Publications by authors named "Ya-Lan Tan"

RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the reconstruction of full atomic structures is desirable.

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Article Synopsis
  • * In the past two decades, significant attention has been focused on RNA structure prediction, developing computational models to determine RNA's 3D shapes.
  • * This review highlights recent progress in RNA structure modeling, covering prediction methods, evaluation of near-native structures, and refinement techniques, while also providing insights for future research directions.
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DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures.

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RNA pseudoknots are a kind of important tertiary motif, and the structures and stabilities of pseudoknots are generally critical to the biological functions of RNAs with the motifs. In this work, we have carefully refined our previously developed coarse-grained model with salt effect through involving a new coarse-grained force field and a replica-exchange Monte Carlo algorithm, and employed the model to predict structures and stabilities of complex RNA pseudoknots in ion solutions beyond minimal H-type pseudoknots. Compared with available experimental data, the newly refined model can successfully predict 3D structures from sequences for the complex RNA pseudoknots including SARS-CoV-2 programming-1 ribosomal frameshifting element and Zika virus xrRNA, and can reliably predict the thermal stabilities of RNA pseudoknots with various sequences and lengths over broad ranges of monovalent/divalent salts.

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Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation.

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The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures.

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The three-dimensional (3D) structure and stability of DNA are essential to understand/control their biological functions and aid the development of novel materials. In this work, we present a coarse-grained (CG) model for DNA based on the RNA CG model proposed by us, to predict 3D structures and stability for both dsDNA and ssDNA from the sequence. Combined with a Monte Carlo simulated annealing algorithm and CG force fields involving the sequence-dependent base-pairing/stacking interactions and an implicit electrostatic potential, the present model successfully folds 20 dsDNAs (≤52nt) and 20 ssDNAs (≤74nt) into the corresponding native-like structures just from their sequences, with an overall mean RMSD of 3.

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Article Synopsis
  • Understanding RNA 3D structures is vital for grasping their biological functions, but current prediction models struggle with longer or complex RNAs.
  • A new model, FebRNA, has been developed that assembles RNA 3D structures using coarse-grained fragment ensembles through a four-step process.
  • FebRNA has shown reliable predictions for various complex RNA structures and is accessible for use online at its GitHub page.
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Potassium (K+) is one of the essential macronutrients for plant growth and development. However, the available K+ concentration in soil is relatively low. Plant roots can perceive low K+ (LK) stress, then enhance high-affinity K+ uptake by activating H+-ATPases in root cells, but the mechanisms are still unclear.

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Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation.

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Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations.

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  • Plants require potassium (K) for essential homeostasis, and understanding how they sense and respond to K availability is crucial for sustainable agriculture.
  • The study identifies a specialized area in plant roots called the K-sensing niche (KSN) where drops in K levels trigger reactive oxygen species (ROS) signals that coordinate responses to K deficiency.
  • The findings reveal a signaling pathway involving CIF peptides and specific receptor complexes that activate processes to enhance K uptake and root maturation, ensuring that plants maintain K homeostasis effectively.
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  • A study was conducted on 33 children diagnosed with congenital choledochal malformation (CM) to analyze the effects of early diagnosis and treatment between prenatally and postnatally diagnosed patients.
  • The results showed that postnatally diagnosed patients experienced more symptoms like abdominal pain and vomiting, as well as more severe histopathological findings compared to those diagnosed prenatally.
  • Despite the prenatally diagnosed group having longer surgery times and longer recovery periods, their overall hospitalization was shorter than that of the postnatally diagnosed patients.
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  • - This study analyzed data from 183 neonates with pneumothorax at Children's Hospital of Soochow University between 2015 and 2018, focusing on factors that predict the need for chest drainage.
  • - Pneumothorax mainly occurred in the first 24 hours after birth, with significant predictors for needing drainage identified as lung collapse on X-ray, chest retractions, cyanosis, and frothing from the mouth.
  • - The findings suggest that assessing these predictive factors can help healthcare providers make better treatment decisions and potentially enhance outcomes for affected neonates.
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RNA kissing complexes are essential for genomic RNA dimerization and regulation of gene expression, and their structures and stability are critical to their biological functions. In this work, we used our previously developed coarse-grained model with an implicit structure-based electrostatic potential to predict three-dimensional (3D) structures and stability of RNA kissing complexes in salt solutions. For extensive RNA kissing complexes, our model shows great reliability in predicting 3D structures from their sequences, and our additional predictions indicate that the model can capture the dependence of 3D structures of RNA kissing complexes on monovalent/divalent ion concentrations.

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  • Knowledge-based statistical potentials are effective for evaluating and predicting protein structures, but there is a lack of comprehensive research on reference states specifically for RNA structure evaluation.
  • This study created six statistical potentials using different reference states from protein analysis and tested them on three RNA datasets, finding that the finite-ideal-gas and random-walk-chain methods performed best overall.
  • The results indicate that the effectiveness of these potentials is influenced by both the quality of the training sets used and the specific origins of the RNA test sets, with generally poor performance observed for realistic RNA test subsets.
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Objective: The aims of this study were to highlight some epidemiological aspects of intussusception cases younger than 48 months and to develop a forecasting model for the occurrence of intussusception in children younger than 48 months in Suzhou.

Design: A retrospective study of intussusception cases that occurred between January 2007 and December 2017.

Setting: Retrospective chart reviews of intussusception paediatric patients in a large Children's hospital in South-East China were performed.

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Article Synopsis
  • Double-stranded RNAs (dsRNAs) are crucial for cell metabolism, and understanding their 3D structure, stability, and flexibility in salt solutions helps clarify their biological roles.* -
  • This study expands a coarse-grained model to accurately predict the 3D structures and thermal stability of dsRNAs in different ion conditions, improving predictions by including electrostatic potential.* -
  • The model shows that the thermal stability of dsRNAs varies based on their length and sequence, and it effectively correlates predictions of dsRNA flexibility in salt solutions with experimental data.*
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RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions.

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We investigated the expression of Toll-like receptor 4 (TLR4) in the acute phase of intestinal I/R injury during intussusception and evaluated whether anti-TLR4 antibody-conjugated lead sulfide quantum dots (TLR4-PbS QDs) could be used to detect and monitor the injury. We first established a mouse model of I/R injury during intussusception. TLR-PbS QDs were then intravenously administered to intestinal I/R injured mice and visualized using whole-body fluorescence imaging in the second near-infrared window (NIR-II).

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Article Synopsis
  • The study aimed to evaluate the frequency and clinical features of recurrent intussusception in children, focusing on the risk factors that contribute to these recurring episodes.
  • A retrospective analysis was conducted on 1,007 intussusception cases over five years at a children's hospital, examining patient demographics, symptoms, and recurrence.
  • Key findings indicated that factors like age over one year, symptom duration less than 12 hours, and right abdominal mass location were significantly associated with recurrent intussusception, with vomiting also playing a role when pathological lead points were involved.
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