Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. ...Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC). In this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes. Furthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.
Transcription factors (TFs) recognize and bind to specific DNA sequences, thereby altering the chromatin structure and regulating transcription. TFs aid in the formation of a guide genome that ...facilitates the expression of genes under complex regulation. Understanding the underlying mechanism that mediates the TF-led regulation of gene expression is a popular topic in current genomic research. However, identifying the precise TF binding site (TFBS) and the specific role of the TFs in transcriptional regulation is challenging. This article summarizes the status of research concerning the prediction of TFBS. First, the experimental methods for identifying TFBS have been summarized by accessing related databases. Second, the machine learning methods for predicting TFBS, especially deep learning, have been summarized. Finally, the study elaborates on the main challenges faced in TFBS prediction. The purpose of this article is to provide researchers with a comprehensively understand the prediction of TFBS and to promote further development in this field.
Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing ...their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions.
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS ...have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts the convolution neural network to extract the data feature and optimize the pre-trained model. In particular, the model obtains faster training and prediction due to the embedded attention mechanism. For datasets, we take ATAC-seq, ChIP-seq, and DNA sequences data for the pre-trained model, and single-cell ATAC-seq data is used to predict the TF binding graph in the given cell. We verify the interpretability of the model through ablation experiments and sensitivity analysis. IscPAM can efficiently predict the combination of whole genome transcription factors in single cells and study cellular heterogeneity through chromatin accessibility of related diseases.
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•Present an interpretable deep learning neural network model for predicting single-cell TFBS.•Integrate the channel attention mechanism to accelerate the data feature extraction process.•Verified the interpretability of the model through ablation and sensitivity analysis.
Transcription factors (TFs) can regulate gene expression by recognizing specific cis-regulatory elements in DNA sequences. TF-DNA binding prediction has become a fundamental step in comprehending the ...underlying cis-regulation mechanism. Since a particular genome region is bound depending on multiple features, such as the arrangement of nucleotides, DNA shape, and an epigenetic mechanism, many researchers attempt to develop computational methods to predict TF binding sites (TFBSs) based on various genomic features. This paper provides a comprehensive compendium to better understand TF-DNA binding from genomic features. We first summarize the commonly used datasets and data processing manners. Subsequently, we classify current deep learning methods in TFBS prediction according to their utilized genomic features and analyze each technique’s merit and weakness. Furthermore, we illustrate the functional consequences characterization of TF-DNA binding by prioritizing noncoding variants in identified motif instances. Finally, the challenges and opportunities of deep learning in TF-DNA binding prediction are discussed. This survey can bring valuable insights for researchers to study the modeling of TF-DNA binding.
•Classify and analyze TFBS prediction models according to genomic features.•Characterization of functional consequences of TF binding are summarized.•Challenges and opportunities of studying TF-DNA binding are discussed.
Interpreting single-cell chromatin accessibility data is crucial for understanding intercellular heterogeneity regulation. Despite the progress in computational methods for analyzing this data, there ...is still a lack of a comprehensive analytical framework and a user-friendly online analysis tool. To fill this gap, we developed a pre-trained deep learning-based framework, single-cell auto-correlation transformers (scAuto), to overcome the challenge. Following DNABERT’s methodology of pre-training and fine-tuning, scAuto learns a general understanding of DNA sequence’s grammar by being pre-trained on unlabeled human genome via self-supervision; it is then transferred to the single-cell chromatin accessibility analysis task of scATAC-seq data for supervised fine-tuning. We extensively validated scAuto on the Buenrostro2018 dataset, demonstrating its superior performance on chromatin accessibility prediction, single-cell clustering, and data denoising. Based on scAuto, we further developed an interactive web server for single-cell chromatin accessibility data analysis. It integrates tutorial-style interfaces for those with limited programming skills. The platform is accessible at http://zhanglab.icaup.cn. To our knowledge, this work is expected to help analyze single-cell chromatin accessibility data and facilitate the development of precision medicine.
•Present a framework for single-cell chromatin accessibility analysis.•Develop an online analysis platform, scAuto.•Conduct extensive experiments and achieve the state-of-the-art performance.
The electrothermal effect of hysteroscopic bipolar electrosurgical resection may cause damage to the endometrium, leading to intrauterine adhesion (IUA). Although some studies have demonstrated the ...efficacy and feasibility of auto-cross-linked hyaluronic (ACP) gel in preventing IUAs, controversy over its use continues. In this randomized controlled multi-center 2-arm parallel trial, we aimed to examine the efficacy and safety of ACP gel in preventing IUA after hysteroscopic electrosurgical resection and facilitate pregnancy in patients.
Patients from 4 centers in China were randomly assigned (1:1) to receive an intrauterine infusion of ACP gel or nothing after hysteroscopic electrosurgical resection. The randomization assignment was generated by computer and kept in a sealed envelope. A second-look hysteroscopy was performed within 3 months of the surgery.
From June 2018 to May 2021, 200 patients were recruited. Ultimately, 82 patients in both groups were included in the result analysis. The baseline characteristics were comparable. The outcomes were assessed by using per-protocol analysis. The incidence of IUA in the ACP gel group was lower than that in the control group 3.66%
10.98%, risk ratio (RR) =0.333, 95% confidence interval (CI): 0.094-1.187, P=0.072, and the planned pregnancy rate was higher than that of the control group (60.98%
40.54%, RR =1.504, 95% CI: 0.949-2.384, P=0.071), but the difference was not statistically significant. There was no significant difference in menstruation change. Menstrual volume remained unchanged in most cases (86.59% in ACP gel group
89.02% in the control group, RR =0.877, 95% CI: 0.877-1.109, P=0.815). Menstrual volume decreased in 10 women in the ACP gel group and 8 in the control group (12.20%
9.76%, RR =1.250, 95% CI: 0.520-3.007, P=0.617). No adverse effects were observed after the ACP administration.
The present study showed that the use of ACP gel appeared to reduce both the tendency of IUA and American Fertility Society (AFS) scores and improve the subsequent pregnancy rate during hysteroscopic electrosurgical resection when treating polyps, fibroids, and uterine septum. ACP might be recommended to prevent IUA after such surgery. Further studies should be conducted with larger numbers of participants.
Chinese Clinical Trial Registry ChiCTR2100047165.
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that is spreading rapidly, which seriously impacts global public health and economy. Thus, developing ...effective drugs remains urgent. We identify two potent antibodies, nCoVmab1 and nCoVmab2, targeting the SARS-CoV-2 spike protein receptor-binding domain (RBD) with high affinities from a naïve human phage-displayed Fab library. nCoVmab1 and nCoVmab2 neutralize authentic SARS-CoV-2 with picomolar and nanomolar IC
50
values, respectively. No detectable defects of nCoVmab1 and nCoVmab2 are found during the preliminary druggability evaluation. nCoVmab1 could reduce viral titer and lung injury when administered prophylactically and therapeutically in human angiotensin-converting enzyme II (hACE2)-transgenic mice. Therefore, phage display platform could be efficiently used for rapid development of neutralizing monoclonal antibodies (nmabs) with clinical potential against emerging infectious diseases. In addition, we determinate epitopes in RBD of these antibodies to elucidate the neutralizing mechanism. We also convert nCoVmab1 and nCoVmab2 to their germline formats for further analysis, which reveals the contribution of somatic hypermutation (SHM) during nCoVmab1 and nCoVmab2 maturation. Our findings not only provide two highly potent nmabs against SARS-CoV-2 as prophylactic and therapeutic candidates, but also give some clues for development of anti-SARS-CoV-2 agents (e.g., drugs and vaccines) targeting the RBD.
It is unknown whether pangolins, the most trafficked mammals, play a role in the zoonotic transmission of bat coronaviruses. We report the circulation of a novel MERS-like coronavirus in Malayan ...pangolins, named Manis javanica HKU4-related coronavirus (MjHKU4r-CoV). Among 86 animals, four tested positive by pan-CoV PCR, and seven tested seropositive (11 and 12.8%). Four nearly identical (99.9%) genome sequences were obtained, and one virus was isolated (MjHKU4r-CoV-1). This virus utilizes human dipeptidyl peptidase-4 (hDPP4) as a receptor and host proteases for cell infection, which is enhanced by a furin cleavage site that is absent in all known bat HKU4r-CoVs. The MjHKU4r-CoV-1 spike shows higher binding affinity for hDPP4, and MjHKU4r-CoV-1 has a wider host range than bat HKU4-CoV. MjHKU4r-CoV-1 is infectious and pathogenic in human airways and intestinal organs and in hDPP4-transgenic mice. Our study highlights the importance of pangolins as reservoir hosts of coronaviruses poised for human disease emergence.
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•A bat HKU4r-CoV (Merbecovirus) MjHKU4r-CoV is circulating in Malayan pangolins•MjHKU4r-CoV-1 uses human DPP4 receptor and can be isolated•MjHKU4r-CoV-1 has a furin cleavage site and a wider host range than bat HKU4-CoV•MjHKU4r-CoV-1 is infectious and pathogenic in human organs and hDPP4-Tg mice
A bat MERS-like coronavirus similar to HKU4-CoV is identified circulating in Malayan pangolins. This virus uses human DPP4 as a receptor, bears a furin cleavage site that potentially broadens its host range, and is infectious in human organs and transgenic animals.
Linear friction welding (LFW) is a kind of advanced manufacturing technology and used mainly in the manufacturing of aircraft engine bladed disks (blisks) currently. However, the residual stress ...evolution of TC17 titanium alloy during LFW is complex and its distribution is difficult to characterize. In this study, the residual stress of welding was studied using numerical simulation and experimental methods. The results showed that the maximum temperature on the welded surface was up to 1000 °C and the cooling rates in the lengthwise, widthwise, and normal direction with the same distance from the center of the weld were 456 °C/s, 448 °C/s, and 232 °C/s, respectively. The lengthwise stress on the welding surface was the largest, followed by the widthwise stress and normal stress. Among the three factors affecting welding stress, the upsetting force played a leading role, followed by the vibration amplitude and frequency of the welded parts. By optimizing the process parameters: upsetting force 18.2 kN, vibration amplitude 2.5 mm, vibration frequency 40 Hz, a 30% decrease of the maximum residual stress could be achieved compared to that without optimization. The residual stress before and after welding parameter optimization was measured by the contour method, and the measured results were in good agreement with the simulation results, which verified the effectiveness of parameter optimization on residual stress controlling.
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