A population-based statistical approach identifies parameters characteristic of human microRNA-mRNA interactions

BACKGROUND: MicroRNAs are ~17–24 nt. noncoding RNAs found in all eukaryotes that degrade messenger RNAs via RNA interference (if they bind in a perfect or near-perfect complementarity to the target mRNA), or arrest translation (if the binding is imperfect). Several microRNA targets have been identified in lower organisms, but only one mammalian microRNA target has yet been validated experimentally. RESULTS: We carried out a population-wide statistical analysis of how human microRNAs interact complementarily with human mRNAs, looking for characteristics that differ significantly as compared with scrambled control sequences. These characteristics were used to identify a set of 71 outlier mRNAs unlikely to have been hit by chance. Unlike the case in C. elegans and Drosophila, many human microRNAs exhibited long exact matches (10 or more bases in a row), up to and including perfect target complementarity. Human microRNAs hit outlier mRNAs within the protein coding region about 2/3 of the time. And, the stretches of perfect complementarity within microRNA hits onto outlier mRNAs were not biased near the 5'-end of the microRNA. In several cases, an individual microRNA hit multiple mRNAs that belonged to the same functional class. CONCLUSIONS: The analysis supports the notion that sequence complementarity is the basis by which microRNAs recognize their biological targets, but raises the possibility that human microRNA-mRNA target interactions follow different rules than have been previously characterized in Drosophila and C. elegans

Introduction to a special series: What Makes Man Human

One of the most pressing and timely scientific questions concerns the evolution of man. In 1970, Karl Pribram delivered the James Arthur Lecture at the American Museum of Natural History in New York City. His lecture, "What Makes Man Human," was one of the most eloquent and brilliant syntheses of this problem ever made. The Journal is proud to publish this Lecture for the first time in an open access format that will make its insights available widely to a new generation of students and investigators. Accompanying the lecture is a new commentary written by Prof. Pribram, and four additional commentaries from prominent investigators who were invited to consider the question from their own perspectives. Together, these articles provide a scholarly, yet accessible, snapshot of different approaches to the study of human evolution in 2006

Exosomal transfer of proteins and RNAs at synapses in the nervous system

<p>Abstract</p> <p>Background</p> <p>Many cell types have been reported to secrete small vesicles called exosomes, that are derived from multivesicular bodies and that can also form from endocytic-like lipid raft domains of the plasma membrane. Secretory exosomes contain a characteristic composition of proteins, and a recent report indicates that mast cell exosomes harbor a variety of mRNAs and microRNAs as well. Exosomes express cell recognition molecules on their surface that facilitate their selective targeting and uptake into recipient cells.</p> <p>Results</p> <p>In this review, I suggest that exosomal secretion of proteins and RNAs may be a fundamental mode of communication within the nervous system, supplementing the known mechanisms of anterograde and retrograde signaling across synapses. In one specific scenario, exosomes are proposed to bud from the lipid raft region of the postsynaptic membrane adjacent to the postsynaptic density, in a manner that is stimulated by stimuli that elicit long-term potentiation. The exosomes would then transfer newly synthesized synaptic proteins (such as CAM kinase II alpha) and synaptic RNAs to the presynaptic terminal, where they would contribute to synaptic plasticity.</p> <p>Conclusion</p> <p>The model is consistent with the known cellular and molecular features of synaptic neurobiology and makes a number of predictions that can be tested in vitro and in vivo.</p> <p>Open peer review</p> <p>Reviewed by Etienne Joly, Gaspar Jekely, Juergen Brosius and Eugene Koonin. For the full reviews, please go to the Reviewers' comments section.</p

Synaptic enrichment of microRNAs in adult mouse forebrain is related to structural features of their precursors

Within mouse forebrain, a subset of microRNAs are significantly enriched in synaptoneurosomes (a synaptic fraction containing pinched-off dendritic spines) and a subset are significantly depleted relative to total forebrain homogenate. Here I show that, as a group, the pre-miR hairpin precursors of synaptically enriched microRNAs exhibit significantly different structural features than those that are non-enriched or depleted. Precursors of synaptically enriched microRNAs tend to have a) shorter uninterrupted double-stranded stem segments, and b) more symmetrical bulges containing a single nucleotide on each side. These structural differences may provide a basis for the differential binding of proteins that mediate dendritic transport of pre-miRs, or that prevent pre-miRs from being prematurely processed into mature miRNAs during the transport process

Launching the "Journal of Biomedical Discovery and Collaboration"

The Journal of Biomedical Discovery and Collaboration was created to provide, for the first time, a unified forum to consider all factors that affect scientific practice and scientific discovery – with an emphasis on the changing face of contemporary biomedical science. In this endeavor we are bringing together three different groups of scholars: a) laboratory investigators, who make the discoveries that are the currency of the scientific enterprise; b) computer science and informatics investigators, who devise tools for data analysis, mining, visualization and integration; and c) social scientists, including sociologists, historians, and philosophers, who study scientific practice, collaboration, and information needs. We will publish original research articles, case studies, focus pieces, reviews, and software articles. All articles in the Journal of Biomedical Discovery and Collaboration will be peer reviewed, published immediately upon acceptance, freely available online via open access, and archived in PubMed Central and other international full-text repositories

Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation

In the present paper, we have created and characterized several similarity metrics for relating any two Medical Subject Headings (MeSH terms) to each other. The article-based metric measures the tendency of two MeSH terms to appear in the MEDLINE record of the same article. The author-based metric measures the tendency of two MeSH terms to appear in the body of articles written by the same individual (using the 2009 Author-ity author name disambiguation dataset as a gold standard). The two metrics are only modestly correlated with each other (r = 0.50), indicating that they capture different aspects of term usage. The article-based metric provides a measure of semantic relatedness, and MeSH term pairs that co-occur more often than expected by chance may reflect relations between the two terms. In contrast, the author metric is indicative of how individuals practice science, and may have value for author name disambiguation and studies of scientific discovery. We have calculated article metrics for all MeSH terms appearing in at least 25 articles in MEDLINE (as of 2014) and author metrics for MeSH terms published as of 2009. The dataset is freely available for download and can be queried at http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html. Handling editor: Elizabeth Workman, MLIS, PhD.

A tutorial on information retrieval: basic terms and concepts

This informal tutorial is intended for investigators and students who would like to understand the workings of information retrieval systems, including the most frequently used search engines: PubMed and Google. Having a basic knowledge of the terms and concepts of information retrieval should improve the efficiency and productivity of searches. As well, this knowledge is needed in order to follow current research efforts in biomedical information retrieval and text mining that are developing new systems not only for finding documents on a given topic, but extracting and integrating knowledge across documents

Guidelines for Negotiating Scientific Collaboration

Whether it's sharing reagents with a laboratory on the other side of the world or working with the postdoc at the neighboring bench, some simple rules of collaboration might help