Identification of novel components of Trypanosoma brucei editosomes

The editosome is a multiprotein complex that catalyzes the insertion and deletion of uridylates that occurs during RNA editing in trypanosomatids. We report the identification of nine novel editosome proteins in Trypanosoma brucei. They were identified by mass spectrometric analysis of functional editosomes that were purified by serial ion exchange/gel permeation chromatography, immunoaffinity chromatography specific to the TbMP63 editosome protein, or tandem affinity purification based on a tagged RNA editing ligase. The newly identified proteins have ribonuclease and/or RNA binding motifs suggesting nuclease function for at least some of these. Five of the proteins are interrelated, as are two others, and one is related to four previously identified editosome proteins. The implications of these findings are discussed.     

Four related proteins of the Trypanosoma brucei RNA editing complex

RNA editing in kinetoplastid mitochondria occurs by a series of enzymatic steps that is catalyzed by a macromolecular complex. Four novel proteins and their corresponding genes were identified by mass spectrometric analysis of purified editing complexes from Trypanosoma brucei. These four proteins, TbMP81, TbMP63, TbMP42, and TbMP18, contain conserved sequences to various degrees. All four proteins have sequence similarity in the C terminus; TbMP18 has considerable sequence similarity to the C-terminal region of TbMP42, and TbMP81, TbMP63, and TbMP42 contain zinc finger motif(s). Monoclonal antibodies that are specific for TbMP63 and TbMP42 immunoprecipitate in vitro RNA editing activities. The proteins are present in the immunoprecipitates and sediment at 20S along with the in vitro editing, and RNA editing ligases TbMP52 and TbMP48. Recombinant TbMP63 and TbMP52 coimmunoprecipitate. These results indicate that these four proteins are components of the RNA editing complex and that TbMP63 and TbMP52 can interact.     

A fluorescence-based reporter substrate for monitoring RNA editing in trypanosomatid pathogens

RNA editing regulates mitochondrial gene expression in trypanosomatid pathogens by creating functional mRNAs. It is catalyzed by a multi-protein complex (the editosome), and is found to be essential in both insect stage and mammalian blood stream form of Trypanosoma brucei. This particular form of RNA editing is unique to trypanosomatids, and thus provides a suitable drug target in trypanosomatid pathogens. Here, we demonstrate the feasibility of a rapid and sensitive fluorescence-based reporter assay to monitor RNA editing based on ribozyme activity. We could validate our new assay using previously identified inhibitors against the essential RNA editing ligase. The principle advantages of this assay are: (i) the use of non-radioactively labeled materials, (ii) sensitivity afforded by fluorescence instrumentation applicable to high-throughput screening of chemical inhibitors against the essential editosome and (iii) a rapid and convenient ‘mix and measure’ type of assay in low volume with a high signal to noise ratio. This assay should enhance rapid identification and characterization of the editosome inhibitors primarily based on the overall composition of the editosomes from T. brucei. These inhibitors could also be tested against the editosomes from the closely related pathogens including T. cruzi and Leishmania species.     

Analysis of proteomic changes in roots of soybean seedlings during recovery after flooding

A proteomic approach was used to identify proteins involved in post-flooding recovery in soybean roots. Two-day-old soybean seedlings were flooded with water for up to 3 days. After the flooding treatment, seedlings were grown until 7 days after sowing and root proteins were then extracted and separated using two-dimensional polyacrylamide gel electrophoresis (2-DE). Comparative analysis of 2-D gels of control and 3 day flooding-experienced soybean root samples revealed 70 differentially expressed protein spots, from which 80 proteins were identified. Many of the differentially expressed proteins are involved in protein destination/storage and metabolic processes. Clustering analysis based on the expression profiles of the 70 differentially expressed protein spots revealed that 3 days of flooding causes significant changes in protein expression, even during post-flooding recovery. Three days of flooding resulted in downregulation of ion transport-related proteins and upregulation of proteins involved in cytoskeletal reorganization, cell expansion, and programmed cell death. Furthermore, 7 proteins involved in cell wall modification and S-adenosylmethionine synthesis were identified in roots from seedlings recovering from 1 day of flooding. These results suggest that alteration of cell structure through changes in cell wall metabolism and cytoskeletal organization may be involved in post-flooding recovery processes in soybean seedlings.     

RNA Catalyst as a Reporter for Screening Drugs against RNA Editing in Trypanosomes

Substantial progress has been made in determining the mechanism of mitochondrial RNA editing in trypanosomes. Similarly, considerable progress has been made in identifying the components of the editosome complex that catalyze RNA editing. However, it is still not clear how those proteins work together. Chemical compounds obtained from a high-throughput screen against the editosome may block or affect one or more steps in the editing cycle. Therefore, the identification of new chemical compounds will generate valuable molecular probes for dissecting the editosome function and assembly. In previous studies, in vitro editing assays were carried out using radio-labeled RNA. These assays are time consuming, inefficient and unsuitable for high-throughput purposes. Here, a homogenous fluorescence-based “mix and measure” hammerhead ribozyme in vitro reporter assay to monitor RNA editing, is presented. Only as a consequence of RNA editing of the hammerhead ribozyme a fluorescence resonance energy transfer (FRET) oligoribonucleotide substrate undergoes cleavage. This in turn results in separation of the fluorophore from the quencher thereby producing a signal. In contrast, when the editosome function is inhibited, the fluorescence signal will be quenched. This is a highly sensitive and simple assay that should be generally applicable to monitor in vitro RNA editing or high throughput screening of chemicals that can inhibit the editosome function.     

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’s topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly. As a result, the inverse methods that utilize firing activity of neurons in order to identify the (functional) connections have gained momentum recently, especially in light of rapid advances in recording technologies; It will soon be possible to simultaneously monitor the activities of tens of thousands of neurons in real time. While there are a number of excellent approaches that aim to identify the functional connections from firing activities, the scalability of the proposed techniques plays a major challenge in applying them on large-scale datasets of recorded firing activities. In exceptional cases where scalability has not been an issue, the theoretical performance guarantees are usually limited to a specific family of neurons or the type of firing activities. In this paper, we formulate the neural network reconstruction as an instance of a graph learning problem, where we observe the behavior of nodes/neurons (i.e., firing activities) and aim to find the links/connections. We develop a scalable learning mechanism and derive the conditions under which the estimated graph for a network of Leaky Integrate and Fire (LIf) neurons matches the true underlying synaptic connections. We then validate the performance of the algorithm using artificially generated data (for benchmarking) and real data recorded from multiple hippocampal areas in rats.