Highlights d RNA-BisSeq revealed a dynamic RNA m 5 C landscape during zebrafish embryogenesis d Ybx1 preferentially recognizes m 5 C-modified mRNAs d Ybx1 deficiency leads to early gastrulation defects in zebrafish embryos d Ybx1 and Pabpc1a coordinately regulate m 5 C-modified maternal mRNA stability

Human hnRNP A2/B1 is an RNA-binding protein that plays important roles in many biological processes, including maturation, transport, and metabolism of mRNA, and gene regulation of long noncoding RNAs. hnRNP A2/B1 was reported to control the microRNAs sorting to exosomes and promote primary microRNA processing as a potential m6A “reader.” hnRNP A2/B1 contains two RNA recognition motifs that provide sequence-specific recognition of RNA substrates. Here, we determine crystal structures of tandem RRM domains of hnRNP A2/B1 in complex with various RNA substrates, elucidating specific recognitions of AGG and UAG motifs by RRM1 and RRM2 domains, respectively. Further structural and biochemical results demonstrate multivariant binding modes for sequence-diversified RNA substrates, supporting a RNA matchmaker mechanism in hnRNP A2/B1 function. Moreover, our studies in combination with bioinformatic analysis suggest that hnRNP A2/B1 may mediate effects of m6A through a “m6A switch” mechanism, instead of acting as a direct “reader” of m6A modification.

In addition to storage of genetic information, DNA can also catalyze various reactions. RNA-cleaving DNAzymes are the catalytic DNAs discovered the earliest, and they can cleave RNAs in a sequence-specific manner. Owing to their great potential in medical therapeutics, virus control, and gene silencing for disease treatments, RNA-cleaving DNAzymes have been extensively studied; however, the mechanistic understandings of their substrate recognition and catalysis remain elusive. Here, we report three catalytic form 8–17 DNAzyme crystal structures. 8–17 DNAzyme adopts a V-shape fold, and the Pb2+ cofactor is bound at the pre-organized pocket. The structures with Pb2+ and the modification at the cleavage site captured the pre-catalytic state of the RNA cleavage reaction, illustrating the unexpected Pb2+-accelerated catalysis, intrinsic tertiary interactions, and molecular kink at the active site. Our studies reveal that DNA is capable of forming a compacted structure and that the functionality-limited bio-polymer can have a novel solution for a functional need in catalysis.

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Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thoracic Computed Tomography (CT) and nodule location. However, these studies cannot tell how the CNN works in terms of predicting the malignancy of the given nodule, e.g., it's hard to conclude that whether the region within the nodule or the contextual information matters according to the output of the CNN. In this paper, we propose an interpretable and multi-task learning CNN -Joint learning for Pulmonary Nodule Segmentation Attributes and Malignancy Prediction (PN-SAMP). It is able to not only accurately predict the malignancy of lung nodules, but also provide semantic high-level attributes as well as the areas of detected nodules. Moreover, the combination of nodule segmentation, attributes and malignancy prediction is helpful to improve the performance of each single task. In addition, inspired by the fact that radiologists often change window widths and window centers to help to make decision on uncertain nodules, PN-SAMP mixes multiple WW/WC together to gain information for the raw CT input images. To verify the effectiveness of the proposed method, the evaluation is implemented on the public LIDC-IDRI dataset, which is one of the largest dataset for lung nodule malignancy prediction. Experiments indicate that the proposed PN-SAMP achieves significant improvement with respect to lung nodule classification, and promising performance on lung nodule segmentation and attribute learning, compared with the-state-of-the-art methods.