MACC: a visual interactive knowledgebase of metabolite-associated cell communications.

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.

Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. SIGNIFICANCE: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

MetNC: Predicting Metabolites in vivo for Natural Compounds.

Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets.

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients' Targets covering PubMed literatures 2000-2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into 'My-target' system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

Risk Estimation of Infectious and Inflammatory Disorders in Hospitalized Patients With Acute Ischemic Stroke Using Clinical-Lab Nomogram.

Background: Infections after acute ischemic stroke are common and likely to complicate the clinical course and negatively affect patient outcomes. Despite the development of various risk factors and predictive models for infectious and inflammatory disorders (IAID) after stroke, more objective and easily obtainable predictors remain necessary. This study involves the development and validation of an accessible, accurate nomogram for predicting in-hospital IAID in patients with acute ischemic stroke (AIS). Methods: A retrospective cohort of 2,257 patients with AIS confirmed by neurological examination and radiography was assessed. The International Statistical Classification of Diseases and Health related Problem's definition was used for IAID. Data was obtained from two hospitals between January 2016 and March 2020. Results: The incidence of IAID was 19.8 and 20.8% in the derivation and validation cohorts, respectively. Using an absolute shrinkage and selection operator (LASSO) algorithm, four biochemical blood predictors and four clinical indicators were optimized from fifty-five features. Using a multivariable analysis, four predictors, namely age (adjusted odds ratio, 1.05; 95% confidence interval [CI], 1.038-1.062; p < 0.001), comatose state (28.033[4.706-536.403], p = 0.002), diabetes (0.417[0.27-0.649], p < 0.001), and congestive heart failure (CHF) (5.488[2.451-12.912], p < 0.001) were found to be risk factors for IAID. Furthermore, neutrophil, monocyte, hemoglobin, and high-sensitivity C-reactive protein were also found to be independently associated with IAID. Consequently, a reliable clinical-lab nomogram was constructed to predict IAID in our study (C-index value = 0.83). The results of the ROC analysis were consistent with the calibration curve analysis. The decision curve demonstrated that the clinical-lab model added more net benefit than either the lab-score or clinical models in differentiating IAID from AIS patients. Conclusions: The clinical-lab nomogram predicted IAID in patients with acute ischemic stroke. As a result, this nomogram can be used for identification of high-risk patients and to further guide clinical decisions.

Budd-Chiari syndrome associated with liver cirrhosis: A case report.

BACKGROUND: Budd-Chiari syndrome (BCS) is a rare heterogeneous liver disease characterized by obstruction of the hepatic venous outflow tract. The incidence of BCS is so low that it is difficult to detect in general practice and difficult to include within the scope of routine diagnosis. The clinical manifestations of BCS are not specific; hence, BCS tends to be misdiagnosed. CASE SUMMARY: We report the case of a 33-year-old Chinese woman who presented with progressive distension in the upper abdomen. She was initially misdiagnosed with liver cirrhosis (LC) due to abnormalities on an upper abdominal computed tomography scan. Although she was taking standard anti-cirrhosis therapy, her symptoms did not improve. Magnetic resonance imaging showed caudate lobe hypertrophy; and dilated lumbar and hemiazygos veins. Venography revealed membranous obstruction of the inferior vena cava owing to congenital vascular malformation. A definitive diagnosis of BCS was made. Balloon angioplasty was performed to recanalize the obstructed inferior vena cava and the patient's symptoms were completely resolved. CONCLUSION: BCS lacks specific clinical features and can eventually lead to LC. Clinicians and radiologists must carefully differentiate BCS from LC. Correct diagnosis and timely treatment are vital to the patient's health.

H-RACS: a handy tool to rank anti-cancer synergistic drugs.

Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling. Based on the largest dataset of 33,574 combinational scenarios, we proposed a handy webserver, H-RACS, to overcome the above problems. Being loaded with chemical structures and target information, H-RACS can recommend potential synergistic pairs between candidate drugs on 928 cell lines of 24 prevalent cancer types. A high model performance was achieved with AUC of 0.89 on independent combinational scenarios. On the second independent validation of DREAM dataset, H-RACS obtained precision of 67% among its top 5% ranking list. When being tested on new combinations and new cell lines, H-RACS showed strong extendibility with AUC of 0.84 and 0.81 respectively. As the first online server freely accessible at http://www.badd-cao.net/h-racs, H-RACS may promote the pre-screening of synergistic combinations for new chemical drugs on unexplored cancers.

Comparative Network Pharmacology Analysis of Classical TCM Prescriptions for Chronic Liver Disease.

Chronic liver disease (CLD) has become a major global health problem while herb prescriptions are clinically observed with significant efficacy. Three classical Traditional Chinese Medicine (TCM) formulae, Yinchenhao Decoction (YCHT), Huangqi Decoction (HQT), and Yiguanjian (YGJ) have been widely applied in China to treat CLD, but no systematic study has yet been published to investigate their common and different mechanism of action (MOA). Partial limitation may own to deficiency of effective bioinformatics methods. Here, a computational framework of comparative network pharmacology is firstly proposed and then applied to herbal recipes for CLD disease. The analysis showed that, the three formulae modulate CLD mainly through functional modules of immune response, inflammation, energy metabolism, oxidative stress, and others. On top of that, each formula can target additional unique modules. Typically, YGJ ingredients can uniquely target the ATP synthesis and neurotransmitter release cycle. Interestingly, different formulae may regulate the same functional module in different modes. For instance, YCHT and YGJ can activate oxidative stress-related genes of SOD family while HQT are found to inhibit SOD1 gene. Overall, our framework of comparative network pharmacology proposed in our work may not only explain the MOA of different formulae treating CLD, but also provide hints to further investigate the biological basis of CLD subtypes.

SEPPA 3.0-enhanced spatial epitope prediction enabling glycoprotein antigens.

B-cell epitope information is critical to immune therapy and vaccine design. Protein epitopes can be significantly affected by glycosylation, while no methods have considered this till now. Based on previous versions of Spatial Epitope Prediction of Protein Antigens (SEPPA), we here present an enhanced tool SEPPA 3.0, enabling glycoprotein antigens. Parameters were updated based on the latest and largest dataset. Then, additional micro-environmental features of glycosylation triangles and glycosylation-related amino acid indexes were added as important classifiers, coupled with final calibration based on neighboring antigenicity. Logistic regression model was retained as SEPPA 2.0. The AUC value of 0.794 was obtained through 10-fold cross-validation on internal validation. Independent testing on general protein antigens resulted in AUC of 0.740 with BA (balanced accuracy) of 0.657 as baseline of SEPPA 3.0. Most importantly, when tested on independent glycoprotein antigens only, SEPPA 3.0 gave an AUC of 0.749 and BA of 0.665, leading the top performance among peers. As the first server enabling accurate epitope prediction for glycoproteins, SEPPA 3.0 shows significant advantages over popular peers on both general protein and glycoprotein antigens. It can be accessed at http://bidd2.nus.edu.sg/SEPPA3/ or at http://www.badd-cao.net/seppa3/index.html. Batch query is supported.

Effects of Impurity Doping on the Luminescence Performance of Mn4+-Doped Aluminates with the Magnetoplumbite-Type Structure for Plant Cultivation.

Mn4+ activated LaMgAl11O19 (LMA/Mn4+) with red emitting phosphor was obtained by sintering under air conditioning. The X-ray diffraction pattern Rietveld refinement results reveal that three six-fold coordinated Al sites are substituted by Mn4+ ions. Furthermore, the chemical valence state of manganese in the LMA host was further confirmed through X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR). Photoluminescence emission (PL) and excitation (PLE) spectra of LMA/Mn4+ as well as the lifetime were measured, and the 663 nm emission is ascribed to the ²Eg→4A2g from the 3d³ electrons in the [MnO6]8- octahedral complex. The emission spectrum matches well with the absorption of phytochrome. Temperature-dependent PL spectra show that the color changes of the phosphor at 420 K are 0.0110 for Δx and -0.0109 for Δy. Moreover, doping Zn2+ and Mg2+ ions in the host enhances the emission intensity of Mn4+ ions. These results highlight the potential of LMA/Mn4+ phosphor for a light-emitting diode (LED) plant lamp.

Chromaticity-Tunable and Thermal Stable Phosphor-in-Glass Inorganic Color Converter for High Power Warm w-LEDs.

In this work, an aluminate silicate garnet phosphor, Y2Mg2Al2Si2O12:Ce3+ (YMASG:Ce3+), exhibiting strong and broad yellow-orange emission, was successfully synthesized. Attributed to the double cation substitution of YAG:Ce3+, which led to a compression effect, a redshift was observed with respect to YAG:Ce3+. More importantly, a transparent phosphor-in-glass (PiG) sample was obtained by incorporating the phosphor YMASG:Ce3+ into a special low-melting precursor glass. The energy dispersive spectrometer (EDS) mapping analysis of the as-prepared PiG sample indicates that YMASG:Ce3+ was successfully incorporated into the glass host, and its powders were uniformly distributed in glass. The photoluminescence intensity of the PiG sample was higher than that of the powder due to its relatively high thermal conductivity. Additionally, the combination of the PiG sample and a blue high-power chip generated a modular white LED with a luminous efficacy of 54.5 lm/W, a correlated color temperature (CCT) of 5274 K, and a color rendering index (CRI) of 79.5 at 350 mA.

Effect of bacteria proportion on the fermentation of goat yoghurt with probiotic culture.

BACKGROUND: Goat milk production in Shaanxi province is dominant in China, but the product is mainly infant formula and adult milk powder; product homogeneity is serious and has no goat yoghurt with probiotic culture. METHODS: The effect of bacteria proportion (1:3:1, 1:2:1, 1:1:1, 2:1:1, 3:1:1) on pH, acidity, and viable counts and sensory evaluation of goat milk fermented by probiotics including L. acidophilus, B. bifidum  or L. casei besides, S. thermophilus and L. bulgaricus for developing AB-goat yoghurt and BC-goat yoghurt was investigated. RESULTS: The optimum bacteria proportion of L. acidophilus : B. bifidum : S. thermophilus and L. bulgaricus for AB-goat yoghurt and B. bifidum : L. casei : S. thermophilus and L. bulgaricus for BC-goat yoghurt were both 2:1:1. The pH, acidity, the viable counts of L. acidophilus and B. bifidum, the total viable counts were respectively 4.60, 7.73 (g/L), 3.50×107 cfu/mL, 3.40×107 cfu/mL and 2.30×109 cfu/mL in AB-goat yoghurt. The pH, acidity, the viable counts of B. bifidum and L. casei, the total viable counts were respectively  4.61, 8.16 (g/L), 7.60×107 cfu/mL, 5.60×107 cfu/mL and 2.04×109 cfu/mL in BC-goat yoghurt. CONCLUSIONS: The bacteria proportion had a significant effect on fermentation of AB- and BC-goat yoghurt, the results are beneficial for developing AB-goat yoghurt and BC-goat yoghurt.