Computational protein design suggests that human PCNA‐partner interactions are not optimized for affinity

Increasing the affinity of binding proteins is invaluable for basic and applied biological research. Currently, directed protein evolution experiments are the main approach for generating such proteins through the construction and screening of large mutant libraries. Proliferating cell nuclear antigen (PCNA) is an essential hub protein that interacts with many different partners to tightly regulate DNA replication and repair in all eukaryotes. Here, we used computational design to generate human PCNA mutants with enhanced affinity for several different partners. We identified double mutations in PCNA, outside the main partner binding site, that were predicted to increase PCNA‐partner binding affinities compared to the wild‐type protein by forming additional hydrophobic interactions with conserved residues in the PCNA partners. Affinity increases were experimentally validated with four different PCNA partners, demonstrating that computational design can reveal unexpected regions where affinity enhancements in natural systems are possible. The designed PCNA mutants can be used as a valuable tool for further examination of the regulation of PCNA‐partner interactions during DNA replication and repair both in vitro and in vivo. More broadly, the ability to engineer affinity increases toward several PCNA partners suggests that interaction affinity is not an evolutionarily optimized trait of this system. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Proliferating cell nuclear antigen is a novel inhibitory ligand for the natural cytotoxicity receptor NKp44

NK cells play an important role in the early immune response to cancer. The NKp44 activating receptor is the only natural cytotoxicity receptor that is expressed exclusively by primate NK cells, yet its cellular ligands remain largely unknown. Proliferating cell nuclear Ag (PCNA) is overexpressed in cancer cells. In this study, we show that the NKp44 receptor recognizes PCNA. Their interaction inhibits NK cell function through NKp44/ITIM. The physical interaction of NKp44 and PCNA is enabled by recruitment of target cell PCNA to the NK immunological synapse. We demonstrate that PCNA promotes cancer survival by immune evasion through inhibition of NKp44-mediated NK cell attack.     

Asymmetric adaptation to indolic and aliphatic glucosinolates in the B and Q sibling species of Bemisia tabaci (Hemiptera: Aleyrodidae)

The role glucosinolates play in defending plants against phloem feeders such as aphids and whiteflies is currently not clear as these herbivores may avoid bringing glucosinolates from the phloem sap into contact with myrosinase enzymes. Here, we investigated the effects of high levels of aliphatic and indolic glucosinolates on life history traits and detoxification gene expression in two sibling species, B and Q, of the whitefly Bemisia tabaci. High levels of aliphatic glucosinolates decreased the average oviposition rate of both species and reduced the survival and developmental rate of Q nymphs. High levels of indolic glucosinolates decreased the oviposition rate and survival of nymphal stages of the B species and the developmental rate of both species. Molecular analyses revealed two major asymmetries between the B and Q species. First, specific GST genes (BtGST1 and BtGST2) were significantly induced during exposure to indolic glucosinolates only in Q. This may reflect the genes putative involvement in indolic glucosinolates detoxification and explain the species' good performance on plants accumulating indolic glucosinolates. Second, the constitutive expression of eight of the 10 detoxification genes analysed was higher in the Q species than in the B species. Interestingly, four of these genes were induced in B in response to high levels of glucosinolates. It seems, therefore, that the B and Q species differ in their 'optimal defence strategy'. B utilizes inducible defences that are profitable if the probability of experiencing the stress is small and its severity is low, while Q invests significant resources in being always 'ready' for a challenge.     

In vitro microbiotic fermentation causes an extensive metabolite turnover of rye bran phytochemicals

The human gut hosts a microbial community which actively contributes to the host metabolism and has thus remarkable effect on our health. Intestinal microbiota is known to interact remarkably with the dietary constituents entering the colon, causing major metabolic conversions prior to absorption. To investigate the effect of microbial metabolism on the phytochemical pool of rye bran, we applied an in vitro simulated colonic fermentation where samples were collected with intervals and analyzed by LC-MS based non-targeted metabolite profiling. The analyses revealed extensive metabolic turnover on the phytochemical composition of the bran samples, and showed effects on all the metabolite classes detected. Furthermore, the majority of the metabolites, both the precursors and the conversion products, remained unidentified indicating that there are numerous yet unknown phytochemicals, which can potentially affect on our health. This underlines the importance of comprehensive profiling assays and subsequent detailed molecular investigations in order to clarify the effect of microbiota on phytochemicals present in our everyday diet.     

<Emphasis Type="Italic">pc8.1</Emphasis>, a major QTL for pigment content in pepper fruit,is associated with variation in plastid compartment size

Studies on the genetic control of pigment content in pepper fruit have focused mainly on monogenic mutations leading to changes in fruit color. In addition to the qualitative variation in fruit color, quantitative variation in pigment content and color intensity exists in pepper giving rise to a range of color intensities. However, the genetic basis for this variation is poorly understood, hindering the development of peppers that are rich in these beneficial compounds. In this paper, quantitative variation in pigment content was studied in a cross between a dark-green Capsicum annuum pepper and a light-green C. chinense pepper. Two major pigment content QTLs that control chlorophyll content were identified, pc8.1 and pc10.1. The major QTL pc8.1, also affected carotenoid content in the ripe fruit. However, additional analyses in subsequent generations did not reveal a consistent effect of this QTL on carotenoid content in ripe fruit. Confocal microscopy analyses of green immature fruits of the parents and of near-isogenic lines for pc8.1 indicated that the QTL exerts its effect via increasing chloroplast compartment size in the dark-green genotypes, predominantly in a fruit-specific manner. Metabolic analyses indicated that in addition to chlorophyll, chloroplast-associated tocopherols and carotenoids are also elevated. Future identification of the genes controlling pigment content QTLs in pepper will provide a better understanding of this important trait and new opportunities for breeding peppers and other Solanaceae species with enhanced nutritional value.     

Computational identification of three-way junctions in folded RNAs: a case study in Arabidopsis

Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.     

Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy

Background

Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers.

Principal Findings

The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system.

Conclusion

The combined EuResist prediction engine is freely available at http://engine.euresist.org.