Identifying pairwise RNA-RNA interactions is key to understanding how RNAs fold and interact with other RNAs inside the cell. We present a high-throughput approach, sequencing of psoralen crosslinked, ligated, and selected hybrids (SPLASH), that maps pairwise RNA interactions in vivo with high sensitivity and specificity, genome-wide. Applying SPLASH to human and yeast transcriptomes revealed the diversity and dynamics of thousands of long-range intra- and intermolecular RNA-RNA interactions. Our analysis highlighted key structural features of RNA classes, including the modular organization of mRNAs, its impact on translation and decay, and the enrichment of long-range interactions in noncoding RNAs. Additionally, intermolecular mRNA interactions were organized into network clusters and were remodeled during cellular differentiation. We also identified hundreds of known and new snoRNA-rRNA binding sites, expanding our knowledge of rRNA biogenesis. These results highlight the underexplored complexity of RNA interactomes and pave the way to better understanding how RNA organization impacts biology.

We have comprehensively mapped long-range associations between chromosomal regions throughout the fission yeast genome using the latest genomics approach that combines next generation sequencing and chromosome conformation capture (3C). Our relatively simple approach, referred to as enrichment of ligation products (ELP), involves digestion of the 3C sample with a 4 bp cutter and self-ligation, achieving a resolution of 20 kb. It recaptures previously characterized genome organizations and also identifies new and important interactions. We have modeled the 3D structure of the entire fission yeast genome and have explored the functional relationships between the global genome organization and transcriptional regulation. We find significant associations among highly transcribed genes. Moreover, we demonstrate that genes co-regulated during the cell cycle tend to associate with one another when activated. Remarkably, functionally defined genes derived from particular gene ontology groups tend to associate in a statistically significant manner. Those significantly associating genes frequently contain the same DNA motifs at their promoter regions, suggesting that potential transcription factors binding to these motifs are involved in defining the associations among those genes. Our study suggests the presence of a global genome organization in fission yeast that is functionally similar to the recently proposed mammalian transcription factory.

Mesenchymal stem cell (MSC), a widely used adult stem cell candidate for regenerative medicine, has been shown to exert some of its therapeutic effects through the secretion of extracellular vesicles (EVs). These homogenously sized EVs of 100–150 ηm exhibited many exosome-like biophysical and biochemical properties and carry both proteins and RNAs. Recently, exosome-associated proteins in this MSC EV preparation were found to segregate primarily to those EVs that bind cholera toxin B chain (CTB), a GM1 ganglioside-specific ligand, and pulse-chase experiments demonstrated that these EVs have endosomal origin and carried many of the exosome-associated markers. Here, we report that only a fraction of the MSC EV proteome was found in CTB-bound EVs. Using Annexin V (AV) and Shiga toxin B subunit (ST) with affinities for phosphatidylserine and globotriaosylceramide, respectively, AV- and a ST-binding EV were identified. CTB-, AV- and ST–binding EVs all carried actin. However, the AV-binding EVs carried low or undetectable levels of the exosome-associated proteins. Only the ST-binding EVs carried RNA and EDA-containing fibronectin. Proteins in AV-binding EVs were also different from those released by apoptotic MSCs. CTB- and AV-binding activities were localized to the plasma membrane and cytoplasm of MSCs, while ST-binding activity was localized to the nucleus. Together, this study demonstrates that cells secrete many types of EVs. Specifically, MSCs secrete at least 3 types. They can be differentially isolated based on their affinities for membrane lipid-binding ligands. As the subcellular sites of the binding activities of these ligands and cargo load are different for each EV type, they are likely to have a different biogenesis pathway and possibly different functions.

Background Peanut-allergic subjects have highly stable pathologic antibody repertoires to the immunodominant B cell epitopes of the major peanut allergens Ara h 1-3. Objective We used a peptide microarray technique to analyze the effect of treatment with peanut oral immunotherapy (OIT) on such repertoires. Methods Measurements of total peanut-specific IgE (psIgE) and psIgG4 were made with CAP-FEIA. We analyzed sera from 22 OIT subjects and 6 controls and measured serum specific IgE and IgG4 binding to epitopes of Ara h 1-3 using a high-throughput peptide microarray technique. Antibody affinity was measured using a competitive peptide microarray as previously described. Results At baseline, psIgE and psIgG4 diversity were similar between subjects and controls, and there was broad variation in epitope recognition. After a median 41 months of OIT, polyclonal psIgG4 increased from a median 0.3 mcg/mL (IQR 0.1-0.43) at baseline to 10.5 mcg/mL (3.95-45.48) (p<0.0001) and included de novo specificities. PsIgE was reduced from a median baseline of 85.45 kUA/L (23.05-101.0) to 7.75 kUA/L (2.58-30.55) (p<0.0001). Affinity was unaffected. Although the psIgE repertoire contracted in most OIT-treated subjects, several subjects generated new IgE specificities even as the total psIgE decreased. Global epitope-specific shifts from IgE to IgG4 binding occurred, including at an informative epitope of Ara h 2. Conclusion OIT differentially alters Ara h 1-3 binding patterns. These changes are variable between subjects, not observed in controls, and include a progressive polyclonal increase in IgG4, with concurrent reduction in IgE amount and diversity.

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Background The peptide microarray is a novel assay which facilitates high-throughput screening of peptides with a small quantity of sample. Objective We sought to use overlapping peptides of milk allergenic proteins as a model system to establish a reliable and sensitive peptide microarray-based immunoassay for large scale epitope mapping of food allergens. Methods A milk peptide microarray was developed using commercially synthesized peptides (20-mers, 3 offset) covering the primary sequences of αs1-, αs2-, β-, and κ-caseins, and β-lactoglobulin. Conditions for printing and immunolabeling were optimized using a serum pool of five milk-allergic patients. Reproducibility of the milk peptide microarray was evaluated using replicate arrays immunolabeled with the serum pool, whereas specificity and sensitivity were assessed using serial dilution of the serum pool and a peptide inhibition assay. Results Our results show that epitopes identified by the peptide microarray were mostly consistent with those identified previously by SPOT membrane technology, but with specific binding to a few newly identified epitopes of milk allergens. Data from replicate arrays were reproducible (R≥0.92) regardless of printing lots, immunolabeling and serum pool batches. Using the serially diluted serum pool, we confirmed that IgE antibody binding detected in the array was specific. Peptide inhibition of IgE binding to the same peptide and overlapping peptides further confirmed the specificity of the array. Conclusions A reliable peptide microarray was established for large scale IgE epitope mapping of milk allergens and this robust technology could be applied for epitope mapping of other food allergens.

Patients with positive shrimp challenges present in general more intense and diverse epitope recognition to all four shrimp allergens. IgE antibodies to these shrimp epitopes could be used as biomarkers for prediction of clinical reactivity in subjects with sensitization to shrimp. Patients with positive shrimp challenges show more intense sensitization and more diverse epitope recognition. Several IgE-binding shrimp epitopes could be used as biomarkers for predicting clinical reactivity in subjects with sensitization to shrimp.

Background Peanut allergy is relatively common, typically permanent, and often severe. Double-blind, placebo-controlled food challenge is considered the gold standard for the diagnosis of food allergy–related disorders. However, the complexity and potential of double-blind, placebo-controlled food challenge to cause life-threatening allergic reactions affects its clinical application. A laboratory test that could accurately diagnose symptomatic peanut allergy would greatly facilitate clinical practice. Objective We sought to develop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using peptide microarray immunoassays and bioinformatic methods. Methods Microarray immunoassays were performed by using the sera from 62 patients (31 with symptomatic peanut allergy and 31 who had outgrown their peanut allergy or were sensitized but were clinically tolerant to peanut). Specific IgE and IgG4 binding to 419 overlapping peptides (15 mers, 3 offset) covering the amino acid sequences of Ara h 1, Ara h 2, and Ara h 3 were measured by using a peptide microarray immunoassay. Bioinformatic methods were applied for data analysis. Results Individuals with peanut allergy showed significantly greater IgE binding and broader epitope diversity than did peanut-tolerant individuals. No significant difference in IgG4 binding was found between groups. By using machine learning methods, 4 peptide biomarkers were identified and prediction models that can predict the outcome of double-blind, placebo-controlled food challenges with high accuracy were developed by using a combination of the biomarkers. Conclusions In this study, we developed a novel diagnostic approach that can predict peanut allergy with high accuracy by combining the results of a peptide microarray immunoassay and bioinformatic methods. Further studies are needed to validate the efficacy of this assay in clinical practice.