Computational prediction of RNA-protein interaction partners and interfaces

RNA-protein interactions play important roles in fundamental cellular processes involved in human diseases, viral replication and defense against pathogens in plants, animals and microbes. However, the detailed recognition mechanisms underlying these interactions are poorly understood. To gain a better understanding of the molecular recognition code for RNA-protein interactions, this dissertation has three related goals: i) to develop methods for predicting RNA-protein interaction partners; ii) to develop an approach for predicting interfacial residues in both the RNA and protein components of RNA-protein complexes; and iii) to develop computational tools and resources for investigating RNA-protein interactions. First, we present machine learning classifiers for predicting RNA-protein interaction partners. The classifiers use the amino acid composition of proteins and the ribonucleotide composition of RNAs as input to predict whether a given RNA-protein pair interacts. We show that protein and RNA sequences alone (i.e., in the absence of any structural information) contain enough signal to allow reliable prediction of interaction partners. Second, we present RPISeq, a webserver that predicts the interaction probabilities of input RNA-protein pairs, using the above-mentioned machine learning classifiers. A comprehensive database of RNA-protein interactions, RPIntDB, is integrated with the webserver to allow users to search for homologous proteins and their known interacting RNA partners. Finally, we perform an analysis of contiguous interfacial amino acids and ribonucleotides in RNA-protein complexes for which structures are known. We generate a dataset of bipartite RNA-protein motifs that can be used to predict interfacial residues in both the RNA and protein sequences of a given RNA-protein pair simultaneously. We show that taking binding partner information into account leads to higher precision in the prediction of RNA-binding residues in proteins. Taken together, these studies have increased our understanding of how RNA and proteins interact

A motif-based method for predicting interfacial residues in both the RNA and protein components of protein-RNA complexes

Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for “partner-specific” interfacial residue prediction. Given a specific protein-RNA pair, the goal is to simultaneously predict RNA binding residues in the protein sequence and protein-binding residues in the RNA sequence. In 5-fold cross validation experiments, our method, PS-PRIP, achieved 92% Specificity and 61% Sensitivity, with a Matthews correlation coefficient (MCC) of 0.58 in predicting RNA-binding sites in proteins. The method achieved 69% Specificity and 75% Sensitivity, but with a low MCC of 0.13 in predicting protein binding sites in RNAs. Similar performance results were obtained when PS-PRIP was tested on two independent “blind” datasets of experimentally validated protein- RNA interactions, suggesting the method should be widely applicable and valuable for identifying potential interfacial residues in protein-RNA complexes for which structural information is not available. The PS-PRIP webserver and datasets are available at: http://pridb.gdcb.iastate.edu/PSPRIP/

A Comparative Analysis of Methylome Profiles of Campylobacter jejuni Sheep Abortion Isolate and Gastroenteric Strains Using PacBio Data

Campylobacter jejuni is a leading cause of human gastrointestinal disease and small ruminant abortions in the United States. The recent emergence of a highly virulent, tetracycline-resistant C. jejuni subsp. jejunisheep abortion clone (clone SA) in the United States, and that strain\u27s association with human disease, has resulted in a heightened awareness of the zoonotic potential of this organism. Pacific Biosciences\u27 Single Molecule, Real-Time sequencing technology was used to explore the variation in the genome-wide methylation patterns of the abortifacient clone SA (IA3902) and phenotypically distinct gastrointestinal-specific C. jejuni strains (NCTC 11168 and 81-176). Several notable differences were discovered that distinguished the methylome of IA3902 from that of 11168 and 81-176: identification of motifs novel to IA3902, genome-specific hypo- and hypermethylated regions, strain level variability in genes methylated, and differences in the types of methylation motifs present in each strain. These observations suggest a possible role of methylation in the contrasting disease presentations of these three C. jejuni strains. In addition, the methylation profiles between IA3902 and a luxS mutant were explored to determine if variations in methylation patterns could be identified that might explain the role of LuxS-dependent methyl recycling in IA3902 abortifacient potential

Transcriptional analysis of phloem-associated cells of potato

Background Numerous signal molecules, including proteins and mRNAs, are transported through the architecture of plants via the vascular system. As the connection between leaves and other organs, the petiole and stem are especially important in their transport function, which is carried out by the phloem and xylem, especially by the sieve elements in the phloem system. The phloem is an important conduit for transporting photosynthate and signal molecules like metabolites, proteins, small RNAs, and full-length mRNAs. Phloem sap has been used as an unadulterated source to profile phloem proteins and RNAs, but unfortunately, pure phloem sap cannot be obtained in most plant species. Results Here we make use of laser capture microdissection (LCM) and RNA-seq for an in-depth transcriptional profile of phloem-associated cells of both petioles and stems of potato. To expedite our analysis, we have taken advantage of the potato genome that has recently been fully sequenced and annotated. Out of the 27 k transcripts assembled that we identified, approximately 15 k were present in phloem-associated cells of petiole and stem with greater than ten reads. Among these genes, roughly 10 k are affected by photoperiod. Several RNAs from this day length-regulated group are also abundant in phloem cells of petioles and encode for proteins involved in signaling or transcriptional control. Approximately 22 % of the transcripts in phloem cells contained at least one binding motif for Pumilio, Nova, or polypyrimidine tract-binding proteins in their downstream sequences. Highlighting the predominance of binding processes identified in the gene ontology analysis of active genes from phloem cells, 78 % of the 464 RNA-binding proteins present in the potato genome were detected in our phloem transcriptome. Conclusions As a reasonable alternative when phloem sap collection is not possible, LCM can be used to isolate RNA from specific cell types, and along with RNA-seq, provides practical access to expression profiles of phloem tissue. The combination of these techniques provides a useful approach to the study of phloem and a comprehensive picture of the mechanisms associated with long-distance signaling. The data presented here provide valuable insights into potentially novel phloem-mobile mRNAs and phloem-associated RNA-binding proteins

Transcriptome profiling of soybean (Glycine max) roots challenged with pathogenic and non-pathogenic isolates of Fusarium oxysporum.

Abstract Background: Fusarium oxysporum is one of the most common fungal pathogens causing soybean root rot and seedling blight in U.S.A. In a recent study, significant variation in aggressiveness was observed among isolates of F. oxysporum collected from roots in Iowa, ranging from highly pathogenic to weakly or non-pathogenic isolates. Results: We used RNA-seq analysis to investigate the molecular aspects of the interactions of a partially resistant soybean genotype with non-pathogenic/pathogenic isolates of F. oxysporum at 72 and 96 h post inoculation (hpi). Markedly different gene expression profiles were observed in response to the two isolates. A peak of highly differentially expressed genes (HDEGs) was triggered at 72 hpi in soybean roots and the number of HDEGs was about eight times higher in response to the pathogenic isolate compared to the non-pathogenic one (1,659 vs. 203 HDEGs, respectively). Furthermore, the magnitude of induction was much greater in response to the pathogenic isolate. This response included a stronger activation of defense-related genes, transcription factors, and genes involved in ethylene biosynthesis, secondary and sugar metabolism. Conclusions: The obtained data provide an important insight into the transcriptional responses of soybean-F. oxysporum interactions and illustrate the more drastic changes in the host transcriptome in response to the pathogenic isolate. These results may be useful in the developing new methods of broadening resistance of soybean to F. oxysporum, including the over-expression of key soybean genes

A Plasmodium‐like virulence effector of the soybean cyst nematode suppresses plant innate immunity

• Heterodera glycines, the soybean cyst nematode, delivers effector proteins into soybean roots to initiate and maintain an obligate parasitic relationship. HgGLAND18 encodes a candidate H. glycines effector and is expressed throughout the infection process. • We used a combination of molecular, genetic, bioinformatic and phylogenetic analyses to determine the role of HgGLAND18 during H. glycines infection. • HgGLAND18 is necessary for pathogenicity in compatible interactions with soybean. The encoded effector strongly suppresses both basal and hypersensitive cell death innate immune responses, and immunosuppression requires the presence and coordination between multiple protein domains. The N-terminal domain in HgGLAND18 contains unique sequence similarity to domains of an immunosuppressive effector of Plasmodium spp., the malaria parasites. The Plasmodium effector domains functionally complement the loss of the N-terminal domain from HgGLAND18. • In-depth sequence searches and phylogenetic analyses demonstrate convergent evolution between effectors from divergent parasites of plants and animals as the cause of sequence and functional similarity