KREH1 RNA helicase activity promotes utilization of initiator gRNAs across multiple mRNAs in trypanosome RNA editing.

Mitochondrial U-indel RNA editing in kinetoplastid protozoa is directed by trans-acting gRNAs and mediated by a holoenzyme with associated factors. Here, we examine the function of the holoenzyme-associated KREH1 RNA helicase in U-indel editing. We show that KREH1 knockout (KO) impairs editing of a small subset of mRNAs. Overexpression of helicase-dead mutants results in expanded impairment of editing across multiple transcripts, suggesting the existence of enzymes that can compensate for KREH1 in KO cells. In depth analysis of editing defects using quantitative RT-PCR and high-throughput sequencing reveals compromised editing initiation and progression in both KREH1-KO and mutant-expressing cells. In addition, these cells exhibit a distinct defect in the earliest stages of editing in which the initiator gRNA is bypassed, and a small number of editing events takes place just outside this region. Wild type KREH1 and a helicase-dead KREH1 mutant interact similarly with RNA and holoenzyme, and overexpression of both similarly disorders holoenzyme homeostasis. Thus, our data support a model in which KREH1 RNA helicase activity facilitates remodeling of initiator gRNA-mRNA duplexes to permit accurate utilization of initiating gRNAs on multiple transcripts.

Conserved and transcript-specific functions of the RESC factors, RESC13 and RESC14, in kinetoplastid RNA editing.

Uridine insertion/deletion RNA editing is an extensive post-transcriptional modification of mitochondrial mRNAs in kinetoplastid organisms, including Trypanosoma brucei This process is carried out using trans-acting gRNAs and complex protein machinery. The essential RNA editing substrate binding complex (RESC) serves as the scaffold that modulates protein and RNA interactions during editing, and contains the guide RNA binding complex (GRBC), the RNA editing mediator complexes (REMCs), and organizer proteins. Despite the importance of RESC in editing, the functions of each protein comprising this complex are not completely understood. Here, we further define the roles of a REMC protein, RESC13, and a RESC organizer, RESC14, using high-throughput sequencing on two large pan-edited mRNAs, A6 and COIII. When comparing our analyses to that of a previously published small pan-edited mRNA, RPS12, we find that RESC13 has conserved functions across the three transcripts with regard to editing initiation, gRNA utilization, gRNA exchange, and restricting the formation of long misedited junctions that likely arise from its ability to modulate RNA structure. However, RESC13 does have transcript-specific effects on the types of long junctions whose formation it restricts. RESC14 has a conserved effect on gRNA utilization across the three transcripts analyzed, but has transcript-specific effects on editing initiation, gRNA exchange, and junction formation. Our data suggest that transcript-specific effects of both proteins are due to differences in transcript length and sequences as well as transcript-specific protein interactions. These findings highlight the importance of studying multiple transcripts to determine the function of editing factors.

Alcohol Consumption and the Diversity of the Oral Microbiome in Postmenopausal Women.

BACKGROUND: Alcohol reduces neutrophil function and decreases salivary flow, which could affect the composition of the oral microbiome. OBJECTIVE: We hypothesized that the α- and ß-diversity of the oral microbiome and the relative abundance of bacterial taxa would differ by frequency and type of alcohol consumption. METHODS: We used a food frequency questionnaire to assess the frequency of consumption of beer, wine, and liquor (drinks/week) in a sample of 1179 postmenopausal women in the Osteoporosis and Periodontal Disease Study. Women were categorized as nondrinkers, drinking <1 drink/wk, ≥1 to <7 drinks/wk, or ≥7 drinks/wk for total alcohol consumption and for beer, wine, and liquor consumption. The composition and diversity of the oral microbiome was assessed from subgingival plaque samples using 16S ribosomal RNA amplicon sequencing. Permutational multivariate analysis of variance (PERMANOVA) was used to examine ß-diversity (between-sample diversity) in the microbiome between alcohol consumption categories. Analysis of covariance was used to examine the mean α-diversity (within-sample diversity), assessed by the Shannon index (species evenness), Chao1 index (species richness), and observed operational taxonomic unit (OTU) count and the mean relative abundance of 245 bacterial taxa across alcohol consumption categories. RESULTS: Over half of the participants (67%) consumed alcohol, with 14% reporting ≥1 drink/d. The ß-diversity across categories of total alcohol consumption, but not categories of alcohol type, was statistically significantly different (P for PERMANOVA = 0.016). Mean α-diversity measures were statistically significantly higher (P < 0.05) in the highest category of total alcohol and wine consumption compared to nondrinkers; no significant associations were found for beer or liquor consumption. The relative abundance of 1 OTU, Selenomonassp._oral_taxon_133, was significantly lower in the highest level of total alcohol consumption compared to nondrinkers after adjustment for multiple comparisons. CONCLUSIONS: Alcohol consumption was associated with the diversity and composition of the subgingival microbiome.

Computational approach to modeling microbiome landscapes associated with chronic human disease progression.

A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn's disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn's disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn's disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

Thickness effect of 2D PdSe2film on performance of PdSe2/Si heterostructure photodetectors.

Two-dimensional (2D) PdSe2film has the characteristics of adjustable bandgap, high carrier mobility, and high stability. Photodetector (PD) based on 2D PdSe2exhibits wide spectral self-driving features, demonstrating enormous potential in the field of optical detection. Here, we design and fabricate PdSe2/Si heterojunction PDs with various thicknesses of the PdSe2films from 10 to 35 nm. Due to the enhancement of light absorption capacity and built-in electric field of heterojunction, the photodetector with thicker PdSe2film can generate more photo-generated carriers and effectively separate them to form a large photocurrent, thus showing more excellent photodetection performance. The responsivity and specific detectivity of the PdSe2/Si PDs with 10 nm, 20 nm, and 35 nm PdSe2films are 2.12 A W-1and 6.72 × 109Jones, 6.17 A W-1and 1.95 × 1010Jones, and 8.02 A W-1and 2.54 × 1010Jones, respectively (808 nm illumination). The PD with 35 nm PdSe2film exhibits better performance than the other two PDs, with the rise/fall times of 15.8µs/138.9µs atf= 1 kHz and the cut-off frequency of 8.6 kHz. Furthermore, we demonstrate that the properties of PdSe2/Si PD array have excellent uniformity and stability at room temperature and shows potential for image sensing in the UV-vis-NIR wavelength range.

Design, synthesis and evaluation of dihydro-1H-indene derivatives as novel tubulin polymerisation inhibitors with anti-angiogenic and antitumor potency.

Angiogenesis plays an important role in tumour generation and progression, which is used to supply nutrients and metastasis. Herein, a series of novel dihydro-1H-indene derivatives were designed and evaluated as tubulin polymerisation inhibitors by binding to colchicine site, exhibiting anti-angiogenic activities against new vessel forming. Through structure-activity relationships study, compound 12d was found to be the most potent derivative possessing the antiproliferative activity against four cancer lines with IC50 values among 0.028-0.087 µM. Compound 12d bound to colchicine site on tubulin and inhibited tubulin polymerisation in vitro. In addition, compound 12d induced cell cycle arrest at G2/M phase, stimulated cell apoptosis, inhibited tumour metastasis and angiogenesis. Finally, the results of in vivo assay suggested that compound 12d could prevent tumour generation, inhibit tumour proliferation and angiogenesis without obvious toxicity. Collectively, all these findings suggested that compound 12d is a novel tubulin polymerisation inhibitor deserving further research.

Developmental regulation of edited CYb and COIII mitochondrial mRNAs is achieved by distinct mechanisms in Trypanosoma brucei.

Trypanosoma brucei is a parasitic protozoan that undergoes a complex life cycle involving insect and mammalian hosts that present dramatically different nutritional environments. Mitochondrial metabolism and gene expression are highly regulated to accommodate these environmental changes, including regulation of mRNAs that require extensive uridine insertion/deletion (U-indel) editing for their maturation. Here, we use high throughput sequencing and a method for promoting life cycle changes in vitro to assess the mechanisms and timing of developmentally regulated edited mRNA expression. We show that edited CYb mRNA is downregulated in mammalian bloodstream forms (BSF) at the level of editing initiation and/or edited mRNA stability. In contrast, edited COIII mRNAs are depleted in BSF by inhibition of editing progression. We identify cell line-specific differences in the mechanisms abrogating COIII mRNA editing, including the possible utilization of terminator gRNAs that preclude the 3' to 5' progression of editing. By examining the developmental timing of altered mitochondrial mRNA levels, we also reveal transcript-specific developmental checkpoints in epimastigote (EMF), metacyclic (MCF), and BSF. These studies represent the first analysis of the mechanisms governing edited mRNA levels during T. brucei development and the first to interrogate U-indel editing in EMF and MCF life cycle stages.

Phthalide metabolites produced by mangrove endophytic fungus Pestalotiopsis sp. SAS4.

Two new phthalide derivatives (1-2) and four known phthalide compounds (3-6) were purified from the culture of a mangrove endophytic fungus Pestalotiopsis sp. SAS4. Their chemical structures were established by analyses of 1D and 2D nuclear magnetic resonance (NMR) and high resolution mass spectrometry (HR-MS) spectroscopic data. All of these compounds were evaluated in vitro for antibacterial, cytotoxicity, and resistance to hypoxic-ischemic injury activities.

MRB10130 is a RESC assembly factor that promotes kinetoplastid RNA editing initiation and progression.

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.

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.

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.

Diagnostic performance of Oncuria™, a urinalysis test for bladder cancer.

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.

Intrinsic and regulated properties of minimally edited trypanosome mRNAs.

Most mitochondrial mRNAs in kinetoplastids require extensive uridine insertion/deletion editing to generate translatable open reading frames. Editing is specified by trans-acting gRNAs and involves a complex machinery including basal and accessory factors. Here, we utilize high-throughput sequencing to analyze editing progression in two minimally edited mRNAs that provide a simplified system due their requiring only two gRNAs each for complete editing. We show that CYb and MURF2 mRNAs exhibit barriers to editing progression that differ from those previously identified for pan-edited mRNAs, primarily at initial gRNA usage and gRNA exchange. We demonstrate that mis-edited junctions arise through multiple pathways including mis-alignment of cognate gRNA, incorrect and sometimes promiscuous gRNA utilization and inefficient gRNA anchoring. We then examined the roles of accessory factors RBP16 and MRP1/2 in maintaining edited CYb and MURF2 populations. RBP16 is essential for initiation of CYb and MURF2 editing, as well as MURF2 editing progression. In contrast, MRP1/2 stabilizes both edited mRNA populations, while further promoting progression of MURF2 mRNA editing. We also analyzed the effects of RNA Editing Substrate Binding Complex components, TbRGG2 and GAP1, and show that both proteins modestly impact progression of editing on minimally edited mRNAs, suggesting a novel function for GAP1.

Evaluation of alternative confidence intervals to address non-inferiority through the stratified difference between proportions.

The confidence interval (CI) for the difference between two proportions has been an important and active research topic, especially in the context of non-inferiority hypothesis testing. Issues concerning the Type 1 error rate, power, coverage rate and aberrations have been extensively studied for non-stratified cases. However, stratified confidence intervals are frequently used in non-inferiority trials and similar settings. In this paper, several methods for stratified confidence intervals for the difference between two proportions, including existing methods and novel extensions from unstratified CIs, are evaluated across different scenarios. When sparsity across the strata is not a concern, adding imputed observations to the stratification analysis can strengthen Type-1 error control without substantial loss of power. When sparseness of data is a concern, most of the evaluated methods fail to control Type-1 error; the modified stratified t-test CI is an exception. We recommend the modified stratified t-test CI as the most useful and flexible method across the respective scenarios; the modified stratified Wald CI may be useful in settings where sparsity is unlikely. These findings substantially contribute to the application of stratified CIs for non-inferiority testing of differences between two proportions.

MRB7260 is essential for productive protein-RNA interactions within the RNA editing substrate binding complex during trypanosome RNA editing.

The trypanosome RNA editing substrate binding complex (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 (guide RNA binding complex) and REMC (RNA editing mediator complex). 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.

A parallel computational framework for ultra-large-scale sequence clustering analysis.

Motivation: The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results: In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation: Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information: Supplementary data are available at Bioinformatics online.

SENSE: Siamese neural network for sequence embedding and alignment-free comparison.

MOTIVATION: Sequence analysis is arguably a foundation of modern biology. Classic approaches to sequence analysis are based on sequence alignment, which is limited when dealing with large-scale sequence data. A dozen of alignment-free approaches have been developed to provide computationally efficient alternatives to alignment-based approaches. However, existing methods define sequence similarity based on various heuristics and can only provide rough approximations to alignment distances. RESULTS: In this article, we developed a new approach, referred to as SENSE (SiamEse Neural network for Sequence Embedding), for efficient and accurate alignment-free sequence comparison. The basic idea is to use a deep neural network to learn an explicit embedding function based on a small training dataset to project sequences into an embedding space so that the mean square error between alignment distances and pairwise distances defined in the embedding space is minimized. To the best of our knowledge, this is the first attempt to use deep learning for alignment-free sequence analysis. A large-scale experiment was performed that demonstrated that our method significantly outperformed the state-of-the-art alignment-free methods in terms of both efficiency and accuracy. AVAILABILITY AND IMPLEMENTATION: Open-source software for the proposed method is developed and freely available at https://www.acsu.buffalo.edu/∼yijunsun/lab/SENSE.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

BACKGROUND: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Plasminogen activator inhibitor-2 (PAI-2) overexpression supports bladder cancer development in PAI-1 knockout mice in N-butyl-N- (4-hydroxybutyl)-nitrosamine- induced bladder cancer mouse model.

BACKGROUND: Accumulating evidence suggests that plasminogen activator inhibitor-1 (PAI-1) plays an important role in bladder tumorigenesis by regulating cell cycle. However, it remains unclear whether and how inhibition of PAI-1 suppresses bladder tumorigenesis. METHODS: To elucidate the therapeutic effect of PAI-1 inhibition, we tested its tumorigenicity in PAI-1 knockout (KO) mice exposed to a known bladder carcinogen. RESULTS: PAI-1 deficiency did not inhibit carcinogen-induced bladder cancer in mice although carcinogen-exposed wild type mice significantly increased PAI-1 levels in bladder tissue, plasma and urine. We found that PAI-1 KO mice exposed to carcinogen tended to upregulate protein C inhibitor (PAI-3), urokinase-type plasminogen activator (uPA) and tissue-type PA (tPA), and significantly increased PAI-2, suggesting a potential compensatory function of these molecules when PAI-1 is abrogated. Subsequent studies employing gene expression microarray using mouse bladder tissues followed by post hoc bioinformatics analysis and validation experiments by qPCR and IHC demonstrated that SERPING1 is further downregulated in PAI-1 KO mice exposed to BBN, suggesting that SERPING1 as a potential missing factor that regulate PAI-2 overexpression (compensation pathway). CONCLUSIONS: These results indicate that serpin compensation pathway, specifically PAI-2 overexpression in this model, supports bladder cancer development when oncoprotein PAI-1 is deleted. Further investigations into PAI-1 are necessary in order to identify true potential targets for bladder cancer therapy.

Computational approach for deriving cancer progression roadmaps from static sample data.

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.

Trypanosome RNA Editing Mediator Complex proteins have distinct functions in gRNA utilization.

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.