Targeted RNA editing: novel tools to study post-transcriptional regulation.

RNA binding proteins (RBPs) regulate nearly all post-transcriptional processes within cells. To fully understand RBP function, it is essential to identify their in vivo targets. Standard techniques for profiling RBP targets, such as crosslinking immunoprecipitation (CLIP) and its variants, are limited or suboptimal in some situations, e.g. when compatible antibodies are not available and when dealing with small cell populations such as neuronal subtypes and primary stem cells. This review summarizes and compares several genetic approaches recently designed to identify RBP targets in such circumstances. TRIBE (targets of RNA binding proteins identified by editing), RNA tagging, and STAMP (surveying targets by APOBEC-mediated profiling) are new genetic tools useful for the study of post-transcriptional regulation and RBP identification. We describe the underlying RNA base editing technology, recent applications, and therapeutic implications.

TDP-43 dysfunction restricts dendritic complexity by inhibiting CREB activation and altering gene expression.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two related neurodegenerative diseases that present with similar TDP-43 pathology in patient tissue. TDP-43 is an RNA-binding protein which forms aggregates in neurons of ALS and FTD patients as well as in a subset of patients diagnosed with other neurodegenerative diseases. Despite our understanding that TDP-43 is essential for many aspects of RNA metabolism, it remains obscure how TDP-43 dysfunction contributes to neurodegeneration. Interestingly, altered neuronal dendritic morphology is a common theme among several neurological disorders and is thought to precede neurodegeneration. We previously found that both TDP-43 overexpression (OE) and knockdown (KD) result in reduced dendritic branching of cortical neurons. In this study, we used TRIBE (targets of RNA-binding proteins identified by editing) as an approach to identify signaling pathways that regulate dendritic branching downstream of TDP-43. We found that TDP-43 RNA targets are enriched for pathways that signal to the CREB transcription factor. We further found that TDP-43 dysfunction inhibits CREB activation and CREB transcriptional output, and restoring CREB signaling rescues defects in dendritic branching. Finally, we demonstrate, using RNA sequencing, that TDP-43 OE and KD cause similar changes in the abundance of specific messenger RNAs, consistent with their ability to produce similar morphological defects. Our data therefore provide a mechanism by which TDP-43 dysfunction interferes with dendritic branching, and may define pathways for therapeutic intervention in neurodegenerative diseases.

Reliability and validity of the Chinese version of the LMC Skills, Confidence & Preparedness Index (SCPI) in patients with type 2 diabetes.

BACKGROUND: A variety of diabetes self-management instruments have been developed but few of them consist of the preparedness for diabetes self-management behavior. The novel psychometric evaluation tool "the LMC Skills, Confidence & Preparedness Index (SCPI)" measures three key aspects of a patient's diabetes self-management: knowledge of the skill, confidence in being able to perform skill and preparedness to implement the skill. The objective of this study was to translate, adapt and validate the SCPI for use in Chinese adult patients with type 2 diabetes. METHODS: This study followed the guideline recommended by the American Academy of Orthopaedic Surgeons Evidence Based Medicine Committee (AAOS) to indigenize the scale. Forward and back translation, and cross-cultural language debugging were completed according to the recommended steps. A convenience sample of Chinese patients with type 2 diabetes (n = 375) were recruited from a university-affiliated hospital in Shanghai. The validity (criterion, discriminant validity, and construct validity), reliability (internal consistency and test-retest reliability) and the interpretability of the instrument were examined. The content validity was calculated by experts' evaluation. RESULTS: The Chinese version of SCPI (C-SCPI) has good internal consistency with a Cronbach's alpha of 0.92. The ceiling effects of the preparedness subscales is 21%. The criterion validity of three dimensions of C-SCPI was established with significantly moderate correlations between the DKT, DES-SF and SDSCA (p < 0.05). The S-CVI of the whole scale was 0.83. Except for entry 21, the I-CVI values of all entries were greater than 0.78. The C-SCPI has also shown good discriminative validity with statistically significant differences between the patients with good and poor glycemic control. Confirmatory factor analysis showed that modified results indicate that the fitting degree of the model is good, χ2/df = 2.775, RMSEA = 0.069, CFI = 0.903, GFI = 0.873, TLI = 0.889, IFI = 0.904. The test-retest reliability coefficient was 0.61 (p < 0.01). CONCLUSION: We established a Chinese version of SCPI through translation and cross-cultural adaptation. The C-SCPI is reliable and valid for assessment of the level of self-management in Chinese patients with type 2 diabetes.

Mechanistic implications of enhanced editing by a HyperTRIBE RNA-binding protein.

We previously developed TRIBE, a method for the identification of cell-specific RNA-binding protein targets. TRIBE expresses an RBP of interest fused to the catalytic domain (cd) of the RNA-editing enzyme ADAR and performs adenosine-to-inosine editing on RNA targets of the RBP. However, target identification is limited by the low editing efficiency of the ADARcd. Here we describe HyperTRIBE, which carries a previously characterized hyperactive mutation (E488Q) of the ADARcd. HyperTRIBE identifies dramatically more editing sites, many of which are also edited by TRIBE but at a much lower editing frequency. HyperTRIBE therefore more faithfully recapitulates the known binding specificity of its RBP than TRIBE. In addition, separating RNA binding from the enhanced editing activity of the HyperTRIBE ADAR catalytic domain sheds light on the mechanism of ADARcd editing as well as the enhanced activity of the HyperADARcd.

Phylogenetic analysis of CDK and cyclin proteins in premetazoan lineages.

BACKGROUND: The molecular history of animal evolution from single-celled ancestors remains a major question in biology, and little is known regarding the evolution of cell cycle regulation during animal emergence. In this study, we conducted a comprehensive evolutionary analysis of CDK and cyclin proteins in metazoans and their unicellular relatives. RESULTS: Our analysis divided the CDK family into eight subfamilies. Seven subfamilies (CDK1/2/3, CDK5, CDK7, CDK 20, CDK8/19, CDK9, and CDK10/11) are conserved in metazoans and fungi, with the remaining subfamily, CDK4/6, found only in eumetazoans. With respect to cyclins, cyclin C, H, L, Y subfamilies, and cyclin K and T as a whole subfamily, are generally conserved in animal, fungi, and amoeba Dictyostelium discoideum. In contrast, cyclin subfamilies B, A, E, and D, which are cell cycle-related, have distinct evolutionary histories. The cyclin B subfamily is generally conserved in D. discoideum, fungi, and animals, whereas cyclin A and E subfamilies are both present in animals and their unicellular relatives such as choanoflagellate Monosiga brevicollis and filasterean Capsaspora owczarzaki, but are absent in fungi and D. discoideum. Although absent in fungi and D. discoideum, cyclin D subfamily orthologs can be found in the early-emerging, non-opisthokont apusozoan Thecamonas trahens. Within opisthokonta, the cyclin D subfamily is conserved only in eumetazoans, and is absent in fungi, choanoflagellates, and the basal metazoan Amphimedon queenslandica. CONCLUSIONS: Our data indicate that the CDK4/6 subfamily and eumetazoans emerged simultaneously, with the evolutionary conservation of the cyclin D subfamily also tightly linked with eumetazoan appearance. Establishment of the CDK4/6-cyclin D complex may have been the key step in the evolution of cell cycle control during eumetazoan emergence.

ERNet: Edge Regularization Network for Cerebral Vessel Segmentation in Digital Subtraction Angiography Images.

Stroke is a leading cause of disability and fatality in the world, with ischemic stroke being the most common type. Digital Subtraction Angiography images, the gold standard in the operation process, can accurately show the contours and blood flow of cerebral vessels. The segmentation of cerebral vessels in DSA images can effectively help physicians assess the lesions. However, due to the disturbances in imaging parameters and changes in imaging scale, accurate cerebral vessel segmentation in DSA images is still a challenging task. In this paper, we propose a novel Edge Regularization Network (ERNet) to segment cerebral vessels in DSA images. Specifically, ERNet employs the erosion and dilation processes on the original binary vessel annotation to generate pseudo-ground truths of False Negative and False Positive, which serve as constraints to refine the coarse predictions based on their mapping relationship with the original vessels. In addition, we exploit a Hybrid Fusion Module based on convolution and transformers to extract local features and build long-range dependencies. Moreover, to support and advance the open research in the field of ischemic stroke, we introduce FPDSA, the first pixel-level semantic segmentation dataset for cerebral vessels. Extensive experiments on FPDSA illustrate the leading performance of our ERNet.

DIAS: A dataset and benchmark for intracranial artery segmentation in DSA sequences.

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.11401368 and https://github.com/lseventeen/DIAS.

Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation.

Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity. The code is available at: https://github.com/lseventeen/FR-UNet.

TRIBE editing reveals specific mRNA targets of eIF4E-BP in Drosophila and in mammals.

4E-BP (eIF4E-BP) represses translation initiation by binding to the 5' cap-binding protein eIF4E and inhibiting its activity. Although 4E-BP has been shown to be important in growth control, stress response, cancer, neuronal activity, and mammalian circadian rhythms, it is not understood how it preferentially represses a subset of mRNAs. We successfully used HyperTRIBE (targets of RNA binding proteins identified by editing) to identify in vivo 4E-BP mRNA targets in both Drosophila and mammals under conditions known to activate 4E-BP. The protein associates with specific mRNAs, and ribosome profiling data show that mTOR inhibition changes the translational efficiency of 4E-BP TRIBE targets more substantially compared to nontargets. In both systems, these targets have specific motifs and are enriched in translation-related pathways, which correlate well with the known activity of 4E-BP and suggest that it modulates the binding specificity of eIF4E and contributes to mTOR translational specificity.

The optimal delivery time and order quantity in an oligopoly market with time-sensitive customers.

With the development of e-commerce, delivery time is regarded as a key competitive advantage in an oligopoly market, as shortening the delivery time can stimulate demand for products. Many firms adopt a variety of strategies to shorten delivery time, and holding sufficient inventory is reported as an effective way. This study integrates a market share attraction model based on delivery time competition with the traditional inventory model to determine the optimal delivery time and order quantity. With the use of supermodular game method, we investigate the effect of changes in marketing and operations factors on the equilibrium delivery time and order quantity in non-dominated and dominated oligopolistic markets. The results reveal that the equilibrium delivery time and order quantity exhibit a directional response to changes in marketing and operations factors, and the response differs between the non-dominated oligopoly and the dominated oligopoly. Furthermore, under a cooperative oligopolistic market with asymmetry, it is beneficial for the firms with high competitive strength to adopt the delivery time strategy, but it fails to do so for the firm with the low competitive strength. Lastly, numerical analysis suggests that marketing factors play a more important role in affecting equilibrium measures than operations factors.

Identification of RNA-binding protein targets with HyperTRIBE.

RNA-binding proteins (RBPs) accompany RNA from birth to death, affecting RNA biogenesis and functions. Identifying RBP-RNA interactions is essential to understanding their complex roles in different cellular processes. However, detecting in vivo RNA targets of RBPs, especially in a small number of discrete cells, has been a technically challenging task. We previously developed a novel technique called TRIBE (targets of RNA-binding proteins identified by editing) to overcome this problem. TRIBE expresses a fusion protein consisting of a queried RBP and the catalytic domain of the RNA-editing enzyme ADAR (adenosine deaminase acting on RNA) (ADARcd), which marks target RNA transcripts by converting adenosine to inosine near the RBP binding sites. These marks can be subsequently identified via high-throughput sequencing. In spite of its usefulness, TRIBE is constrained by a low editing efficiency and editing-sequence bias from the ADARcd. Therefore, we developed HyperTRIBE by incorporating a previously characterized hyperactive mutation, E488Q, into the ADARcd. This strategy increases the editing efficiency and reduces sequence bias, which markedly increases the sensitivity of this technique without sacrificing specificity. HyperTRIBE provides a more powerful strategy for identifying RNA targets of RBPs with an easy experimental and computational protocol at low cost, that can be performed not only in flies, but also in mammals. The HyperTRIBE experimental protocol described below can be carried out in cultured Drosophila S2 cells in 1 week, using tools available in a common molecular biology laboratory; the computational analysis requires 3 more days.

Meta-analysis of gene expression profiles indicates genes in spliceosome pathway are up-regulated in hepatocellular carcinoma (HCC).

Hepatocellular carcinoma (HCC) is among the commonest kind of malignant tumors, which accounts for more than 500,000 cases of newly diagnosed cancer annually. Many microarray studies for identifying differentially expressed genes (DEGs) in HCC have been conducted, but results have varied across different studies. Here, we performed a meta-analysis of publicly available microarray Gene Expression Omnibus datasets, which covers five independent studies, containing 753 HCC samples and 638 non-tumor liver samples. We identified 192 DEGs that were consistently up-regulated in HCC vs. normal liver tissue. For the 192 up-regulated genes, we performed Kyoto Encyclopedia of Genes and Genomes pathway analysis. To our surprise, besides several cell growth-related pathways, spliceosome pathway was also up-regulated in HCC. For further exploring the relationship between spliceosome pathway and HCC, we investigated the expression data of spliceosome pathway genes in 15 independent studies in Nextbio database ( https://www.nextbio.com/b/nextbioCorp.nb ). It was found that many genes of spliceosome pathway such as HSPA1A, SNRPE, SF3B2, SF3B4 and TRA2A genes which we identified to be up-regulated in our meta-analysis were generally overexpressed in HCC. At last, using real-time PCR, we also found that BUD31, SF3B2, SF3B4, SNRPE, SPINK1, TPA2A and HSPA1A genes are significantly up-regulated in clinical HCC samples when compared to the corresponding non-tumorous liver tissues. Our study for the first time indicates that many genes of spliceosome pathway are up-regulated in HCC. This finding might put new insights for people's understanding about the relationship of spliceosome pathway and HCC.

Ubiquitin Conjugating Enzyme Ubc9 is Involved in Protein Degradation of Redox Factor-1 (Ref-1).

Redox factor-1 (Ref-1) is a bifunctional protein playing an important role in both cellular redox regulation and DNA apurinic/apyrimidinic sites' repair. To find Ref-1interacting proteins (Rips), a yeast two-hybrid screening was performed by using Ref-1 redox domain as the 'bait', and five positive clones were obtained. One of them (Rip3) was identified to be the ubiquitin-conjugating enzyme Ubc9. Simultaneous overexpression of Ubc9 in Hela cells dramatically inhibited the enhancement of AP-1 reporter gene by Ref-1. Western blot indicated that the protein level of Ref-1 dropped down as the result of simultaneous overexpression of Ubc9. These results suggest that Ubc9 is involved in the protein degradation of Ref-1, resulting in the downregulation of Ref-1 physiological function.