The role of antithymocyte globulin (ATG) in preventing acute graft-versus-host disease (aGVHD) after HLA-matched sibling donor transplantation (MSDT) is still controversial.
We performed a ...prospective, multicenter, open-label, randomized controlled trial (RCT) across 23 transplantation centers in China. Patients ages 40-60 years with standard-risk hematologic malignancies with an HLA-matched sibling donor were randomly assigned to an ATG group (4.5 mg/kg thymoglobulin plus cyclosporine CsA, methotrexate MTX, and mycophenolate mofetil MMF) and a control group (CsA, MTX, and MMF). The primary end point of this study was grade 2-4 aGVHD on day 100.
From November 2013 to April 2018, 263 patients were enrolled. The cumulative incidence rate of grade 2-4 aGVHD was significantly reduced in the ATG group (13.7%; 95% CI, 13.5% to 13.9%) compared with the control group (27.0%; 95% CI, 26.7% to 27.3%;
= .007). The ATG group had significantly lower incidences of 2-year overall chronic GVHD (27.9% 95% CI, 27.6% to 28.2%
52.5% 95% CI, 52.1% to 52.9%;
< .001) and 2-year extensive chronic GVHD (8.5% 95% CI, 8.4% to 8.6%
23.2% 95% CI, 22.9% to 23.5%;
= .029) than the control group. There were no differences between the ATG and control groups with regard to cytomegalovirus reactivation, Epstein-Barr virus reactivation, 3-year nonrelapse mortality (NRM), 3-year cumulative incidence of relapse (CIR), 3-year overall survival, or 3-year leukemia-free survival. Three-year GVHD relapse-free survival was significantly improved in the ATG group (38.7%; 95% CI, 29.9% to 47.5%) compared with the control group (24.5%; 95% CI, 16.9% to 32.1%;
= .003).
Our study is the first prospective RCT in our knowledge to demonstrate that ATG can effectively decrease the incidence of aGVHD after MSDT in the CsA era without affecting the CIR or NRM.
Met tyrosine kinase, a receptor for a hepatocyte growth factor (HGF), plays a critical role in tumor growth, metastasis, and drug resistance. Mitochondria are highly dynamic and undergo fission and ...fusion to maintain a functional mitochondrial network. Dysregulated mitochondrial dynamics are responsible for the progression and metastasis of many cancers. Here, using structured illumination microscopy (SIM) and high spatial and temporal resolution live cell imaging, we identified mitochondrial trafficking of receptor tyrosine kinase Met. The contacts between activated Met kinase and mitochondria formed dramatically, and an intact HGF/Met axis was necessary for dysregulated mitochondrial fission and cancer cell movements. Mechanically, we found that Met directly phosphorylated outer mitochondrial membrane protein Fis1 at Tyr38 (Fis1 pY38). Fis1 pY38 promoted mitochondrial fission by recruiting the mitochondrial fission GTPase dynamin-related protein-1 (Drp1) to mitochondria. Fragmented mitochondria fueled actin filament remodeling and lamellipodia or invadopodia formation to facilitate cell metastasis in hepatocellular carcinoma (HCC) cells both in vitro and in vivo. These findings reveal a novel and noncanonical pathway of Met receptor tyrosine kinase in the regulation of mitochondrial activities, which may provide a therapeutic target for metastatic HCC.
Abstract
More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our ...understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew’s correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.
Abstract
The determination of the absolute and relative position of a spacecraft is critical for its operation, observations, data analysis, scientific studies, as well as deep-space exploration in ...general. A spacecraft that can determine its own absolute position autonomously may perform better than those that must rely on transmission solutions. In this work, we report an absolute navigation accuracy of ∼20 km using 16 day Crab pulsar data observed with Fermi’s Gamma-ray Burst Monitor (GBM). In addition, we propose a new method with the inverse process of the triangulation for joint navigation using repeated bursts like those from the magnetar SGR J1935+2154 observed by the Gravitational-wave High-energy Electromagnetic Counterpart All-sky Monitor and GBM.
Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for ...predicting DNA-protein binding from sequence data have achieved significant success. Nevertheless, the current state-of-the-art computational methods have some drawbacks associated with the use of limited datasets with insufficient experimental data. To address this, we propose a novel transfer learning-based method, termed SAResNet, which combines the self-attention mechanism and residual network structure. More specifically, the attention-driven module captures the position information of the sequence, while the residual network structure guarantees that the high-level features of the binding site can be extracted. Meanwhile, the pre-training strategy used by SAResNet improves the learning ability of the network and accelerates the convergence speed of the network during transfer learning. The performance of SAResNet is extensively tested on 690 datasets from the ChIP-seq experiments with an average AUC of 92.0%, which is 4.4% higher than that of the best state-of-the-art method currently available. When tested on smaller datasets, the predictive performance is more clearly improved. Overall, we demonstrate that the superior performance of DNA-protein binding prediction on DNA sequences can be achieved by combining the attention mechanism and residual structure, and a novel pipeline is accordingly developed. The proposed methodology is generally applicable and can be used to address any other sequence classification problems.
Accurate identification of protein–DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the ...prediction of protein–DNA binding sites. However, the data imbalance problem, in which the number of nonbinding residues (negative-class samples) is far larger than that of binding residues (positive-class samples), seriously restricts the performance improvements of machine-learning-based predictors. In this work, we designed a two-stage imbalanced learning algorithm, called ensembled hyperplane-distance-based support vector machines (E-HDSVM), to improve the prediction performance of protein–DNA binding sites. The first stage of E-HDSVM designs a new iterative sampling algorithm, called hyperplane-distance-based under-sampling (HD-US), to extract multiple subsets from the original imbalanced data set, each of which is used to train a support vector machine (SVM). Unlike traditional sampling algorithms, HD-US selects samples by calculating the distances between the samples and the separating hyperplane of the SVM. The second stage of E-HDSVM proposes an enhanced AdaBoost (EAdaBoost) algorithm to ensemble multiple trained SVMs. As an enhanced version of the original AdaBoost algorithm, EAdaBoost overcomes the overfitting problem. Stringent cross-validation and independent tests on benchmark data sets demonstrated the superiority of E-HDSVM over several popular imbalanced learning algorithms. Based on the proposed E-HDSVM algorithm, we further implemented a sequence-based protein–DNA binding site predictor, called DNAPred, which is freely available at http://csbio.njust.edu.cn/bioinf/dnapred/ for academic use. The computational experimental results showed that our predictor achieved an average overall accuracy of 91.7% and a Mathew’s correlation coefficient of 0.395 on five benchmark data sets and outperformed several state-of-the-art sequence-based protein–DNA binding site predictors.
A twin support vector machine (TWSVM) is a classic distance metric learning method for classification problems. The TWSVM criterion is formulated based on the squared L2-norm distance, making it ...prone to being influenced by the presence of outliers. In this paper, to develop a robust distance metric learning method, we propose a new objective function, called L1-TWSVM, for the TWSVM classifier using the robust L1-norm distance metric. The optimization strategy is to maximize the ratio of the inter-class distance dispersion to the intra-class distance dispersion by using the robust L1-norm distance rather than the traditional L2-norm distance. The resulting objective function is much more challenging to optimize because it involves a non-smooth L1-norm term. As an important contribution of this paper, we design a simple but valid iterative algorithm for solving L1-norm optimal problems. This algorithm is easy to implement, and its convergence to an optimum is theoretically guaranteed. The efficiency and robustness of L1-TWSVM have been validated by extensive experiments on both UCI datasets as well as synthetic datasets. The promising experimental results indicate that our proposal approaches outperform relevant state-of-the-art methods in all kinds of experimental settings.
Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, ...we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer’s self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.
The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein ...structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2–10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.
PDF
