RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction.

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction.

Non-coding RNA (ncRNA) plays an important role in many fundamental biological processes, and it may be closely associated with many complex human diseases. NcRNAs exert their functions by interacting with proteins. Therefore, identifying novel ncRNA-protein interactions (NPIs) is important for understanding the mechanism of ncRNAs role. The computational approach has the advantage of low cost and high efficiency. Machine learning and deep learning have achieved great success by making full use of sequence information and structure information. Graph neural network (GNN) is a deep learning algorithm for complex network link prediction, which can extract and discover features in graph topology data. In this study, we propose a new computational model called GATLGEMF. We used a line graph transformation strategy to obtain the most valuable feature information and input this feature information into the attention network to predict NPIs. The results on four benchmark datasets show that our method achieves superior performance. We further compare GATLGEMF with the state-of-the-art existing methods to evaluate the model performance. GATLGEMF shows the best performance with the area under curve (AUC) of 92.41% and 98.93% on RPI2241 and NPInter v2.0 datasets, respectively. In addition, a case study shows that GATLGEMF has the ability to predict new interactions based on known interactions. The source code is available at https://github.com/JianjunTan-Beijing/GATLGEMF.

LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions.

Long non-coding RNAs (LncRNAs) play important roles in a series of life activities, and they function primarily with proteins. The wet experimental-based methods in lncRNA-protein interactions (lncRPIs) study are time-consuming and expensive. In this study, we propose for the first time a novel feature fusion method, the LPI-CSFFR, to train and predict LncRPIs based on a Convolutional Neural Network (CNN) with feature reuse and serial fusion in sequences, secondary structures, and physicochemical properties of proteins and lncRNAs. The experimental results indicate that LPI-CSFFR achieves excellent performance on the datasets RPI1460 and RPI1807 with an accuracy of 83.7 % and 98.1 %, respectively. We further compare LPI-CSFFR with the state-of-the-art existing methods on the same benchmark datasets to evaluate the performance. In addition, to test the generalization performance of the model, we independently test sample pairs of five model organisms, where Mus musculus are the highest prediction accuracy of 99.5 %, and we find multiple hotspot proteins after constructing an interaction network. Finally, we test the predictive power of the LPI-CSFFR for sample pairs with unknown interactions. The results indicate that LPI-CSFFR is promising for predicting potential LncRPIs. The relevant source code and the data used in this study are available at https://github.com/JianjunTan-Beijing/LPI-CSFFR.

VMP1 and TMEM41B are essential for DMV formation during ß-coronavirus infection.

ß-coronaviruses reshape host cell endomembranes to form double-membrane vesicles (DMVs) for genome replication and transcription. Ectopically expressed viral nonstructural proteins nsp3 and nsp4 interact to zipper and bend the ER for DMV biogenesis. Genome-wide screens revealed the autophagy proteins VMP1 and TMEM41B as important host factors for SARS-CoV-2 infection. Here, we demonstrated that DMV biogenesis, induced by virus infection or expression of nsp3/4, is impaired in the VMP1 KO or TMEM41B KO cells. In VMP1 KO cells, the nsp3/4 complex forms normally, but the zippered ER fails to close into DMVs. In TMEM41B KO cells, the nsp3-nsp4 interaction is reduced and DMV formation is suppressed. Thus, VMP1 and TMEM41B function at different steps during DMV formation. VMP1 was shown to regulate cross-membrane phosphatidylserine (PS) distribution. Inhibiting PS synthesis partially rescues the DMV defects in VMP1 KO cells, suggesting that PS participates in DMV formation. We provide molecular insights into the collaboration of host factors with viral proteins to remodel host organelles.

Long term spatial-temporal dynamics of fluoride in sources of drinking water and associated health risks in a semiarid region of Northern China.

There is a concern about the increasing prevalence of health problems related to the ingestion of fluoride (F-) in the developing world. Drinking water is one important source of F-, and the concentration of F- needs to be known to ensure the safety of drinking water. In this study, F- levels in drinking water were investigated across Taiyuan in Shanxi Province, China. Spatial-temporal distribution characteristics and potential associated health risks were analyzed using GIS. We collected 485 samples from shallow wells without any defluoridation treatments between 2008 and 2016. After analyzing the samples of F- content we found that mean F- levels of urban areas (0.61 ±â€¯0.39 mg L-1), suburban areas (0.70 ±â€¯0.87 mg L-1) and for all of Taiyuan city (0.63 ±â€¯0.56 mg L-1) were in optimum range based on the recommendation by USEPA. However, individual locations within industrial areas (e.g. Gujiao District) had higher F- levels (1.06 mg L-1). A concerning result showed that 12.37% of tested locations had F- concentrations larger than 1.0 mg L-1. We calculated F- Health Risk Indices (HRIsF) and found that highest were associated with suburban areas, especially in the year 2009 and 2010. However, from 2008 to 2016, overall F- levels and HRIsF of the sampled groundwater in Taiyuan City showed a decreasing trend. HRIsF in suburban areas was higher than urban areas, possible due to the heavily prevalent coal mining industry in those areas. Specific policies should be formulated to address HRIsF.

The ER-Localized Transmembrane Protein EPG-3/VMP1 Regulates SERCA Activity to Control ER-Isolation Membrane Contacts for Autophagosome Formation.

During autophagosome formation in mammalian cells, isolation membranes (IMs; autophagosome precursors) dynamically contact the ER. Here, we demonstrated that the ER-localized metazoan-specific autophagy protein EPG-3/VMP1 controls ER-IM contacts. Loss of VMP1 causes stable association of IMs with the ER, thus blocking autophagosome formation. Interaction of WIPI2 with the ULK1/FIP200 complex and PI(3)P contributes to the formation of ER-IM contacts, and these interactions are enhanced by VMP1 depletion. VMP1 controls contact formation by promoting SERCA (sarco[endo]plasmic reticulum calcium ATPase) activity. VMP1 interacts with SERCA and prevents formation of the SERCA/PLN/SLN inhibitory complex. VMP1 also modulates ER contacts with lipid droplets, mitochondria, and endosomes. These ER contacts are greatly elevated by the SERCA inhibitor thapsigargin. Calmodulin acts as a sensor/effector to modulate the ER contacts mediated by VMP1/SERCA. Our study provides mechanistic insights into the establishment and disassociation of ER-IM contacts and reveals that VMP1 modulates SERCA activity to control ER contacts.

Characteristics of N-Acylhomoserine Lactones Produced by Hafnia alvei H4 Isolated from Spoiled Instant Sea Cucumber.

This study aimed to identify N-acylhomoserine lactone (AHL) produced by Hafnia alvei H4, which was isolated from spoiled instant sea cucumber, and to investigate the effect of AHLs on biofilm formation. Two biosensor strains, Chromobacterium violaceum CV026 and Agrobacterium tumefaciens KYC55, were used to detect the quorum sensing (QS) activity of H. alvei H4 and to confirm the existence of AHL-mediated QS system. Thin layer chromatography (TLC) and high resolution triple quadrupole liquid chromatography/mass spectrometry (LC/MS) analysis of the AHLs extracted from the culture supernatant of H. alvei H4 revealed the existence of at least three AHLs: N-hexanoyl-l-homoserine lactone (C6-HSL), N-(3-oxo-octanoyl)-l-homoserine lactone (3-oxo-C8-HSL), and N-butyryl-l-homoserine lactone (C4-HSL). This is the first report of the production of C4-HSL by H. alvei. In order to determine the relationship between the production of AHL by H. alvei H4 and bacterial growth, the ß-galactosidase assay was employed to monitor AHL activity during a 48-h growth phase. AHLs production reached a maximum level of 134.6 Miller unites at late log phase (after 18 h) and then decreased to a stable level of about 100 Miller unites. AHL production and bacterial growth displayed a similar trend, suggesting that growth of H. alvei H4 might be regulated by QS. The effect of AHLs on biofilm formation of H. alvei H4 was investigated by adding exogenous AHLs (C4-HSL, C6-HSL and 3-oxo-C8-HSL) to H. alvei H4 culture. Biofilm formation was significantly promoted (p < 0.05) by 5 and 10 µM C6-HSL, inhibited (p < 0.05) by C4-HSL (5 and 10 µM) and 5 µM 3-oxo-C8-HSL, suggesting that QS may have a regulatory role in the biofilm formation of H. alvei H4.