Interaction of Nipah Virus F and G with the Cellular Protein Cortactin Discovered by a Proximity Interactome Assay.

Nipah virus (NiV) is a highly lethal zoonotic virus with a potential large-scale outbreak, which poses a great threat to world health and security. In order to explore more potential factors associated with NiV, a proximity labeling method was applied to investigate the F, G, and host protein interactions systematically. We screened 1996 and 1524 high-confidence host proteins that interacted with the NiV fusion (F) glycoprotein and attachment (G) glycoprotein in HEK293T cells by proximity labeling technology, and 863 of them interacted with both F and G. The results of GO and KEGG enrichment analysis showed that most of these host proteins were involved in cellular processes, molecular binding, endocytosis, tight junction, and other functions. Cytoscape software (v3.9.1) was used for visual analysis, and the results showed that Cortactin (CTTN), Serpine mRNA binding protein 1 (SERBP1), and stathmin 1 (STMN1) were the top 20 proteins and interacted with F and G, and were selected for further validation. We observed colocalization of F-CTTN, F-SERBP1, F-STMN1, G-CTTN, G-SERBP1, and G-STMN1 using confocal fluorescence microscopy, and the results showed that CTTN, SERBP1, and STMN1 overlapped with NiV F and NiV G in HEK293T cells. Further studies found that CTTN can significantly inhibit the infection of the Nipah pseudovirus (NiVpv) into host cells, while SERBP1 and STMN1 had no significant effect on pseudovirus infection. In addition, CTTN can also inhibit the infection of the Hendra pseudovirus (HeVpv) in 293T cells. In summary, this study revealed that the potential host proteins interacted with NiV F and G and demonstrated that CTTN could inhibit NiVpv and HeVpv infection, providing new evidence and targets for the study of drugs against these diseases.

HMDD v4.0: a database for experimentally supported human microRNA-disease associations.

MicroRNAs (miRNAs) are a class of important small non-coding RNAs with critical molecular functions in almost all biological processes, and thus, they play important roles in disease diagnosis and therapy. Human MicroRNA Disease Database (HMDD) represents an important and comprehensive resource for biomedical researchers in miRNA-related medicine. Here, we introduce HMDD v4.0, which curates 53530 miRNA-disease association entries from literatures. In comparison to HMDD v3.0 released five years ago, HMDD v4.0 contains 1.5 times more entries. In addition, some new categories have been curated, including exosomal miRNAs implicated in diseases, virus-encoded miRNAs involved in human diseases, and entries containing miRNA-circRNA interactions. We also curated sex-biased miRNAs in diseases. Furthermore, in a case study, disease similarity analysis successfully revealed that sex-biased miRNAs related to developmental anomalies are associated with a number of human diseases with sex bias. HMDD can be freely visited at http://www.cuilab.cn/hmdd.

LncRNADisease v3.0: an updated database of long non-coding RNA-associated diseases.

Systematic integration of lncRNA-disease associations is of great importance for further understanding their underlying molecular mechanisms and exploring lncRNA-based biomarkers and therapeutics. The database of long non-coding RNA-associated diseases (LncRNADisease) is designed for the above purpose. Here, an updated version (LncRNADisease v3.0) has curated comprehensive lncRNA (including circRNA) and disease associations from the burgeoning literatures. LncRNADisease v3.0 exhibits an over 2-fold increase in experimentally supported associations, with a total of 25440 entries, compared to the last version. Besides, each lncRNA-disease pair is assigned a confidence score based on experimental evidence. Moreover, all associations between lncRNAs/circRNAs and diseases are classified into general associations and causal associations, representing whether lncRNAs or circRNAs can directly lead to the development or progression of corresponding diseases, with 15721 and 9719 entries, respectively. In a case study, we used the data of LncRNADisease v3.0 to calculate the phenotypic similarity between human and mouse lncRNAs. This database will continue to serve as a valuable resource for potential clinical applications related to lncRNAs and circRNAs. LncRNADisease v3.0 is freely available at http://www.rnanut.net/lncrnadisease.

Detection of diabetic patients in people with normal fasting glucose using machine learning.

BACKGROUND: Diabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG. METHODS: The physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors. RESULTS: The prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application ( http://www.cuilab.cn/dring ). CONCLUSIONS: DRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.

Defining the single base importance of human mRNAs and lncRNAs.

As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. However, there are still no sequence-based methods of doing that. Here, we proposed Base Importance Calculator (BIC), an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We then confirmed its power by applying BIC to three different tasks. Firstly, we revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases through single nucleotide variations. Moreover, the BIC score in The Cancer Genome Atlas somatic mutations is able to predict the prognosis of some cancers. Finally, we show that BIC can also precisely predict the transmissibility of SARS-CoV-2. The above results indicate that BIC is a useful tool for evaluating the single base importance of human mRNAs and lncRNAs.

A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency.

CRISPR/Cas9 system is a powerful tool for genome editing. Numerous studies have shown that sgRNAs can strongly affect the efficiency of editing. However, it is still not clear what rules should be followed for designing sgRNA with high cleavage efficiency. At present, several machine learning or deep learning methods have been developed to predict the cleavage efficiency of sgRNAs, however, the prediction accuracy of these tools is still not satisfactory. Here we propose a fusion framework of deep learning and machine learning, which first deals with the primary sequence and secondary structure features of the sgRNAs using both convolutional neural network (CNN) and recurrent neural network (RNN), and then uses the features extracted by the deep neural network to train a conventional machine learning model with LGBM. As a result, the new approach overwhelmed previous methods. The Spearman's correlation coefficient between predicted and measured sgRNA cleavage efficiency of our model (0.917) is improved by over 5% compared with the most advanced method (0.865), and the mean square error reduces from 7.89 × 10-3 to 4.75 × 10-3. Finally, we developed an online tool, CRISep (http://www.cuilab.cn/CRISep), to evaluate the availability of sgRNAs based on our models.

Identification and Analysis of Sex-Biased MicroRNAs in Human Diseases.

It is well known that significant differences exist between males and females in both physiology and disease. Thus, it is important to identify and analyze sex-biased miRNAs. However, previous studies investigating sex differences in miRNA expression have predominantly focused on healthy individuals or restricted their analysis to a single disease. Therefore, it is necessary to comprehensively identify and analyze the sex-biased miRNAs in diseases. For this purpose, in this study, we first identified the miRNAs showing sex-biased expression between males and females in diseases based on a number of miRNA expression datasets. Then, we performed a bioinformatics analysis for these sex-biased miRNAs. Notably, our findings revealed that women exhibit a greater number of conserved miRNAs that are highly expressed compared to men, and these miRNAs are implicated in a broader spectrum of diseases. Additionally, we explored the enriched transcription factors, functions, and diseases associated with these sex-biased miRNAs using the miRNA set enrichment analysis tool TAM 2.0. The insights gained from this study could carry implications for endeavors such as precision medicine and possibly pave the way for more targeted and tailored approaches to disease management.

G Protein-Coupled Receptor 40 Agonist LY2922470 Alleviates Ischemic-Stroke-Induced Acute Brain Injury and Functional Alterations in Mice.

Stroke is a major cause of fatalities and disabilities around the world, yet the available treatments for it are still limited. The quest for more efficacious drugs and therapies is still an arduous task. LY2922470 is currently used as a G protein-coupled receptor 40 (GPR40) agonist for the treatment of type 2 diabetes. Previous studies have reported protective effects of other GPR40 activators on the brain; however, it remains unclear whether LY2922470 could be a new stroke therapy and improve the stroke-induced brain damage. Here, we first reveal that the transcriptomic gene signature induced by LY2922470 is highly similar to those induced by some agents being involved in defending from cerebrovascular accidents and transient ischemic attacks, including acetylsalicylic acid, progesterone, estradiol, dipyridamole, and dihydroergotamine. This result thus suggests that LY2922470 could have protective effects against ischemic stroke. As a result, further experiments show that giving the small molecule LY2922470 via oral administration or intraperitoneal injection was seen to have a positive effect on neuroprotection with a reduction in infarct size and an improvement in motor skills in mice. Finally, it was demonstrated that LY2922470 could successfully mitigate the harm to the brain caused by ischemic stroke.

Combating pancreatic cancer with ovarian cancer cells.

With overall five-year survival rate less than 10%, pancreatic cancer (PC) represents the most lethal one in all human cancers. Given that the incidence of PC is still increasing and current cancer treatment strategies are often inefficacious, its therapy is still a huge challenge. Here, we first revealed ovarian serous carcinoma is mostly anti-correlated with pancreatic cancer in gene expression signatures. Based on this observation, we proposed that ovarian cancer cells could defend PC. To confirm this strategy, we first showed that ovarian cancer cell SKOV3 can significantly inhibit the proliferation of pancreatic cancer cell SW1990 when they were co-cultured. We further validated this strategy by an animal model of pancreatic cancer xenografts. The result showed that the injection of SKOV3 significantly inhibits pancreatic cancer xenografts. Moreover, we found that SKOV3 with transgenic African elephant TP53 gene further enhances the therapeutic effect. RNA-sequencing analysis revealed that the ovarian cancer cell treatment strikingly induced changes of genes being involved in pancreas function and phenotype (e.g. enhancing pancreas function, pancreas regeneration, and cell adhesion) but not immune and inflammation-related functions, suggesting that the proposed strategy is different from immunotherapy and could be a novel strategy for cancer treatment.

Erratum: MicroRNA Profiling in Paired Left and Right Eyes, Lungs, and Testes of Normal Mice.

[This corrects the article DOI: 10.1016/j.omtn.2020.07.006.].

Pre-miRNA Hsa-Let-7a-2: a Novel Intracellular Partner of Angiotensin II Type 2 Receptor Negatively Regulating its Signals.

G protein-coupled receptors (GPCRs) are the largest family of druggable targets, and their biological functions depend on different ligands and intracellular interactomes. Some microRNAs (miRNAs) bind as ligands to RNA-sensitive toll-like receptor 7 to regulate the inflammatory response, thereby contributing to the pathogenesis of cancer or neurodegeneration. It is unknown whether miRNAs bind to angiotensin II (Ang II) type 2 receptor (AGTR2), a critical protective GPCR in cardiovascular diseases, as ligands or intracellular interactomes. Here, screening for miRNAs that bind to AGTR2, we identified and confirmed that the pre-miRNA hsa-let-7a-2 non-competitively binds to the intracellular third loop of AGTR2. Functionally, intracellular hsa-let-7a-2 overexpression suppressed the Ang II-induced AGTR2 effects such as cAMP lowering, RhoA inhibition, and activation of Src homology 2 domain-containing protein-tyrosine phosphatase 1, whereas hsa-let-7a-2 knockdown enhanced these effects. Consistently, overexpressed hsa-let-7a-2 restrained the AGTR2-induced antiproliferation, antimigration, and proapoptosis of cells, and vasodilation of mesenteric arteries. Our findings demonstrated that hsa-let-7a-2 is a novel intracellular partner of AGTR2 that negatively regulates AGTR2-activated signals.

Hepatocellular cystathionine γ lyase/hydrogen sulfide attenuates nonalcoholic fatty liver disease by activating farnesoid X receptor.

BACKGROUND AND AIMS: Hydrogen sulfide (H2 S) plays a protective role in NAFLD. However, whether cystathionine γ lyase (CSE), a dominant H2 S generating enzyme in hepatocytes, has a role in the pathogenesis of NAFLD is currently unclear. APPROACH AND RESULTS: We showed that CSE protein expression is dramatically downregulated, especially in fibrotic areas, in livers from patients with NAFLD. In high-fat diet (HFD)-induced NAFLD mice or an oleic acid-induced hepatocyte model, the CSE/H2 S pathway is also downregulated. To illustrate a regulatory role for CSE in NAFLD, we generated a hepatocyte-specific CSE knockout mouse (CSELKO ). Feeding an HFD to CSELKO mice, they showed more hepatic lipid deposition with increased activity of the fatty acid de novo synthesis pathway, increased hepatic insulin resistance, and higher hepatic gluconeogenic ability compared to CSELoxp control mice. By contrast, H2 S donor treatment attenuated these phenotypes. Furthermore, the protection conferred by H2 S was blocked by farnesoid X receptor (FXR) knockdown. Consistently, serum deoxycholic acid and lithocholic acid (FXR antagonists) were increased, and tauro-ß-muricholic acid (FXR activation elevated) was reduced in CSELKO . CSE/H2 S promoted a post-translation modification (sulfhydration) of FXR at Cys138/141 sites, thereby enhancing its activity to modulate expression of target genes related to lipid and glucose metabolism, inflammation, and fibrosis. Sulfhydration proteomics in patients' livers supported the CSE/H2 S modulation noted in the CSELKO mice. CONCLUSIONS: FXR sulfhydration is a post-translational modification affected by hepatic endogenous CSE/H2 S that may promote FXR activity and attenuate NAFLD. Hepatic CSE deficiency promotes development of nonalcoholic steatohepatitis. The interaction between H2 S and FXR may be amenable to therapeutic drug treatment in NAFLD.

wTAM: a web server for annotation of weighted human microRNAs.

It is well-known that some microRNAs (miRNAs) are more important than the others for life, hinting the wide range of miRNA in essentiality or importance. Functional enrichment analysis is a quite pervasive method to dig out the underlying biological pathway for a given gene list and several tools of miRNA set enrichment analysis have been developed. However, all those tools treat each miRNA equally and neglect the importance score of miRNA itself, which could be an obstacle to seek more insightful biological processes for researchers. Here, we developed wTAM, a tool for annotation of weighted human miRNAs, introducing the miRNA importance scores into enrichment analysis. In addition, the annotation repository has been enlarged comparing to TAM. Finally, the case study demonstrated the availability and flexibility of wTAM. Availability and implementation: wTAM is freely available at http://www.cuilab.cn/wtam/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs.

Versatile RNA modifications play important roles in post-transcriptional regulations of gene expression, among which glycosylation modifications on small RNAs emerge as a novel clade whose characteristics need further interrogations. Here, we demonstrated that the sequence pattern around RNA glycosylation sites was not random and could be exploited for glycosylation site prediction. A machine learning predictor, GlyinsRNA, which integrated multiple RNA sequence representation encodings, was established. GlyinsRNA achieved AUROC (area under the receiver operating characteristic curve) of 0.7933 and 0.7979 in five-fold cross-validation and independent tests, respectively. GlyinsRNA was implemented as an online webserver, where both the predicted glycosylation sites and the overrepresented RNA-binding protein (RBP)-related motifs were annotated to facilitate the users. GlyinsRNA webserver is freely available at http://www.rnanut.net/glyinsrna.

Comprehensive Analysis of RNA Expression Correlations between Biofluids and Human Tissues.

In recent years, biofluid has been considered a promising source of non-invasive biomarkers for health monitoring and disease diagnosis. However, the expression consistency between biofluid and human tissue, which is fundamental to RNA biomarker development, has not been fully evaluated. In this study, we collected expression profiles across 53 human tissues and five main biofluid types. Utilizing the above dataset, we uncovered a globally positive correlation pattern between various biofluids (including blood, urine, bile, saliva and stool) and human tissues. However, significantly varied biofluid-tissue similarity levels and tendencies were observed between mRNA and lncRNA. Moreover, a higher correlation was found between biofluid types and their functionally related and anatomically closer tissues. In particular, a highly specific correlation was discovered between urine and the prostate. The biological sex of the donor was also proved to be an important influencing factor in biofluid-tissue correlation. Moreover, genes enriched in basic biological processes were found to display low variability across biofluid types, while genes enriched in catabolism-associated pathways were identified as highly variable.

Defining the functional divergence of orthologous genes between human and mouse in the context of miRNA regulation.

Animal models have a certain degree of similarity with human in genes and physiological processes, which leads them to be valuable tools for studying human diseases and for assisting drug development. However, translational researches adopting animal models are largely restricted by the species heterogeneity, which is also a major reason for the failure of drug research. Currently, computational method for exploring the functional differences between orthologous genes is still insufficient. For this purpose, here, we presented an algorithm, functional divergence score (FDS), by comprehensively evaluating the functional differences between the microRNAs regulating the paired orthologous genes. Given that mouse is one of the most popular model animals, currently, FDS was designed to dissect the functional divergence of orthologous genes between human and mouse. The results showed that gene FDS value is significantly associated with gene evolutionary characteristics and can discover expression divergence of human-mouse orthologous genes. Moreover, FDS performed well in distinguishing the targets of approved drugs and the failed ones. These results suggest that FDS is a valuable tool to evaluate the functional divergence of paired human and mouse orthologous genes. In addition, for each orthologous gene pair, FDS can provide detailed differences in functions and phenotypes. Our study provided a useful tool for quantifying the functional difference between human and mouse, and the presented framework is easily to be extended to the orthologous genes between human and other species. An online server of FDS is available at http://www.cuilab.cn/fds/.

AGTR2, One Possible Novel Key Gene for the Entry of SARS-CoV-2 Into Human Cells.

Recently, it was confirmed that ACE2 is the receptor of SARS-CoV-2, the pathogen causing the recent outbreak of severe pneumonia around the world. It is confused that ACE2 is widely expressed across a variety of organs and is expressed moderately but not highly in lung, which, however, is the major infected organ. Therefore, we hypothesized that there could be some other genes playing key roles in the entry of SARS-CoV-2 into human cells. Here we found that AGTR2 (angiotensin II receptor type 2), a G-protein coupled receptor, has interaction with ACE2 and is highly expressed in lung with a high tissue specificity. More importantly, simulation of 3D structure based protein-protein interaction reveals that AGTR2 shows a higher binding affinity with the Spike protein of SARS-CoV-2 than ACE2 (energy: -8.2 vs. -5.1 [kcal/mol]). A number of compounds, biologics and traditional Chinese medicine that could decrease the expression level of AGTR2 were predicted. Finally, we suggest that AGTR2 could be a putative novel gene for the entry of SARS-CoV-2 into human cells, which could provide different insight for the research of SARS-CoV-2 proteins with their receptors.

smORFunction: a tool for predicting functions of small open reading frames and microproteins.

BACKGROUND: Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. RESULTS: In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. CONCLUSIONS: We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction .

MicroRNA Profiling in Paired Left and Right Eyes, Lungs, and Testes of Normal Mice.

Physiological and pathophysiological differences widely exist in paired organ systems. However, the molecular basis for these differences remains largely unknown. We previously reported that there exist differentially expressed miRNAs (DEMs) in the left and right kidneys of normal mice. Here, we identified the DEMs in the left and right eyes, lungs, and testes of normal mice via RNA sequencing. As a result, we identified 26 DEMs in eyes, with 23 higher and 3 lower in the left eyes compared with right eyes; 21 DEMs in lungs, with 15 higher and 6 lower in the left lungs compared with right lungs; and 54 DEMs in testes, with 6 higher and 48 lower in the left testes compared with right testes. Ten microRNAs (miRNAs) were further examined by quantitative PCR assays, and seven of these were confirmed. In addition, correlation analysis was performed between paired organ miRNA expressions and diverse body fluid miRNA expressions. Finally, we explored the functions and networks of DEMs and performed biological process and pathway enrichment analysis of target genes for DEMs, providing insights into the physiological and pathophysiological differences between the two entities of paired organs.