The distinct transcriptomes of slow and fast adult muscles are delineated by noncoding RNAs

Adult muscles have a vast adaptation capacity, enabling function switches in response to altered conditions. During intensive physical activity, disease, or aging, adult skeletal muscles change and adjust their functions. The competence to adjust varies among muscles. Muscle-specific molecular mechanisms in healthy and normal conditions could designate changes in physiologic and pathologic conditions. We generated deep mRNA-sequencing data in adult fast and slow mouse muscles, and applying paired analysis, we identified that the muscle-specific signatures are composed of half of the muscle transcriptome. The fast muscles showed a more compact gene network that is concordant with homogenous myofiber typing, compared with the pattern in the slow muscle. The muscle-specific mRNA landscape did not correlate with alternative spicing, alternative polyadenylation, or the expression of muscle transcription factor gene networks. However, we found significant correlation between the differentially expressed noncoding RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) and their target genes. More than 25% of the genes expressed in a muscle-specific fashion were found to be targets of muscle-specific miRNAs and lncRNAs. We suggest that muscle-specific miRNAs and lncRNAs contribute to the establishment of muscle-specific transcriptomes in adult muscles.-Raz, V., Riaz, M., Tatum, Z., Kielbasa, S. M., 't Hoen, P. A. C. The distinct transcriptomes of slow and fast adult muscles are delineated by noncoding RNAs.

Repeated FcεRI triggering reveals modified mast cell function related to chronic allergic responses in tissue

BACKGROUND

Activation of mast cells through FcεRI plays an important role in acute allergic reactions. However, little is known about the function of mast cells in patients with chronic allergic inflammation or the effect of repeated FcεRI triggering occurring in such responses.

OBJECTIVE

We aimed to identify changes in mast cell function after repeated FcεRI triggering and to correlate these changes to chronic allergic responses in tissue.

METHODS

Human cord blood-derived mast cells were treated for 2 weeks with anti-IgE. The function of naive or treated mast cells was analyzed by means of RNA sequencing, quantitative RT-PCR, flow cytometry, and functional assays. Protein secretion was measured with ELISAs and multiplex assays.

RESULTS

We observed several changes in mast cell function after repeated anti-IgE triggering. Although the acute response was dampened, we identified 289 genes significantly upregulated after repeated anti-IgE. Most of these genes (84%) were not upregulated after a single anti-IgE stimulus, indicating a significantly different response mode characterized by increased antigen presentation, response to bacteria, and chemotaxis. Changes in mast cell function were related to changes in expression of the transcription factors RXRA and BATF and others. Importantly, we found a substantial overlap between genes upregulated after repeated anti-IgE triggering and genes upregulated in tissue from patients with chronic allergy, in particular those of patients with chronic rhinosinusitis.

CONCLUSION

Our study provides evidence for intrinsic modulation of mast cell function on repeated FcεRI-mediated activation. The overlap with gene expression in tissues is suggestive of a direct link between repeated IgE-mediated activation of mast cells and chronic allergy.

The Implicitome: A Resource for Rationalizing Gene-Disease Associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.

Nanopublications for exposing experimental data in the life-sciences: a Huntington's Disease case study

Data from high throughput experiments often produce far more results than can ever appear in the main text or tables of a single research article. In these cases, the majority of new associations are often archived either as supplemental information in an arbitrary format or in publisher-independent databases that can be difficult to find. These data are not only lost from scientific discourse, but are also elusive to automated search, retrieval and processing. Here, we use the nanopublication model to make scientific assertions that were concluded from a workflow analysis of Huntington’s Disease data machine-readable, interoperable, and citable. We followed the nanopublication guidelines to semantically model our assertions as well as their provenance metadata and authorship. We demonstrate interoperability by linking nanopublication provenance to the Research Object model. These results indicate that nanopublications can provide an incentive for researchers to expose data that is interoperable and machine-readable for future use and preservation for which they can get credits for their effort. Nanopublications can have a leading role into hypotheses generation offering opportunities to produce large-scale data integration.

Preserving sequence annotations across reference sequences

BACKGROUND

Matching and comparing sequence annotations of different reference sequences is vital to genomics research, yet many annotation formats do not specify the reference sequence types or versions used. This makes the integration of annotations from different sources difficult and error prone.

RESULTS

As part of our effort to create linked data for interoperable sequence annotations, we present an RDF data model for sequence annotation using the ontological framework established by the OBO Foundry ontologies and the Basic Formal Ontology (BFO). We defined reference sequences as the common domain of integration for sequence annotations, and identified three semantic relationships between sequence annotations. In doing so, we created the Reference Sequence Annotation to compensate for gaps in the SO and in its mapping to BFO, particularly for annotations that refer to versions of consensus reference sequences. Moreover, we present three integration models for sequence annotations using different reference assemblies.

CONCLUSIONS

We demonstrated a working example of a sequence annotation instance, and how this instance can be linked to other annotations on different reference sequences. Sequence annotations in this format are semantically rich and can be integrated easily with different assemblies. We also identify other challenges of modeling reference sequences with the BFO.

Microattribution and nanopublication as means to incentivize the placement of human genome variation data into the public domain

The advances in bioinformatics required to annotate human genomic variants and to place them in public data repositories have not kept pace with their discovery. Moreover, a law of diminishing returns has begun to operate both in terms of data publication and submission. Although the continued deposition of such data in the public domain is essential to maximize both their scientific and clinical utility, rewards for data sharing are few, representing a serious practical impediment to data submission. To date, two main strategies have been adopted as a means to encourage the submission of human genomic variant data: (1) database journal linkups involving the affiliation of a scientific journal with a publicly available database and (2) microattribution, involving the unambiguous linkage of data to their contributors via a unique identifier. The latter could in principle lead to the establishment of a microcitation-tracking system that acknowledges individual endeavor and achievement. Both approaches could incentivize potential data contributors, thereby encouraging them to share their data with the scientific community. Here, we summarize and critically evaluate approaches that have been proposed to address current deficiencies in data attribution and discuss ways in which they could become more widely adopted as novel scientific publication modalities.