Uridine insertion/deletion RNA editing is an essential process in kinetoplastid parasites whereby mitochondrial mRNAs are modified through the specific insertion and deletion of uridines to generate functional open reading frames, many of which encode components of the mitochondrial respiratory chain. The roles of numerous non-enzymatic editing factors have remained opaque given the limitations of conventional methods to interrogate the order and mechanism by which editing progresses and thus roles of individual proteins. Here, we examined whole populations of partially edited sequences using high throughput sequencing and a novel bioinformatic platform, the Trypanosome RNA Editing Alignment Tool (TREAT), to elucidate the roles of three proteins in the RNA Editing Mediator Complex (REMC). We determined that the factors examined function in the progression of editing through a gRNA; however, they have distinct roles and REMC is likely heterogeneous in composition. We provide the first evidence that editing can proceed through numerous paths within a single gRNA and that non-linear modifications are essential, generating commonly observed junction regions. Our data support a model in which RNA editing is executed via multiple paths that necessitate successive re-modification of junction regions facilitated, in part, by the REMC variant containing TbRGG2 and MRB8180.

The trypanosome NAditing ubstrate bindingomplex (RESC) acts as the platform for mitochondrial uridine insertion/deletion RNA editing and facilitates the protein-protein and protein-RNA interactions required for the editing process RESC is broadly comprised of two subcomplexes: GRBC (uide NAinding omplex) and REMC (NA ditingediator omplex). Here, we characterize the function and position in RESC organization of a previously unstudied RESC protein, MRB7260. We show that MRB7260 forms numerous RESC-related complexes, including a novel, small complex with the guide RNA binding protein, GAP1, which is a canonical GRBC component, and REMC components MRB8170 and TbRGG2. RNA immunoprecipitations in MRB7260-depleted cells show that MRB7260 is critical for normal RNA trafficking between REMC and GRBC. Analysis of protein-protein interactions also reveals an important role for MRB7260 in promoting stable association of the two subcomplexes. High-throughput sequencing analysis of RPS12 mRNAs from MRB7260 replete and depleted cells demonstrates that MRB7260 is critical for gRNA exchange and early gRNA utilization, with the exception of the initiating gRNA. Together, these data demonstrate that MRB7260 is essential for productive protein-RNA interactions with RESC during RNA editing.

Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Uridine insertion deletion editing in kinetoplastid protozoa requires a complex machinery, a primary component of which is the RNA editing substrate binding complex (RESC). RESC contains two modules termed GRBC (guide RNA binding complex) and REMC (RNA editing mediator complex), although how interactions between these modules and their mRNA and gRNA binding partners are controlled is not well understood. Here, we demonstrate that the ARM/HEAT repeat containing RESC protein, MRB10130, controls REMC association with mRNA-and gRNA-loaded GRBC. High-throughput sequencing analyses show that MRB10130 functions in both initiation and 3 ′ ′ ′ ′ ′ to 5 ′ ′ ′ ′ ′ progression of editing through gRNA-defined domains. Editing intermediates that accumulate upon MRB10130 depletion significantly intersect those in cells depleted of another RESC organizer, MRB7260, but are distinct from those in cells depleted of specific REMC proteins. We present a model in which MRB10130 coordinates numerous protein-protein and protein-RNA interactions during editing progression.

Background Due to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with bladder cancer. The identification of multiplex molecular signatures associated with disease has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis and monitoring. Methods To evaluate the performance of Oncuria™, a multiplex immunoassay for bladder detection in voided urine samples. The test was evaluated in a multi-institutional cohort of 362 prospectively collected subjects presenting for bladder cancer evaluation. The parallel measurement of 10 biomarkers (A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1 and VEGFA) was performed in an independent clinical laboratory. The ability of the test to identify patients harboring bladder cancer was assessed. Bladder cancer status was confirmed by cystoscopy and tissue biopsy. The association of biomarkers and demographic factors was evaluated using linear discriminant analysis (LDA) and predictive models were derived using supervised learning and cross-validation analyses. Diagnostic performance was assessed using ROC curves. Results The combination of the 10 biomarkers provided an AUROC 0.93 [95% CI 0.87–0.98], outperforming any single biomarker. The addition of demographic data (age, sex, and race) into a hybrid signature improved the diagnostic performance AUROC 0.95 [95% CI 0.90–1.00]. The hybrid signature achieved an overall sensitivity of 0.93, specificity of 0.93, PPV of 0.65 and NPV of 0.99 for bladder cancer classification. Sensitivity values of the diagnostic panel for high-grade bladder cancer, low-grade bladder cancer, MIBC and NMIBC were 0.94, 0.89, 0.97 and 0.93, respectively. Conclusions Urinary levels of a biomarker panel enabled the accurate discrimination of bladder cancer patients and controls. The multiplex Oncuria™ test can achieve the efficient and accurate detection and monitoring of bladder cancer in a non-invasive patient setting.

As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy.

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