66 research outputs found
Mapping Posttranscriptional Regulation of the Human Glycome Uncovers microRNA Defining the Glycocode
Cell surface glycans form a critical interface with the biological milieu, informing diverse processes from the inflammatory cascade to cellular migration. Assembly of discrete carbohydrate structures requires the coordinated activity of a repertoire of proteins, including glycosyltransferases and glycosidases. Little is known about the regulatory networks controlling this complex biosynthetic process. Recent work points to a role for microRNA (miRNA) in the regulation of specific glycan biosynthetic enzymes. Herein we take a unique systems-based approach to identify connections between miRNA and the glycome. By using our glycomic analysis platform, lectin microarrays, we identify glycosylation signatures in the NCI-60 cell panel that point to the glycome as a direct output of genomic information flow. Integrating our glycomic dataset with miRNA data, we map miRNA regulators onto genes in glycan biosynthetic pathways (glycogenes) that generate the observed glycan structures. We validate three of these predicted miRNA/glycogene regulatory networks: high mannose, fucose, and terminal β-GalNAc, identifying miRNA regulation that would not have been observed by traditional bioinformatic methods. Overall, our work reveals critical nodes in the global glycosylation network accessible to miRNA regulation, providing a bridge between miRNA-mediated control of cell phenotype and the glycome
Recommended from our members
BIOSMILE: A semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features
Background: Bioinformatics tools for automatic processing of biomedical literature are invaluable for both the design and interpretation of large-scale experiments. Many information extraction (IE) systems that incorporate natural language processing (NLP) techniques have thus been developed for use in the biomedical field. A key IE task in this field is the extraction of biomedical relations, such as protein-protein and gene-disease interactions. However, most biomedical relation extraction systems usually ignore adverbial and prepositional phrases and words identifying location, manner, timing, and condition, which are essential for describing biomedical relations. Semantic role labeling (SRL) is a natural language processing technique that identifies the semantic roles of these words or phrases in sentences and expresses them as predicate-argument structures. We construct a biomedical SRL system called BIOSMILE that uses a maximum entropy (ME) machine-learning model to extract biomedical relations. BIOSMILE is trained on BioProp, our semi-automatic, annotated biomedical proposition bank. Currently, we are focusing on 30 biomedical verbs that are frequently used or considered important for describing molecular events. Results: To evaluate the performance of BIOSMILE, we conducted two experiments to (1) compare the performance of SRL systems trained on newswire and biomedical corpora; and (2) examine the effects of using biomedical-specific features. The experimental results show that using BioProp improves the F-score of the SRL system by 21.45% over an SRL system that uses a newswire corpus. It is noteworthy that adding automatically generated template features improves the overall F-score by a further 0.52%. Specifically, ArgM-LOC, ArgM-MNR, and Arg2 achieve statistically significant performance improvements of 3.33%, 2.27%, and 1.44%, respectively. Conclusion: We demonstrate the necessity of using a biomedical proposition bank for training SRL systems in the biomedical domain. Besides the different characteristics of biomedical and newswire sentences, factors such as cross-domain framesets and verb usage variations also influence the performance of SRL systems. For argument classification, we find that NE (named entity) features indicating if the target node matches with NEs are not effective, since NEs may match with a node of the parsing tree that does not have semantic role labels in the training set. We therefore incorporate templates composed of specific words, NE types, and POS tags into the SRL system. As a result, the classification accuracy for adjunct arguments, which is especially important for biomedical SRL, is improved significantly
Recommended from our members
A systems approach to analyzing bacterial glycans and glycan-binding proteins
textCarbohydrates are prominent and accessible polymers found on the cell-surface and represent the first interface with the extracellular environment. The enormous diversity of cell-surface carbohydrates is recognized as a high-density coding language that mediates molecular recognition between cells. In the case of bacteria-host interactions, both bacterial glycans and glycan-binding proteins (lectins) are important in fine-tuning the biological response. The nature of these interactions is unique, with affinity and specificity determined by multiple low-affinity binding events between lectins and carbohydrates. Thus, the analysis of lectin-glycan interactions on a systems level is an important and necessary step towards understanding the role of glycosylation in bacteria-host communication. This dissertation describes work pioneering the application of lectin microarrays to the analysis of bacterial glycosylation. Lectin microarrays represent a new glycomics approach for the high-throughput analysis of bacterial glycans. This approach is non-destructive and allows rapid profiling of intact bacterial surfaces. Lectin microarray analysis of fluorescently-labeled bacteria provides a visual binding pattern representing the glycosylation profile of the cell-surface. These lectin-binding profiles can be compared and used to distinguish bacterial strains based on glycosylation. In the course of examining a pathogenic strain, we discover a major limitation of the lectin microarray technology. The majority of plant lectins present on the array are post-translationally modified with carbohydrates, leading to potential false positives due to binding by bacterial lectins. Unexpectedly, this limitation provides the rationale for creating a recombinant lectin panel using bacteria as the source. We define the glycan-binding specificity of the bacterial lectins using both carbohydrate microarrays and ELISA, leading to the discovery of new specificities. The recombinant panel is then used to create the first recombinant lectin microarray, demonstrating that bacterial lectins in an array format are capable of analyzing glycosylated samples. Therefore, the studies on bacterial glycans have led to the development of new tools for carbohydrate analysis, a necessary step towards understanding the structure-function relationship of carbohydrates in nature.Biochemistr
Serine Hydrolase Inhibitors Block Necrotic Cell Death by Preventing Calcium Overload of the Mitochondria and Permeability Transition Pore Formation
general view, 193
- …