Deciphering RNA Regulatory Elements Involved in the Developmental and Environmental Gene Regulation of Trypanosoma brucei

Functional 5' UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks

Biological networks are rich representations of the relationships between entities such as genes or proteins and have become increasingly complete thanks to various high-throughput network mapping experimental approaches. Here, we propose a method to use such networks to guide the search for functional sequence motifs. Specifically, we introduce Local Enrichment of Sequence Motifs in biological Networks (LESMoN), an enumerative motif discovery algorithm that identifies 5' untranslated region (UTR) sequence motifs whose associated proteins form unexpectedly dense clusters in a given biological network. When applied to the human protein-protein interaction network from BioGRID, LESMoN identifies several highly significant 5' UTR sequence motifs, including both previously known motifs and uncharacterized ones. The vast majority of these motifs are evolutionary conserved and the genes containing them are significantly enriched for various gene ontology terms suggesting new associations between 5' UTR motifs and a number of biological processes. We validate in vivo the role in protein expression regulation of three motifs identified by LESMoN.

TrypsNetDB: An integrated framework for the functional characterization of trypanosomatid proteins

Trypanosomatid parasites cause serious infections in humans and production losses in livestock. Due to the high divergence from other eukaryotes, such as humans and model organisms, the functional roles of many trypanosomatid proteins cannot be predicted by homology-based methods, rendering a significant portion of their proteins as uncharacterized. Recent technological advances have led to the availability of multiple systematic and genome-wide datasets on trypanosomatid parasites that are informative regarding the biological role(s) of their proteins. Here, we report TrypsNetDB (http://trypsNetDB.org), a web-based resource for the functional annotation of 16 different species/strains of trypanosomatid parasites. The database not only visualizes the network context of the queried protein(s) in an intuitive way but also examines the response of the represented network in more than 50 different biological contexts and its enrichment for various biological terms and pathways, protein sequence signatures, and potential RNA regulatory elements. The interactome core of the database, as of Jan 23, 2017, contains 101,187 interactions among 13,395 trypanosomatid proteins inferred from 97 genome-wide and focused studies on the interactome of these organisms.

Methods to predict protein function based on sequences enable the rapid annotation of newly sequenced genomes. However, as most of these methods rely on homology-based approaches, non-conserved proteins in trypanosomatids remain elusive for annotation, rendering approximately half of the sequenced proteins uncharacterized. In this study, we developed a user friendly integrated database, TrypsNetDB, which fills multiple gaps in the field by depositing the current interactome knowledge on trypanosomatid proteins and combining this information with other available resources accompanied by related statistical analyses. The database allows automatic inter-species mapping of available data to better characterize the queried proteins in the species of interest. The database is built on fast and reliable ASP.Net framework and provides (i) a significant increase in the genome-wide functional annotation of trypanosomatid proteins, (ii) potential novel targets for therapeutics against trypanosomatids, and (iii) a robust methodology that can be adapted for the functional annotation of other non-model organisms.