A cryptic RNA-binding domain mediates Syncrip recognition and exosomal partitioning of miRNA targets

Exosomal miRNA transfer is a mechanism for cell-cell communication that is important in the immune response, in the functioning of the nervous system and in cancer. Syncrip/hnRNPQ is a highly conserved RNA-binding protein that mediates the exosomal partition of a set of miRNAs. Here, we report that Syncrip's amino-terminal domain, which was previously thought to mediate protein-protein interactions, is a cryptic, conserved and sequence-specific RNA-binding domain, designated NURR (N-terminal unit for RNA recognition). The NURR domain mediates the specific recognition of a short hEXO sequence defining Syncrip exosomal miRNA targets, and is coupled by a non-canonical structural element to Syncrip's RRM domains to achieve high-affinity miRNA binding. As a consequence, Syncrip-mediated selection of the target miRNAs implies both recognition of the hEXO sequence by the NURR domain and binding of the RRM domains 5′ to this sequence. This structural arrangement enables Syncrip-mediated selection of miRNAs with different seed sequences. © 2018 The Author(s)

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

Ageing as a price of cooperation and complexity: Self-organization of complex systems causes the ageing of constituent networks

The analysis of network topology and dynamics is increasingly used for the description of the structure, function and evolution of complex systems. Here we summarize key aspects of the evolvability and robustness of the hierarchical network-set of macromolecules, cells, organisms, and ecosystems. Listing the costs and benefits of cooperation as a necessary behaviour to build this network hierarchy, we outline the major hypothesis of the paper: the emergence of hierarchical complexity needs cooperation leading to the ageing of the constituent networks. Local cooperation in a stable environment may lead to over-optimization developing an ‘always-old’ network, which ages slowly, and dies in an apoptosis-like process. Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise ‘forever-young’ network in a necrosis-like process. Giving a number of examples we explain how local and global cooperation can both evoke and help successful ageing. Finally, we show how various forms of cooperation and consequent ageing emerge as key elements in all major steps of evolution from the formation of protocells to the establishment of the globalized, modern human society. Thus, ageing emerges as a price of complexity, which is going hand-in-hand with cooperation enhancing each other in a successful community

Quantitative tandem affinity purification, an effective tool to investigate protein complex composition in plant hormone signaling : strigolactones in the spotlight

Phytohormones tightly regulate plant growth by integrating changing environmental and developmental cues. Although the key players have been identified in many plant hormonal pathways, the molecular mechanisms and mode of action of perception and signaling remain incompletely resolved. Characterization of protein partners of known signaling components provides insight into the formed protein complexes, but, unless quantification is involved, does not deliver much, if any, information about the dynamics of the induced or disrupted protein complexes. Therefore, in proteomics research, the discovery of what actually triggers, regulates or interrupts the composition of protein complexes is gaining importance. Here, tandem affinity purification coupled to mass spectrometry (TAP-MS) is combined with label-free quantification (LFQ) to a highly valuable tool to detect physiologically relevant, dynamic protein-protein interactions in Arabidopsis thaliana cell cultures. To demonstrate its potential, we focus on the signaling pathway of one of the most recently discovered phytohormones, strigolactones

Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed

Computational Labeling, Partitioning, and Balancing of Molecular Networks

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional behaviors. We build three computational models to label, partition, and balance molecular networks using modern machine learning techniques. (1) Due to the incompleteness of protein functional annotation, we develop AptRank, an adaptive PageRank model for protein function prediction on bilayer networks. By integrating Gene Ontology (GO) hierarchy with protein-protein interaction network, our AptRank outperforms four state-of-the-art methods in a comprehensive evaluation using benchmark datasets. (2) We next extend our AptRank into a network partitioning method, BioSweeper, to identify functional network modules in which molecules share similar functions and also densely connect to each other. Compared to traditional network partitioning methods using only network connections, BioSweeper, which integrates the GO hierarchy, can automatically identify functionally enriched network modules. (3) Finally, we conduct a differential interaction analysis, namely difFBA, on protein-protein interaction networks by simulating protein fluxes using flux balance analysis (FBA). We test difFBA using quantitative proteomic data from colon cancer, and demonstrate that difFBA offers more insights into functional changes in molecular behavior than does protein quantity changes alone. We conclude that our integrative network model increases the observational dimensions of complex biological systems, and enables us to more deeply understand the causal relationships between genotypes and phenotypes

Investigating Hfq-Mrna Interactions In Bacteria

Regulatory RNAs (sRNAs) are essential for bacteria to thrive in diverse environments and they also play a key role in virulence [11]. Trans-sRNAs affect the stability and/or translation of their target mRNAs through complementary base-pairing. The base-pairing interaction is not perfect and requires the action of an RNA binding protein, Hfq. Hfq facilitates these RNA-RNA interactions by stabilizing duplex formation, aiding in structural rearrangements, increasing the rate of structural opening, and/or by increasing the rate of annealing [18-21]. Hfq has two well characterized binding surfaces: the proximal surface, which binds AU rich stretches typical of sRNAs, and the distal surface, which binds (ARN)x motifs typically found in target mRNAs [30, 33, 36]. Studies on Hfq-RNA interactions have focused largely on sRNAs until the more recent discovery of an (ARN)x motif within the 5\u27UTR of target mRNAs[36, 37]. The importance of this motif in facilitating Hfq-mRNA binding and its requirement for regulation of a couple well known target mRNAs led us to further characterize the motif in the work described in this thesis. We performed bioinformatic and in vitro analyses to investigate the prevalence, location, structural contexts, and Hfq-binding of (ARN)x motifs in known target mRNAs. We found that the known targets contain single stranded (ARN)x sequences in their 5\u27UTRs that bind to Hfq. Two predominant structural contexts of the single stranded (ARN)x motifs became clear: they were either flanked by stem loop structures or within a loop of an internal bulge, multi-branch junction or hairpin. The key features of the motifs were then used as a bioinformatic tool on a genome wide scale to identify mRNAs that might bind to Hfq. We found that 21% of mRNAs have a suitable (ARN)x motif and therefore likely bind to Hfq. Messages that bind to Hfq may be novel sRNA targets so we investigated this possibility using an in vivo reporter assay and found that 63% of the mRNAs tested are regulated by a specific sRNA. The novel targets are involved in pathways including iron salvage, biofilm formation, and amino acid metabolism. Overall, we defined key features of (ARN)x motifs and were able to use those to predict novel target mRNAs in E. coli. This approach is efficient, effective and adaptable other bacterial species