Comparative proteomics analysis provides new insights into the haustorium development of Taxillus chinensis (DC.) Danser

Background Loranthus (Taxillus chinensis) is an important medicinal and parasitic plant that attacks other plants for living. To reveal the mechanisms of haustorium development, we employed an iTRAQ proteomics-based approach to identify differentially abundant proteins (DAPs) of fresh seeds (CK), baby (FB), and adult haustoria (FD). Results A total of 563 and 785 DAPs were successfully quantified in the early/later developmental stage, respectively. Pathway enrichment analysis indicated that the DAPs mainly associated with metabolic pathways, ribosome, phenylpropanoid biosynthesis and photosynthesis. In the meantime, DAPs associated with phytohormone signaling pathway changed markedly. Furthermore, we evaluated the contents change of phytohormone during the haustoria development. These results indicated that phytohormone is very important for haustorium development. qRT-PCR validation showed that the mRNA expression levels were consistent with the protein variation, suggesting that our result were reliable. Conclusions To the best of our knowledge, this is the first haustoria proteomes of loranthus, and our findings will improve our understanding of the molecular mechanism of haustoria development.

A Novel Pinkish-White Flower Color Variant Is Caused by a New Allele of Flower Color Gene W1 in Wild Soybean (Glycine soja)

The enzyme flavonoid 3′,5′-hydroxylase (F3′5′H) plays an important role in producing anthocyanin pigments in soybean. Loss of function of the W1 locus encoding F3′5′H always produces white flowers. However, few color variations have been reported in wild soybean. In the present study, we isolated a new color variant of wild soybean accession (IT261811) with pinkish-white flowers. We found that the flower’s pinkish-white color is caused by w1-s3, a single recessive allele of W1. The SNP detected in the mutant caused amino acid substitution (A304S) in a highly conserved SRS4 domain of F3′5′H proteins. On the basis of the results of the protein variation effect analyzer (PROVEAN) tool, we suggest that this mutation may lead to hypofunctional F3′5′H activity rather than non-functional activity, which thereby results in its pinkish-white color.

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation

A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A- > B) and its reverse (B- > A) must have the opposite value of the free energy difference (ΔΔGAB = − ΔΔGBA). In this letter, we complement the Fang’s paper presenting a more general view of the problem. In particular, a machine learning-based method, published in 2015 (INPS), addressed the bias issue directly. We include the analysis of the missing method, showing that INPS is nearly insensitive to the addressed problem.

Destabilizing mutations encode nongenetic variation that drives evolutionary innovation

Evolutionary innovations are often achieved by repurposing existing genes to perform new functions; however, the mechanisms enabling the transition from old to new remain controversial. We identified mutations in bacteriophage λ’s host-recognition gene J that confer enhanced adsorption to λ’s native receptor, LamB, and the ability to access a new receptor, OmpF. The mutations destabilize λ particles and cause conformational bistability of J, which yields progeny of multiple phenotypic forms, each proficient at different receptors. This work provides an example of how nongenetic protein variation can catalyze an evolutionary innovation. We propose that cases where a single genotype can manifest as multiple phenotypes may be more common than previously expected and offer a general mechanism for evolutionary innovation.