RNA-driven JAZF1-SUZ12 gene fusion in human endometrial stromal cells.

Oncogenic fusion genes as the result of chromosomal rearrangements are important for understanding genome instability in cancer cells and developing useful cancer therapies. To date, the mechanisms that create such oncogenic fusion genes are poorly understood. Previously we reported an unappreciated RNA-driven mechanism in human prostate cells in which the expression of chimeric RNA induces specified gene fusions in a sequence-dependent manner. One fundamental question yet to be addressed is whether such RNA-driven gene fusion mechanism is generalizable, or rather, a special case restricted to prostate cells. In this report, we demonstrated that the expression of designed chimeric RNAs in human endometrial stromal cells leads to the formation of JAZF1-SUZ12, a cancer fusion gene commonly found in low-grade endometrial stromal sarcomas. The process is specified by the sequence of chimeric RNA involved and inhibited by estrogen or progesterone. Furthermore, it is the antisense rather than sense chimeric RNAs that effectively drive JAZF1-SUZ12 gene fusion. The induced fusion gene is validated both at the RNA and the genomic DNA level. The ability of designed chimeric RNAs to drive and recapitulate the formation of JAZF1-SUZ12 gene fusion in endometrial cells represents another independent case of RNA-driven gene fusion, suggesting that RNA-driven genomic recombination is a permissible mechanism in mammalian cells. The results could have fundamental implications in the role of RNA in genome stability, and provide important insight in early disease mechanisms related to the formation of cancer fusion genes.

Genome-wide, integrative analysis of circular RNA dysregulation and the corresponding circular RNA-microRNA-mRNA regulatory axes in autism.

Circular RNAs (circRNAs), a class of long noncoding RNAs, are known to be enriched in mammalian neural tissues. Although a wide range of dysregulation of gene expression in autism spectrum disorder (ASD) have been reported, the role of circRNAs in ASD remains largely unknown. Here, we performed genome-wide circRNA expression profiling in postmortem brains from individuals with ASD and controls and identified 60 circRNAs and three coregulated modules that were perturbed in ASD. By integrating circRNA, microRNA, and mRNA dysregulation data derived from the same cortex samples, we identified 8170 ASD-associated circRNA-microRNA-mRNA interactions. Putative targets of the axes were enriched for ASD risk genes and genes encoding inhibitory postsynaptic density (PSD) proteins, but not for genes implicated in monogenetic forms of other brain disorders or genes encoding excitatory PSD proteins. This reflects the previous observation that ASD-derived organoids show overproduction of inhibitory neurons. We further confirmed that some ASD risk genes (NLGN1, STAG1, HSD11B1, VIP, and UBA6) were regulated by an up-regulated circRNA (circARID1A) via sponging a down-regulated microRNA (miR-204-3p) in human neuronal cells. Particularly, alteration of NLGN1 expression is known to affect the dynamic processes of memory consolidation and strengthening. To the best of our knowledge, this is the first systems-level view of circRNA regulatory networks in ASD cortex samples. We provided a rich set of ASD-associated circRNA candidates and the corresponding circRNA-microRNA-mRNA axes, particularly those involving ASD risk genes. Our findings thus support a role for circRNA dysregulation and the corresponding circRNA-microRNA-mRNA axes in ASD pathophysiology.

Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

RNA-mediated gene fusion in mammalian cells.

One of the hallmarks of cancer is the formation of oncogenic fusion genes as a result of chromosomal translocations. Fusion genes are presumed to form before fusion RNA expression. However, studies have reported the presence of fusion RNAs in individuals who were negative for chromosomal translocations. These observations give rise to "the cart before the horse" hypothesis, in which the genesis of a fusion RNA precedes the fusion gene. The fusion RNA then guides the genomic rearrangements that ultimately result in a gene fusion. However, RNA-mediated genomic rearrangements in mammalian cells have never been demonstrated. Here we provide evidence that expression of a chimeric RNA drives formation of a specified gene fusion via genomic rearrangement in mammalian cells. The process is: (i) specified by the sequence of chimeric RNA involved, (ii) facilitated by physiological hormone levels, (iii) permissible regardless of intrachromosomal (TMPRSS2-ERG) or interchromosomal (TMPRSS2-ETV1) fusion, and (iv) can occur in normal cells before malignant transformation. We demonstrate that, contrary to "the cart before the horse" model, it is the antisense rather than sense chimeric RNAs that effectively drive gene fusion, and that this disparity can be explained by transcriptional conflict. Furthermore, we identified an endogenous RNA AZI1 that functions as the "initiator" RNA to induce TMPRSS2-ERG fusion. RNA-driven gene fusion demonstrated in this report provides important insight in early disease mechanisms, and could have fundamental implications in the biology of mammalian genome stability, as well as gene-editing technology via mechanisms native to mammalian cells.

Genome-wide analysis of small nucleolar RNAs of Leishmania major reveals a rich repertoire of RNAs involved in modification and processing of rRNA.

Trypanosomatids are protozoan parasites and the causative agent of infamous infectious diseases. These organisms regulate their gene expression mainly at the post-transcriptional level and possess characteristic RNA processing mechanisms. In this study, we analyzed the complete repertoire of Leishmania major small nucleolar (snoRNA) RNAs by performing RNA-seq analysis on RNAs that were affinity-purified using the C/D snoRNA core protein, SNU13, and the H/ACA core protein, NHP2. This study revealed a large collection of C/D and H/ACA snoRNAs, organized in gene clusters generally containing both snoRNA types. Abundant snoRNAs were identified and predicted to guide trypanosome-specific rRNA cleavages. The repertoire of snoRNAs was compared to that of the closely related Trypanosoma brucei, and 80% of both C/D and H/ACA molecules were found to have functional homologues. The comparative analyses elucidated how snoRNAs evolved to generate molecules with analogous functions in both species. Interestingly, H/ACA RNAs have great flexibility in their ability to guide modifications, and several of the RNA species can guide more than one modification, compensating for the presence of single hairpin H/ACA snoRNA in these organisms. Placing the predicted modifications on the rRNA secondary structure revealed hypermodification regions mostly in domains which are modified in other eukaryotes, in addition to trypanosome-specific modifications.

The hnRNP F/H homologue of Trypanosoma brucei is differentially expressed in the two life cycle stages of the parasite and regulates splicing and mRNA stability.

Trypanosomes are protozoan parasites that cycle between a mammalian host (bloodstream form) and an insect host, the Tsetse fly (procyclic stage). In trypanosomes, all mRNAs are trans-spliced as part of their maturation. Genome-wide analysis of trans-splicing indicates the existence of alternative trans-splicing, but little is known regarding RNA-binding proteins that participate in such regulation. In this study, we performed functional analysis of the Trypanosoma brucei heterogeneous nuclear ribonucleoproteins (hnRNP) F/H homologue, a protein known to regulate alternative splicing in metazoa. The hnRNP F/H is highly expressed in the bloodstream form of the parasite, but is also functional in the procyclic form. Transcriptome analyses of RNAi-silenced cells were used to deduce the RNA motif recognized by this protein. A purine rich motif, AAGAA, was enriched in both the regulatory regions flanking the 3' splice site and poly (A) sites of the regulated genes. The motif was further validated using mini-genes carrying wild-type and mutated sequences in the 3' and 5' UTRs, demonstrating the role of hnRNP F/H in mRNA stability and splicing. Biochemical studies confirmed the binding of the protein to this proposed site. The differential expression of the protein and its inverse effects on mRNA level in the two lifecycle stages demonstrate the role of hnRNP F/H in developmental regulation.

Basal splicing factors regulate the stability of mature mRNAs in trypanosomes.

Gene expression in trypanosomes is mainly regulated post-transcriptionally. Genes are transcribed as polycistronic mRNAs that are dissected by the concerted action of trans-splicing and polyadenylation. In trans-splicing, a common exon, the spliced leader, is added to all mRNAs from a small RNA. In this study, we examined by microarray analysis the transcriptome following RNAi silencing of the basal splicing factors U2AF65, SF1, and U2AF35. The transcriptome data revealed correlations between the affected genes and their splicing and polyadenylation signaling properties, suggesting that differential binding of these factors to pre-mRNA regulates trans-splicing and hence expression of specific genes. Surprisingly, all these factors were shown to affect not only splicing but also mRNA stability. Affinity purification of SF1 and U2AF35 complexes supported their role in mRNA stability. U2AF35 but not SF1 was shown to bind to ribosomes. To examine the role of splicing factors in mRNA stability, mutations were introduced into the polypyrimidine tract located in the 3' UTR of a mini-gene, and the results demonstrate that U2AF65 binds to such a site and controls the mRNA stability. We propose that transcripts carrying splicing signals in their 3' UTR bind the splicing factors and control their stability.

Two splicing factors carrying serine-arginine motifs, TSR1 and TSR1IP, regulate splicing, mRNA stability, and rRNA processing in Trypanosoma brucei.

In trypanosomes, mRNAs are processed by trans-splicing; in this process, a common exon, the spliced leader, is added to all mRNAs from a small RNA donor, the spliced leader RNA (SL RNA). However, little is known regarding how this process is regulated. In this study we investigated the function of two serine-arginine-rich proteins, TSR1 and TSR1IP, implicated in trans-splicing in Trypanosoma brucei. Depletion of these factors by RNAi suggested their role in both cis- and trans-splicing. Microarray was used to examine the transcriptome of the silenced cells. The level of hundreds of mRNAs was changed, suggesting that these proteins have a role in regulating only a subset of T. brucei mRNAs. Mass-spectrometry analyses of complexes associated with these proteins suggest that these factors function in mRNA stability, translation, and rRNA processing. We further demonstrate changes in the stability of mRNA as a result of depletion of the two TSR proteins. In addition, rRNA defects were observed under the depletion of U2AF35, TSR1, and TSR1IP, but not SF1, suggesting involvement of SR proteins in rRNA processing.

RNA-seq analysis of small RNPs in Trypanosoma brucei reveals a rich repertoire of non-coding RNAs.

The discovery of a plethora of small non-coding RNAs (ncRNAs) has fundamentally changed our understanding of how genes are regulated. In this study, we employed the power of deep sequencing of RNA (RNA-seq) to examine the repertoire of ncRNAs present in small ribonucleoprotein particles (RNPs) of Trypanosoma brucei, an important protozoan parasite. We identified new C/D and H/ACA small nucleolar RNAs (snoRNAs), as well as tens of putative novel non-coding RNAs; several of these are processed from trans-spliced and polyadenylated transcripts. The RNA-seq analysis provided information on the relative abundance of the RNAs, and their 5'- and 3'-termini. The study demonstrated that three highly abundant snoRNAs are involved in rRNA processing and highlight the unique trypanosome-specific repertoire of these RNAs. Novel RNAs were studied using in situ hybridization, association in RNP complexes, and 'RNA walk' to detect interaction with their target RNAs. Finally, we showed that the abundance of certain ncRNAs varies between the two stages of the parasite, suggesting that ncRNAs may contribute to gene regulation during the complex parasite's life cycle. This is the first study to provide a whole-genome analysis of the large repertoire of small RNPs in trypanosomes.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Machine learning-based predictive modelling for the enhancement of wine quality.

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis.

Identification of naturally occurring compounds as alternatives to radiation therapy for treatment of small cell lung cancer.

Radiation resistance is one of the major problems in the treatment of small cell lung cancer (SCLC). Most of these patients are given radiation as first-line treatment and it was observed that the initial response in these patients is very good. However, they show relapse in a few months which is also associated with resistance to treatment. Thus, targeting the mechanism by which these cells develop resistance could be an important strategy to improve the survival chances of these patients. From the RNA-Seq data analysis, it was identified that CHEK1 gene was overexpressed. Chk1 protein which is encoded by the CHEK1 gene is an important protein that is involved in radiation resistance in SCLC. It is known to favour the cells to deal with replicative stress. CHEK1 is the major cause for developing radiation resistance in SCLC. Thus, natural compounds that could also serve as potential inhibitors for Chk1 were explored. Accordingly; the compounds were screened based on ADME, docking and MM-GBSA scores. MD simulations were performed for the selected protein-ligand complexes and the results were compared to the co-crystallised ligand, 3-(indol-2-yl)indazole. The results showed that compound INC000033832986 could be a natural alternative to the commercial ligand for the prevention of SCLC.Communicated by Ramaswamy H. Sarma.

Small nucleolar RNA interference in Trypanosoma brucei: mechanism and utilization for elucidating the function of snoRNAs.

Expression of dsRNA complementary to small nucleolar RNAs (snoRNAs) in Trypanosoma brucei results in snoRNA silencing, termed snoRNAi. Here, we demonstrate that snoRNAi requires the nuclear TbDCL2 protein, but not TbDCL1, which is involved in RNA interference (RNAi) in the cytoplasm. snoRNAi depends on Argonaute1 (Slicer), and on TbDCL2, suggesting that snoRNA dicing and slicing takes place in the nucleus, and further suggesting that AGO1 is active in nuclear silencing. snoRNAi was next utilized to elucidate the function of an abundant snoRNA, TB11Cs2C2 (92 nt), present in a cluster together with the spliced leader associated RNA (SLA1) and snR30, which are both H/ACA RNAs with special nuclear functions. Using AMT-UV cross-linking and RNaseH cleavage, we provide evidence for the interaction of TB11Cs2C2 with the small rRNAs, srRNA-2 and srRNA-6, which are part of the large subunit (LSU) rRNA. snoRNAi of TB11Cs2C2 resulted in defects in generating srRNA-2 and LSUß rRNA. This is the first snoRNA described so far to engage in trypanosome-specific processing events.

Chimeric RNA Design Principles for RNA-Mediated Gene Fusion.

One common genetic alteration in cancer is gene fusion resulting from chromosomal translocations. The mechanisms that create such oncogenic fusion genes are not well understood. Previously, we provided the direct evidence that expression of a designed chimeric RNA can drive the formation of TMPRSS2-ERG gene fusion. Central to this RNA-mediated gene fusion mechanism is a proposed three-way junction formed by RNA/DNA hybrid and the intergenic DNA stem formed by target genes. In this study, we determined the important parameters for chimeric RNA-mediated gene fusion using TMPRSS2-ERG fusion gene as the model. Our results indicate that both the chimeric RNA lengths and the sizes of unpaired bulges play important roles in inducing TMPRSS2-ERG gene fusion. The optimal length of unpaired bulges was about 35 nt, while the optimal chimeric RNA length was about 50 nt for targeting. These observations were consistent regardless of the target locations within TMPRSS2 and ERG genes. These empirically determined parameters provide important insight for searching cellular RNAs that may initiate oncogenic fusion genes. The knowledge could also facilitate the development of useful genomic technology for manipulating mammalian genomes.

Complex entropy based encryption and decryption technique for securing medical images.

During medical picture transmission, the most pressing concern is security. Medical images must be encrypted since they are extremely sensitive. Watermarking, digital fingerprinting/signature, and encoding are some of the available image security techniques. Images and movies, for example, must be highly encrypted and decoded without losing any content information. Medical photos, for example, require extra protection, and protecting medical images is a critical issue when medical images and related patient information are transferred over public networks. This research work proposes a visual encryption strategy to secure medical pictures before being transmitted or stored in the cloud. This technique makes such pictures of unauthorized people unavailable and also maintains confidentiality, a prime safety requirement. The process made use of a pixel shuffling-based encryption technique and a secret key created from the image. In this research, we encrypted the medical image using modified Arnold Map Encryption and generated secret key values. Therefore, the image is encrypted, and henceforth it is decrypted as well. So this work gave us the encrypted image and decrypted image/original image as well. The modified Arnold Map Encryption tries to add more randomness, thus increasing the entropy of the image and thus makes it harder to decrypt. The modified Arnold Map Encryption is also compared to other algorithms such as Hyper Chaotic, Secure Hash Algorithm-13 (SHA-13), Ten Logistic Maps, Bakers Map, HenonMap, Cross Chaos Map, and 2D Logistic Map and shows better results in terms of encryption speed and Number of Pixel Change Rate (NPCR) value.

COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches.

Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20-50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner's advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes.

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

Analysis of spliceosomal proteins in Trypanosomatids reveals novel functions in mRNA processing.

In trypanosomatids, all mRNAs are processed via trans-splicing, although cis-splicing also occurs. In trans-splicing, a common small exon, the spliced leader (SL), which is derived from a small SL RNA species, is added to all mRNAs. Sm and Lsm proteins are core proteins that bind to U snRNAs and are essential for both these splicing processes. In this study, SmD3- and Lsm3-associated complexes were purified to homogeneity from Leishmania tarentolae. The purified complexes were analyzed by mass spectrometry, and 54 and 39 proteins were purified from SmD3 and Lsm complexes, respectively. Interestingly, among the proteins purified from Lsm3, no mRNA degradation factors were detected, as in Lsm complexes from other eukaryotes. The U1A complex was purified and mass spectrometry analysis identified, in addition to U1 small nuclear ribonucleoprotein (snRNP) proteins, additional co-purified proteins, including the polyadenylation factor CPSF73. Defects observed in cells silenced for U1 snRNP proteins suggest that the U1 snRNP functions exclusively in cis-splicing, although U1A also participates in polyadenylation and affects trans-splicing. The study characterized several trypanosome-specific nuclear factors involved in snRNP biogenesis, whose function was elucidated in Trypanosoma brucei. Conserved factors, such as PRP19, which functions at the heart of every cis-spliceosome, also affect SL RNA modification; GEMIN2, a protein associated with SMN (survival of motor neurons) and implicated in selective association of U snRNA with core Sm proteins in trypanosomes, is a master regulator of snRNP assembly. This study demonstrates the existence of trypanosomatid-specific splicing factors but also that conserved snRNP proteins possess trypanosome-specific functions.

Multiple roles for polypyrimidine tract binding (PTB) proteins in trypanosome RNA metabolism.

Trypanosomatid genomes encode for numerous proteins containing an RNA recognition motif (RRM), but the function of most of these proteins in mRNA metabolism is currently unknown. Here, we report the function of two such proteins that we have named PTB1 and PTB2, which resemble the mammalian polypyrimidine tract binding proteins (PTB). RNAi silencing of these factors indicates that both are essential for life. PTB1 and PTB2 reside mostly in the nucleus, but are found in the cytoplasm, as well. Microarray analysis performed on PTB1 and PTB2 RNAi silenced cells indicates that each of these factors differentially affects the transcriptome, thus regulating a different subset of mRNAs. PTB1 and PTB2 substrates were categorized bioinformatically, based on the presence of PTB binding sites in their 5' and 3' flanking sequences. Both proteins were shown to regulate mRNA stability. Interestingly, PTB proteins are essential for trans-splicing of genes containing C-rich polypyrimidine tracts. PTB1, but not PTB2, also affects cis-splicing. The specificity of binding of PTB1 was established in vivo and in vitro using a model substrate. This study demonstrates for the first time that trans-splicing of only certain substrates requires specific factors such as PTB proteins for their splicing. The trypanosome PTB proteins, like their mammalian homologs, represent multivalent RNA binding proteins that regulate mRNAs from their synthesis to degradation.