A Latent Pro-survival Function for the Mir-290-295 Cluster in Mouse Embryonic Stem Cells

MicroRNAs (miRNAs) post-transcriptionally regulate the expression of thousands of distinct mRNAs. While some regulatory interactions help to maintain basal cellular functions, others are likely relevant in more specific settings, such as response to stress. Here we describe such a role for the mir-290-295 cluster, the dominant miRNA cluster in mouse embryonic stem cells (mESCs). Examination of a target list generated from bioinformatic prediction, as well as expression data following miRNA loss, revealed strong enrichment for apoptotic regulators, two of which we validated directly: Caspase 2, the most highly conserved mammalian caspase, and Ei24, a p53 transcriptional target. Consistent with these predictions, mESCs lacking miRNAs were more likely to initiate apoptosis following genotoxic exposure to gamma irradiation or doxorubicin. Knockdown of either candidate partially rescued this pro-apoptotic phenotype, as did transfection of members of the mir-290-295 cluster. These findings were recapitulated in a specific mir-290-295 deletion line, confirming that they reflect miRNA functions at physiological levels. In contrast to the basal regulatory roles previously identified, the pro-survival phenotype shown here may be most relevant to stressful gestations, where pro-oxidant metabolic states induce DNA damage. Similarly, this cluster may mediate chemotherapeutic resistance in a neoplastic context, making it a useful clinical target.National Institutes of Health (U.S.) (NIH grant RO1-GM34277)National Cancer Institute (U.S.) (NCI grant PO1-CA42063)National Cancer Institute (U.S.) (NCI Cancer Center Support (core) grant P30-CA14051

In pursuit of P2X3 antagonists: novel therapeutics for chronic pain and afferent sensitization

Treating pain by inhibiting ATP activation of P2X3-containing receptors heralds an exciting new approach to pain management, and Afferent's program marks the vanguard in a new class of drugs poised to explore this approach to meet the significant unmet needs in pain management. P2X3 receptor subunits are expressed predominately and selectively in so-called C- and Aδ-fiber primary afferent neurons in most tissues and organ systems, including skin, joints, and hollow organs, suggesting a high degree of specificity to the pain sensing system in the human body. P2X3 antagonists block the activation of these fibers by ATP and stand to offer an alternative approach to the management of pain and discomfort. In addition, P2X3 is expressed pre-synaptically at central terminals of C-fiber afferent neurons, where ATP further sensitizes transmission of painful signals. As a result of the selectivity of the expression of P2X3, there is a lower likelihood of adverse effects in the brain, gastrointestinal, or cardiovascular tissues, effects which remain limiting factors for many existing pain therapeutics. In the periphery, ATP (the factor that triggers P2X3 receptor activation) can be released from various cells as a result of tissue inflammation, injury or stress, as well as visceral organ distension, and stimulate these local nociceptors. The P2X3 receptor rationale has aroused a formidable level of investigation producing many reports that clarify the potential role of ATP as a pain mediator, in chronic sensitized states in particular, and has piqued the interest of pharmaceutical companies. P2X receptor-mediated afferent activation has been implicated in inflammatory, visceral, and neuropathic pain states, as well as in airways hyperreactivity, migraine, itch, and cancer pain. It is well appreciated that oftentimes new mechanisms translate poorly from models into clinical efficacy and effectiveness; however, the breadth of activity seen from P2X3 inhibition in models offers a realistic chance that this novel mechanism to inhibit afferent nerve sensitization may find its place in the sun and bring some merciful relief to the torment of persistent discomfort and pain. The development philosophy at Afferent is to conduct proof of concept patient studies and best identify target patient groups that may benefit from this new intervention

GeneRIF indexing: sentence selection based on machine learning

BACKGROUND: A Gene Reference Into Function (GeneRIF) describes novel functionality of genes. GeneRIFs are available from the National Center for Biotechnology Information (NCBI) Gene database. GeneRIF indexing is performed manually, and the intention of our work is to provide methods to support creating the GeneRIF entries. The creation of GeneRIF entries involves the identification of the genes mentioned in MEDLINE®; citations and the sentences describing a novel function. RESULTS: We have compared several learning algorithms and several features extracted or derived from MEDLINE sentences to determine if a sentence should be selected for GeneRIF indexing. Features are derived from the sentences or using mechanisms to augment the information provided by them: assigning a discourse label using a previously trained model, for example. We show that machine learning approaches with specific feature combinations achieve results close to one of the annotators. We have evaluated different feature sets and learning algorithms. In particular, Naïve Bayes achieves better performance with a selection of features similar to one used in related work, which considers the location of the sentence, the discourse of the sentence and the functional terminology in it. CONCLUSIONS: The current performance is at a level similar to human annotation and it shows that machine learning can be used to automate the task of sentence selection for GeneRIF annotation. The current experiments are limited to the human species. We would like to see how the methodology can be extended to other species, specifically the normalization of gene mentions in other species

MeSH indexing based on automatically generated summaries

BACKGROUND: MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. RESULTS: We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. CONCLUSIONS: Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading

Application of a Medical Text Indexer to an Online Dermatology Atlas

Clinical dermatology cases are presented as images and semistructured text describing skin lesions and their relationships to disease. Metadata assignment to such cases is hampered by lack of a standardized dermatology vocabulary and facilitated methods for indexing legacy collections. In this pilot study descriptive clinical text from Dermatlas, a Web-based repository of dermatology cases, was indexed to Medical Subject Heading (MeSH) terms using the National Library of Medicine's Medical Text Indexer (MTI). The MTI is an automated text processing system that derives ranked lists of MeSH terms to describe the content of medical journal citations using knowledge from the Unified Medical Language System (UMLS) and from MEDLINE. For a representative, random sample of 50 Dermatlas cases, the MTI frequently derived MeSH indexing terms that matched expert-assigned terms for Diagnoses (88%), Lesion Types (72%), and Patient Characteristics (Gender and Age Groups, 62% and 84% respectively). This pilot demonstrates the potential for extending the MTI to automate indexing of clinical case presentations and for using MeSH to describe aspects of clinical dermatology

Feature engineering for MEDLINE citation categorization with MeSH

BACKGROUND: Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. RESULTS: Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. CONCLUSIONS: We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system