Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.

Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.

Human cytomegalovirus infection impairs neural differentiation via repressing sterol regulatory element binding protein 2-mediated cholesterol biosynthesis.

Congenital human cytomegalovirus (HCMV) infection is a major cause of abnormalities and disorders in the central nervous system (CNS) and/or the peripheral nervous system (PNS). However, the complete pathogenesis of neural differentiation disorders caused by HCMV infection remains to be fully elucidated. Stem cells from human exfoliated deciduous teeth (SHEDs) are mesenchymal stem cells (MSCs) with a high proliferation and neurogenic differentiation capacity. Since SHEDs originate from the neural crest of the early embryonic ectoderm, SHEDs were hypothesized to serve as a promising cell line for investigating the pathogenesis of neural differentiation disorders in the PNS caused by congenital HCMV infection. In this work, SHEDs were demonstrated to be fully permissive to HCMV infection and the virus was able to complete its life cycle in SHEDs. Under neurogenic inductive conditions, HCMV infection of SHEDs caused an abnormal neural morphology. The expression of stem/neural cell markers was also disturbed by HCMV infection. The impairment of neural differentiation was mainly due to a reduction of intracellular cholesterol levels caused by HCMV infection. Sterol regulatory element binding protein-2 (SREBP2) is a critical transcription regulator that guides cholesterol synthesis. HCMV infection was shown to hinder the migration of SREBP2 into nucleus and resulted in perinuclear aggregations of SREBP2 during neural differentiation. Our findings provide new insights into the prevention and treatment of nervous system diseases caused by congenital HCMV infection.

CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response.

To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase-substrate interactions in eukaryotic species.

As an important post-translational modification, ubiquitination mediates ∼80% of protein degradation in eukaryotes. The degree of protein ubiquitination is tightly determined by the delicate balance between specific ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase-mediated deubiquitination. In 2017, we developed UbiBrowser 1.0, which is an integrated database for predicted human proteome-wide E3-substrate interactions. Here, to meet the urgent requirement of proteome-wide E3/deubiquitinase-substrate interactions (ESIs/DSIs) in multiple organisms, we updated UbiBrowser to version 2.0 (http://ubibrowser.ncpsb.org.cn). Using an improved protocol, we collected 4068/967 known ESIs/DSIs by manual curation, and we predicted about 2.2 million highly confident ESIs/DSIs in 39 organisms, with >210-fold increase in total data volume. In addition, we made several new features in the updated version: (i) it allows exploring proteins' upstream E3 ligases and deubiquitinases simultaneously; (ii) it has significantly increased species coverage; (iii) it presents a uniform confidence scoring system to rank predicted ESIs/DSIs. To facilitate the usage of UbiBrowser 2.0, we also redesigned the web interface for exploring these known and predicted ESIs/DSIs, and added functions of 'Browse', 'Download' and 'Application Programming Interface'. We believe that UbiBrowser 2.0, as a discovery tool, will contribute to the study of protein ubiquitination and the development of drug targets for complex diseases.

Probiotic characterization of Bacillus smithii: Research advances, concerns, and prospective trends.

Bacillus smithii is a thermophilic Bacillus that can be isolated from white wine, hot spring soil, high-temperature compost, and coffee grounds, with various biofunctions and wide applications. It is resistant to both gastric acid and high temperature, which makes it easier to perform probiotic effects than traditional commercial probiotics, so it can maintain good vitality during food processing and has great application prospects. This paper starts with the taxonomy and genetics and focuses on aspects, including genetic transformation, functional enzyme production, waste utilization, and application in the field of food science as a potential probiotic. According to available studies during the past 30 years, we considered that B. smithii is a novel class of microorganisms with a wide range of functional enzymes such as hydrolytic enzymes and hydrolases, as well as resistance to pathogenic bacteria. It is available in waste degradation, organic fertilizer production, the feed and chemical industries, the pharmaceutical sector, and food fortification. Moreover, B. smithii has great potentials for applications in the food industry, as it presents high resistance to the technological processes that guarantee its health benefits. It is also necessary to systematically evaluate the safety, flavor, and texture of B. smithii and explore its biological mechanism of action, which is of great value for further application in multiple fields, especially in food and medicine.

DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.

Robust Variable Selection with Exponential Squared Loss for the Spatial Durbin Model.

With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is proposed for the spatial Durbin model. Under mild conditions, we establish the asymptotic and "Oracle" properties of the proposed estimator. However, in model solving, nonconvex and nondifferentiable programming problems bring challenges to solving algorithms. To solve this problem effectively, we design a BCD algorithm and give a DC decomposition of the exponential squared loss. Numerical simulation results show that the method is more robust and accurate than existing variable selection methods when noise is present. In addition, we also apply the model to the 1978 housing price dataset in the Baltimore area.

Comparative study of anatomical locking plate and reconstruction plate in treating acetabular fractures.

PURPOSE: This study aims to compare the effectiveness of anatomic locking plate and reconstruction plate used in treating acetabular fractures. METHODS: From January 2009 to January 2016, patients with acetabular fractures were included in this retrospective study. We grouped the patients into two groups based on the internal fixation: reconstruction plate group (RPG) and anatomic locking plate group (ALPG). The operation time, blood loss, intra-operative screw penetration, and plate breakage were compared between the two groups. The intra-operative fluoroscopic images were used to evaluate the fixation location. The quality of reduction and radiological grading were assessed according to the criteria developed by Matta. The clinical assessment was based on the Merle d'Aubigne-Postel score. RESULTS: Eighty-three patients were included in this study and were followed up for an average of 35 months (range, 25 to 42 months). Thirty-five patients were treated with the anatomical locking plate, and 48 patients were treated with the reconstruction plate. The mean surgical time was significantly shorter (P < 0.0001) in ALPG patients than in RPG patients, and the intra-operative blood loss was significantly lower (P = 0.008). The rates of intra-operative screw penetration or plate breakage in the ALPG (0/35) are significantly lower than that in the RPG (7/48) (P = 0.018). Post-operative Matta score (P = 0.905), Merle d'Aubigne-Postel score (P = 0.957), and overall complication rates (P = 0.391) were not significantly different among the groups. CONCLUSION: Patients treated by anatomical locking plate had shorter operation time, less bleeding, and lower rate screw perforation compared to patients treated by reconstruction plate. Anatomical locking plate is a better choice for acetabulum fractures, especially complicated fractures.

Levels of human cytomegalovirus miR-US25-1-5p and miR-UL112-3p in serum extracellular vesicles from infants with HCMV active infection are significantly correlated with liver damage.

Human cytomegalovirus (HCMV)-encoded microRNAs (miRNAs) are involved in posttranscriptional regulation of gene expression. Extracellular vesicles (EVs) can incorporate miRNAs. Relationship between HCMV infection and miRNAs in EVs remains unknown. EVs were isolated from supernatants of human embryonic lung fibroblasts (HELF) cells. Profiles of miRNAs in EVs were analyzed by deep sequencing. Dynamics of candidate viral miRNAs transportation via EVs was investigated using TaqMan PCR. Levels of candidate viral miRNAs in serum EVs from infants with HCMV active infection were detected and analyzed with their clinical index levels. A total of 16 HCMV miRNAs were found in EVs from infected HELF. Levels of miR-US25-1-5p and miR-UL112-3p in EVs increased at 6 h post-infection and were correlated with those in cells (for miR-US25-1-5p: r2 = 0.9375, p value < 0.05; for miR-UL112-3p: r2 = 0.7557, p value < 0.05). Viral miRNAs were transported into recipient cells at 2 h post-incubation. Moreover, levels of miR-US25-1-5p in serum EVs showed positive correlations with serum levels of γ-glutamyl transpeptidase, direct bilirubin, and total bile acid. Levels of miR-UL112-3p in serum EVs showed a positive correlation with serum levels of direct bilirubin. HCMV miRNAs could be transported to uninfected cells via EVs. Levels of miR-US25-1-5p and miR-UL112-3p in serum EVs from infants with HCMV active infection were significantly correlated with liver damage.

Characterization of Human Cytomegalovirus UL16 and UL17 Transcripts.

Up to now, the UL16-17 region of human cytomegalovirus (HCMV) has not been well characterized at the level of mRNA and protein, especially for the Han strain, the first clinical HCMV strain in China. In previous studies, three transcripts were detected from the UL16-17 region by northern blot analysis for Merlin strain. Transcriptions of UL16 and UL17 were also studied by 5' rapid amplification of cDNA ends (5'RACE) and deep sequencing for AD169 and Towne strains, respectively. However, details of 3' end of UL16 and UL17 transcripts have never been confirmed by 3'RACE. The expressing phage of the UL16-17 region needs further research by northern blot, too. In the present study, cDNA library screening, northern blot and RACE were used to identify the transcription characteristics of the UL16-17 region. Mainly, 3 clusters of transcripts with the same 3' end were found to be expressed from the UL16-17 region in both Han and AD169 strains. The lengths of the core transcripts among the 3 clusters were 1,254nt, 718nt and 468nt, respectively. The corresponding 5' ends are at nt23119, nt23655, nt23905 in the HCMV Han genome. The consistent 3' end is located at nt24372 in the Han genome. The 1,254nt and 468nt transcripts are transcribed in early and late phases, and the 718nt transcript is transcribed only in the late phase.

[TCMATCOV--a bioinformatics platform to predict efficacy of TCM against COVID-19].

There is urgent need to discover effective traditional Chinese medicine(TCM) for treating coronavirus disease 2019(COVID-19). The development of a bioinformatic tool is beneficial to predict the efficacy of TCM against COVID-19. Here we deve-loped a prediction platform TCMATCOV to predict the efficacy of the anti-coronavirus pneumonia effect of TCM, based on the interaction network imitating the disease network of COVID-19. This COVID-19 network model was constructed by protein-protein interactions of differentially expressed genes in mouse pneumonia caused by SARS-CoV and cytokines specifically up-regulated by COVID-19. TCMATCOV adopted quantitative evaluation algorithm of disease network disturbance after multi-target drug attack to predict potential drug effects. Based on the TCMATCOV platform, 106 TCM were calculated and predicted. Among them, the TCM with a high disturbance score account for a high proportion of the classic anti-COVID-19 prescriptions used by clinicians, suggesting that TCMATCOV has a good prediction ability to discover the effective TCM. The five flavors of Chinese medicine with a disturbance score greater than 1 are mainly spicy and bitter. The main meridian of these TCM is lung, heart, spleen, liver, and stomach meridian. The TCM related with QI and warm TCM have higher disturbance score. As a prediction tool for anti-COVID-19 TCM prescription, TCMATCOV platform possesses the potential to discovery possible effective TCM against COVID-19.

Nano oxide intermediate layer assisted room temperature sintering of ink-jet printed silver nanoparticles pattern.

Sintering of metallic nanoparticles (NPs) at low temperature is highly wanted in the manufacturing of flexible electronics. And for ink-jet printing, the metallic NPs after printing usually need thermal or chemical post-treatment to remove stabilizing agents and achieve conductivity. Here, we reported a facile method to realize one-step printed sintering of silver nanoparticle (AgNP) ink at room temperature by using intermediate coated layers composed of oxide NPs and polyvinyl alcohol (PVA) mixture. We found that the detachment of the stabilizer (citrate) from the AgNPs was caused by hydroxyl groups on the surface of the oxide NPs, which enabled the coalescence and sintering of the AgNPs. With the aid of SiO2 NPs based intermediate layer, the patterns showed resistivity as low as 3.45 µΩ cm after sintering. Moreover, the mixed PVA could ensure the forming quality of patterns owing to its adsorption of ink and the high adhesiveness of PVA with substrates. So, we envision that this approach could serve as an adaptive method for sintering of AgNPs based conductive patterns on various substrates at room temperature and promote the manufacture of printed electronics.

Mechanics of fire ant aggregations.

Fire ants link their bodies to form aggregations; these can adopt a variety of structures, they can drip and spread, or withstand applied loads. Here, by using oscillatory rheology, we show that fire ant aggregations are viscoelastic. We find that, at the lowest ant densities probed and in the linear regime, the elastic and viscous moduli are essentially identical over the spanned frequency range, which highlights the absence of a dominant mode of structural relaxation. As ant density increases, the elastic modulus rises, which we interpret by alluding to ant crowding and subsequent jamming. When deformed beyond the linear regime, the aggregation flows, exhibiting shear-thinning behaviour with a stress load that is comparable to the maximum load the aggregation can withstand before individual ants are torn apart. Our findings illustrate the rich, collective mechanical behaviour that can arise in aggregations of active, interacting building blocks.

Potential Mechanism of Neurite Outgrowth Enhanced by Electrical Stimulation: Involvement of MicroRNA-363-5p Targeting DCLK1 Expression in Rat.

Electrical stimulation (ES) promotes neurite outgrowth and nerve regeneration, but the underlying mechanisms remain undefined. In the present study, we investigated the role of micro RNAs (miRNAs) in ES-mediated neurite outgrowth. First, we performed microarray analyses to identify changes in the miRNAs profile of dorsal root ganglion neurons (DRGNs) following ES. The expression of 16 known miRNAs was altered by ES. Bioinformatics showed that the potential targets of these differentially expressed miRNAs were involved in neurite outgrowth. We focused on miRNA-363-5p (miR-363-5p), because its expression was consistently altered by ES in the present study. Silencing miR-363-5p promoted neurite outgrowth, while miR-363-5p mimic reduced neurite outgrowth. Downregulation of miR-363-5p indicated that double cortin-like kinase (DCLK) 1, a major microtubule-associated protein, was a direct target of miR-363-5p in DRGNs. Knockdown of DCLK1 recapitulated the beneficial effect of a miR-363-5p inhibitor on DRG neurite outgrowth. In conclusion, our data has indicated that miR-363-5p is involved in ES-promoted neurite outgrowth by targeting DCLK1. These findings provide new insights into the roles of miRNAs in ES-enhanced neurite outgrowth and regeneration.

Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.

MOTIVATION: Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. RESULTS: Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a user-friendly web service SPACE ( S: imilarity-based P: redictor of A: TC C: od E: ), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability_score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc. AVAILABILITY AND IMPLEMENTATION: The web service SPACE is available at http://www.bprc.ac.cn/space.

Identification and BAC construction of Han, the first characterized HCMV clinical strain in China.

Human cytomegalovirus (HCMV) is the leading infectious cause of birth defects, and may lead to severe or lethal diseases in immunocompromised individuals. Several HCMV strains have been identified and widely applied in research, but no isolate from China has been characterized. In the present study, we isolated, characterized and sequenced the first Chinese HCMV clinical strain Han, and constructed the novel and functional HCMV infectious clone Han-BAC-2311. HCMV Han was isolated from the urine sample of a Chinese infant with multiple developmental disorders. It expresses HCMV specific proteins and contains a representative HCMV genome with minor differences compared to other strains. By homologous recombination using mini-F derived BAC vector pUS-F6, the infectious clone Han-BAC-2311 was constructed containing representative viral genes across the HCMV genome. The insertion site and orientation of BAC sequence were confirmed by restriction enzyme digestion and Southern blotting. The reconstituted recombinant virus HanBAC-2311 expresses typical viral proteins with the same pattern as that of wild-type Han, and also displayed a similar growth kinetics to wild-type Han. The identification of the first clinical HCMV strain in China and the construction of its infectious clone will greatly facilitate the pathogenesis studies and vaccine development in China.

A cluster of 3' coterminal transcripts from US12-US17 locus of human cytomegalovirus.

Among all the human cytomegalovirus (HCMV) gene families, US12 family is relatively undefined in their transcriptional profile and biological functions. In this study, the transcription pattern and characteristics of HCMV US12-US17 gene region were studied extensively. Twenty-three clones harboring US12 cDNA sequence were screened out from a late cDNA library of an HCMV clinical isolate, Han. Using a set of US12-US17 gene-specific probes, six transcripts from US12-US17 locus were detected by northern blot at late kinetics of the clinical isolate. One additional transcript was found in late RNA of HCMV strain AD169. No evidence showing these transcripts contain introns by reverse transcription PCR. 3' and 5' termini of these transcripts were confirmed by Rapid Amplification of cDNA Ends. A novel protein-coding region was predicted in the shorter US14 transcript with an alternative in-frame 5' translation initiation site compared to that of the previously predicted US14 ORF. Our findings demonstrate the existence of a cluster of 3' coterminal unspliced transcripts with distinct 5' transcriptional initiation sites originated from US12-US17 gene region in the late infection phase of an HCMV clinical strain.