The degradation of p53 and its major E3 ligase Mdm2 is differentially dependent on the proteasomal ubiquitin receptor S5a.

p53 and its major E3 ligase Mdm2 are both ubiquitinated and targeted to the proteasome for degradation. Despite the importance of this in regulating the p53 pathway, little is known about the mechanisms of proteasomal recognition of ubiquitinated p53 and Mdm2. In this study, we show that knockdown of the proteasomal ubiquitin receptor S5a/PSMD4/Rpn10 inhibits p53 protein degradation and results in the accumulation of ubiquitinated p53. Overexpression of a dominant-negative deletion of S5a lacking its ubiquitin-interacting motifs (UIM)s, but which can be incorporated into the proteasome, also causes the stabilization of p53. Furthermore, small-interferring RNA (siRNA) rescue experiments confirm that the UIMs of S5a are required for the maintenance of low p53 levels. These observations indicate that S5a participates in the recognition of ubiquitinated p53 by the proteasome. In contrast, targeting S5a has no effect on the rate of degradation of Mdm2, indicating that proteasomal recognition of Mdm2 can be mediated by an S5a-independent pathway. S5a knockdown results in an increase in the transcriptional activity of p53. The selective stabilization of p53 and not Mdm2 provides a mechanism for p53 activation. Depletion of S5a causes a p53-dependent decrease in cell proliferation, demonstrating that p53 can have a dominant role in the response to targeting S5a. This study provides evidence for alternative pathways of proteasomal recognition of p53 and Mdm2. Differences in recognition by the proteasome could provide a means to modulate the relative stability of p53 and Mdm2 in response to cellular signals. In addition, they could be exploited for p53-activating therapies. This work shows that the degradation of proteins by the proteasome can be selectively dependent on S5a in human cells, and that this selectivity can extend to an E3 ubiquitin ligase and its substrate

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input representations into richer internal representations that are effective for learning. However, these internal representations are sub-symbolic and difficult to explain. In many scientific problems explainable models are required, and the input data is semantically complex and unsuitable for DNNs. This is true in the fundamental problem of understanding the mechanism of cancer drugs, which requires complex background knowledge about the functions of genes/proteins, their cells, and the molecular structure of the drugs. This background knowledge cannot be compactly expressed propositionally, and requires at least the expressive power of Datalog. Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. These new descriptors are effective: adding them to standard propositional learning methods significantly improves prediction accuracy. They are also explainable, and add to our understanding of cancer. Our approach can readily be expanded to include other complex forms of background knowledge, and combines the generality of relational learning with the efficiency of standard propositional learning

Integrative Genomics Identifies the Molecular Basis of Resistance to Azacitidine Therapy in Myelodysplastic Syndromes

© 2017 The Author(s) Myelodysplastic syndromes and chronic myelomonocytic leukemia are blood disorders characterized by ineffective hematopoiesis and progressive marrow failure that can transform into acute leukemia. The DNA methyltransferase inhibitor 5-azacytidine (AZA) is the most effective pharmacological option, but only ∼50% of patients respond. A response only manifests after many months of treatment and is transient. The reasons underlying AZA resistance are unknown, and few alternatives exist for non-responders. Here, we show that AZA responders have more hematopoietic progenitor cells (HPCs) in the cell cycle. Non-responder HPC quiescence is mediated by integrin α5 (ITGA5) signaling and their hematopoietic potential improved by combining AZA with an ITGA5 inhibitor. AZA response is associated with the induction of an inflammatory response in HPCs in vivo. By molecular bar coding and tracking individual clones, we found that, although AZA alters the sub-clonal contribution to different lineages, founder clones are not eliminated and continue to drive hematopoiesis even in complete responders

Development and validation of an improved algorithm for overlaying flexible molecules

A program for overlaying multiple flexible molecules has been developed. Candidate overlays are generated by a novel fingerprint algorithm, scored on three objective functions (union volume, hydrogen-bond match, and hydrophobic match), and ranked by constrained Pareto ranking. A diverse subset of the best ranked solutions is chosen using an overlay-dissimilarity metric. If necessary, the solutions can be optimised. A multi-objective genetic algorithm can be used to find additional overlays with a given mapping of chemical features but different ligand conformations. The fingerprint algorithm may also be used to produce constrained overlays, in which user-specified chemical groups are forced to be superimposed. The program has been tested on several sets of ligands, for each of which the true overlay is known from protein–ligand crystal structures. Both objective and subjective success criteria indicate that good results are obtained on the majority of these sets

Transcultural Diabetes Nutrition Therapy Algorithm: The Asian Indian Application

India and other countries in Asia are experiencing rapidly escalating epidemics of type 2 diabetes (T2D) and cardiovascular disease. The dramatic rise in the prevalence of these illnesses has been attributed to rapid changes in demographic, socioeconomic, and nutritional factors. The rapid transition in dietary patterns in India—coupled with a sedentary lifestyle and specific socioeconomic pressures—has led to an increase in obesity and other diet-related noncommunicable diseases. Studies have shown that nutritional interventions significantly enhance metabolic control and weight loss. Current clinical practice guidelines (CPGs) are not portable to diverse cultures, constraining the applicability of this type of practical educational instrument. Therefore, a transcultural Diabetes Nutrition Algorithm (tDNA) was developed and then customized per regional variations in India. The resultant India-specific tDNA reflects differences in epidemiologic, physiologic, and nutritional aspects of disease, anthropometric cutoff points, and lifestyle interventions unique to this region of the world. Specific features of this transculturalization process for India include characteristics of a transitional economy with a persistently high poverty rate in a majority of people; higher percentage of body fat and lower muscle mass for a given body mass index; higher rate of sedentary lifestyle; elements of the thrifty phenotype; impact of festivals and holidays on adherence with clinic appointments; and the role of a systems or holistic approach to the problem that must involve politics, policy, and government. This Asian Indian tDNA promises to help guide physicians in the management of prediabetes and T2D in India in a more structured, systematic, and effective way compared with previous methods and currently available CPGs

Metabolic Adaptation in Transplastomic Plants Massively Accumulating Recombinant Proteins

BACKGROUND: Recombinant chloroplasts are endowed with an astonishing capacity to accumulate foreign proteins. However, knowledge about the impact on resident proteins of such high levels of recombinant protein accumulation is lacking. METHODOLOGY/PRINCIPAL FINDINGS: Here we used proteomics to characterize tobacco (Nicotiana tabacum) plastid transformants massively accumulating a p-hydroxyphenyl pyruvate dioxygenase (HPPD) or a green fluorescent protein (GFP). While under the conditions used no obvious modifications in plant phenotype could be observed, these proteins accumulated to even higher levels than ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco), the most abundant protein on the planet. This accumulation occurred at the expense of a limited number of leaf proteins including Rubisco. In particular, enzymes involved in CO(2) metabolism such as nuclear-encoded plastidial Calvin cycle enzymes and mitochondrial glycine decarboxylase were found to adjust their accumulation level to these novel physiological conditions. CONCLUSIONS/SIGNIFICANCE: The results document how protein synthetic capacity is limited in plant cells. They may provide new avenues to evaluate possible bottlenecks in recombinant protein technology and to maintain plant fitness in future studies aiming at producing recombinant proteins of interest through chloroplast transformation

C-terminal UBA domains protect ubiquitin receptors by preventing initiation of protein degradation

The ubiquitin receptors Rad23 and Dsk2 deliver polyubiquitylated substrates to the proteasome for destruction. The C-terminal ubiquitin-associated (UBA) domain of Rad23 functions as a cis-acting stabilization signal that protects this protein from proteasomal degradation. Here, we provide evidence that the C-terminal UBA domains guard ubiquitin receptors from destruction by preventing initiation of degradation at the proteasome. We show that introduction of unstructured polypeptides that are sufficiently long to function as initiation sites for degradation abrogates the protective effect of UBA domains. Vice versa, degradation of substrates that contain an unstructured extension can be attenuated by the introduction of C-terminal UBA domains. Our study gains insight into the molecular mechanism responsible for the protective effect of UBA domains and explains how ubiquitin receptors can shuttle substrates to the proteasome without themselves becoming subject to proteasomal degradation

Specific Binding of the Pathogenic Prion Isoform: Development and Characterization of a Humanized Single-Chain Variable Antibody Fragment

Murine monoclonal antibody V5B2 which specifically recognizes the pathogenic form of the prion protein represents a potentially valuable tool in diagnostics or therapy of prion diseases. As murine antibodies elicit immune response in human, only modified forms can be used for therapeutic applications. We humanized a single-chain V5B2 antibody using variable domain resurfacing approach guided by computer modelling. Design based on sequence alignments and computer modelling resulted in a humanized version bearing 13 mutations compared to initial murine scFv. The humanized scFv was expressed in a dedicated bacterial system and purified by metal-affinity chromatography. Unaltered binding affinity to the original antigen was demonstrated by ELISA and maintained binding specificity was proved by Western blotting and immunohistochemistry. Since monoclonal antibodies against prion protein can antagonize prion propagation, humanized scFv specific for the pathogenic form of the prion protein might become a potential therapeutic reagent