. The transcript levels of 9 genes were measured and compared using commonly applied bioinformatic tools. Results We could demonstrate that the often-used reference gene ACT1 is not very stably expressed and could identify two genes with outstandingly low transcript level fluctuations. Consequently, we suggest the two genes, RSC1, and TAF10 to be simultaneously used as reference genes in transcript analyses by RT-qPCR in K. phaffii in future RT-qPCR assays. Conclusion The usage of ACT1 as a reference gene in RT-qPCR analysis might lead to distorted results due to the instability of its transcript levels. In this study, we evaluated the transcript levels of several genes and found RSC1 and TAF10 to be extremely stable. Using these genes holds the promise for reliable RT-qPCR results.