Development of an assay system for the analysis of host RISC activity in the presence of a potyvirus RNA silencing suppressor, HC-Pro.

BACKGROUND: To investigate the mechanism of RNA silencing suppression, the genetic transformation of viral suppressors of RNA silencing (VSRs) in Arabidopsis integrates ectopic VSR expression at steady state, which overcomes the VSR variations caused by different virus infections or limitations of host range. Moreover, identifying the insertion of the transgenic VSR gene is necessary to establish a model transgenic plant for the functional study of VSR. METHODS: Developing an endogenous AGO1-based in vitro RNA-inducing silencing complex (RISC) assay prompts further investigation into VSR-mediated suppression. Three P1/HC-Pro plants from turnip mosaic virus (TuMV) (P1/HC-ProTu), zucchini yellow mosaic virus (ZYMV) (P1/HC-ProZy), and tobacco etch virus (TEV) (P1/HC-ProTe) were identified by T-DNA Finder and used as materials for investigations of the RISC cleavage efficiency. RESULTS: Our results indicated that the P1/HC-ProTu plant has slightly lower RISC activity than P1/HC-ProZy plants. In addition, the phenomena are consistent with those observed in TuMV-infected Arabidopsis plants, which implies that HC-ProTu could directly interfere with RISC activity. CONCLUSIONS: In this study, we demonstrated the application of various plant materials in an in vitro RISC assay of VSR-mediated RNA silencing suppression.

Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions.

Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge's center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%.

Investigation of P1/HC-Pro-Mediated ABA/Calcium Signaling Responses via Gene Silencing through High- and Low-Throughput RNA-seq Approaches.

The P1/HC-Pro viral suppressor of potyvirus suppresses posttranscriptional gene silencing (PTGS). The fusion protein of P1/HC-Pro can be cleaved into P1 and HC-Pro through the P1 self-cleavage activity, and P1 is necessary and sufficient to enhance PTGS suppression of HC-Pro. To address the modulation of gene regulatory relationships induced by turnip mosaic virus (TuMV) P1/HC-Pro (P1/HC-ProTu), a comparative transcriptome analysis of three types of transgenic plants (P1Tu, HC-ProTu, and P1/HC-ProTu) were conducted using both high-throughput (HTP) and low-throughput (LTP) RNA-Seq strategies. The results showed that P1/HC-ProTu disturbed the endogenous abscisic acid (ABA) accumulation and genes in the signaling pathway. Additionally, the integrated responses of stress-related genes, in particular to drought stress, cold stress, senescence, and stomatal dynamics, altered the expressions by the ABA/calcium signaling. Crosstalk among the ABA, jasmonic acid, and salicylic acid pathways might simultaneously modulate the stress responses triggered by P1/HC-ProTu. Furthermore, the LTP network analysis revealed crucial genes in common with those identified by the HTP network in this study, demonstrating the effectiveness of the miniaturization of the HTP profile. Overall, our findings indicate that P1/HC-ProTu-mediated suppression in RNA silencing altered the ABA/calcium signaling and a wide range of stress responses.

The Species and Origin of Shark Fins in Taiwan's Fishing Ports, Markets, and Customs Detention: A DNA Barcoding Analysis.

The increasing consumption of shark products, along with the shark's fishing vulnerabilities, has led to the decrease in certain shark populations. In this study we used a DNA barcoding method to identify the species of shark landings at fishing ports, shark fin products in retail stores, and shark fins detained by Taiwan customs. In total we identified 23, 24, and 14 species from 231 fishing landings, 316 fin products, and 113 detained shark fins, respectively. All the three sample sources were dominated by Prionace glauca, which accounted for more than 30% of the collected samples. Over 60% of the species identified in the fin products also appeared in the port landings, suggesting the domestic-dominance of shark fin products in Taiwan. However, international trade also contributes a certain proportion of the fin product markets, as four species identified from the shark fin products are not found in Taiwan's waters, and some domestic-available species were also found in the customs-detained sample. In addition to the species identification, we also found geographical differentiation in the cox1 gene of the common thresher sharks (Alopias vulpinus), the pelagic thresher shark (A. pelagicus), the smooth hammerhead shark (Sphyrna zygaena), and the scalloped hammerhead shark (S. lewini). This result might allow fishing authorities to more effectively trace the origins as well as enforce the management and conservation of these sharks.

High-throughput transcriptome analysis of the leafy flower transition of Catharanthus roseus induced by peanut witches'-broom phytoplasma infection.

Peanut witches'-broom (PnWB) phytoplasma are obligate bacteria that cause leafy flower symptoms in Catharanthus roseus. The PnWB-mediated leafy flower transitions were studied to understand the mechanisms underlying the pathogen-host interaction; however, our understanding is limited because of the lack of information on the C. roseus genome. In this study, the whole-transcriptome profiles from healthy flowers (HFs) and stage 4 (S4) PnWB-infected leafy flowers of C. roseus were investigated using next-generation sequencing (NGS). More than 60,000 contigs were generated using a de novo assembly approach, and 34.2% of the contigs (20,711 genes) were annotated as putative genes through name-calling, open reading frame determination and gene ontology analyses. Furthermore, a customized microarray based on this sequence information was designed and used to analyze samples further at various stages of PnWB infection. In the NGS profile, 87.8% of the genes showed expression levels that were consistent with those in the microarray profiles, suggesting that accurate gene expression levels can be detected using NGS. The data revealed that defense-related and flowering gene expression levels were altered in S4 PnWB-infected leafy flowers, indicating that the immunity and reproductive stages of C. roseus were compromised. The network analysis suggested that the expression levels of >1,000 candidate genes were highly associated with CrSVP1/2 and CrFT expression, which might be crucial in the leafy flower transition. In conclusion, this study provides a new perspective for understanding plant pathology and the mechanisms underlying the leafy flowering transition caused by host-pathogen interactions through analyzing bioinformatics data obtained using a powerful, rapid high-throughput technique.

Predicting RNA-binding residues from evolutionary information and sequence conservation.

BACKGROUND: RNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments. RESULTS: The proposed prediction framework named "ProteRNA" combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546. CONCLUSIONS: This article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.

BACKGROUND: Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research. RESULTS: In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode. CONCLUSION: Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

DNA-binding residues and binding mode prediction with binding-mechanism concerned models.

BACKGROUND: Protein-DNA interactions are essential for fundamental biological activities including DNA transcription, replication, packaging, repair and rearrangement. Proteins interacting with DNA can be classified into two categories of binding mechanisms - sequence-specific and non-specific binding. Protein-DNA specific binding provides a mechanism to recognize correct nucleotide base pairs for sequence-specific identification. Protein-DNA non-specific binding shows sequence independent interaction for accelerated targeting by interacting with DNA backbone. Both sequence-specific and non-specific binding residues contribute to their roles for interaction. RESULTS: The proposed framework has two stage predictors: DNA-binding residues prediction and binding mode prediction. In the first stage - DNA-binding residues prediction, the predictor for DNA specific binding residues achieves 96.45% accuracy with 50.14% sensitivity, 99.31% specificity, 81.70% precision, and 62.15% F-measure. The predictor for DNA non-specific binding residues achieves 89.14% accuracy with 53.06% sensitivity, 95.25% specificity, 65.47% precision, and 58.62% F-measure. While combining prediction results of sequence-specific and non-specific binding residues with OR operation, the predictor achieves 89.26% accuracy with 56.86% sensitivity, 95.63% specificity, 71.92% precision, and 63.51% F-measure. In the second stage, protein-DNA binding mode prediction achieves 75.83% accuracy while using support vector machine with multi-class prediction. CONCLUSION: This article presents the design of a sequence based predictor aiming to identify sequence-specific and non-specific binding residues in a transcription factor with DNA binding-mechanism concerned. The protein-DNA binding mode prediction was introduced to help improve DNA-binding residues prediction. In addition, the results of this study will help with the design of binding-mechanism concerned predictors for other families of proteins interacting with DNA.

ProteDNA: a sequence-based predictor of sequence-specific DNA-binding residues in transcription factors.

This article presents the design of a sequence-based predictor named ProteDNA for identifying the sequence-specific binding residues in a transcription factor (TF). Concerning protein-DNA interactions, there are two types of binding mechanisms involved, namely sequence-specific binding and nonspecific binding. Sequence-specific bindings occur between protein sidechains and nucleotide bases and correspond to sequence-specific recognition of genes. Therefore, sequence-specific bindings are essential for correct gene regulation. In this respect, ProteDNA is distinctive since it has been designed to identify sequence-specific binding residues. In order to accommodate users with different application needs, ProteDNA has been designed to operate under two modes, namely, the high-precision mode and the balanced mode. According to the experiments reported in this article, under the high-precision mode, ProteDNA has been able to deliver precision of 82.3%, specificity of 99.3%, sensitivity of 49.8% and accuracy of 96.5%. Meanwhile, under the balanced mode, ProteDNA has been able to deliver precision of 60.8%, specificity of 97.6%, sensitivity of 60.7% and accuracy of 95.4%. ProteDNA is available at the following websites: http://protedna.csbb.ntu.edu.tw/, http://protedna.csie.ntu.edu.tw/, http://bio222.esoe.ntu.edu.tw/ProteDNA/.