mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning

As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention

A multi-subgroup predictive model based on clinical parameters and laboratory biomarkers to predict in-hospital outcomes of plasma exchange-centered artificial liver treatment in patients with hepatitis B virus-related acute-on-chronic liver failure

BackgroundPostoperative risk stratification is challenging in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) who undergo artificial liver treatment. This study characterizes patients’ clinical parameters and laboratory biomarkers with different in-hospital outcomes. The purpose was to establish a multi-subgroup combined predictive model and analyze its predictive capability.MethodsWe enrolled HBV-ACLF patients who received plasma exchange (PE)-centered artificial liver support system (ALSS) therapy from May 6, 2017, to April 6, 2022. There were 110 patients who died (the death group) and 110 propensity score-matched patients who achieved satisfactory outcomes (the survivor group). We compared baseline, before ALSS, after ALSS, and change ratios of laboratory biomarkers. Outcome prediction models were established by generalized estimating equations (GEE). The discrimination was assessed using receiver operating characteristic analyses. Calibration plots compared the mean predicted probability and the mean observed outcome.ResultsWe built a multi-subgroup predictive model (at admission; before ALSS; after ALSS; change ratio) to predict in-hospital outcomes of HBV-ACLF patients who received PE-centered ALSS. There were 110 patients with 363 ALSS sessions who survived and 110 who did not, and 363 ALSS sessions were analyzed. The univariate GEE models revealed that several parameters were independent risk factors. Clinical parameters and laboratory biomarkers were entered into the multivariate GEE model. The discriminative power of the multivariate GEE models was excellent, and calibration showed better agreement between the predicted and observed probabilities than the univariate models.ConclusionsThe multi-subgroup combined predictive model generated accurate prognostic information for patients undergoing HBV-ACLF patients who received PE-centered ALSS

QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated

Novel Roles of the MAP kinase-interacting kinases

The MAP kinase-interacting kinases (MNKs) are ubiquitously expressed in mammalian cells. They are activated through MAP kinase pathways and can phosphorylate the eukaryotic translation initiation factor 4E (eIF4E) at a single site. eIF4E plays a key role in protein synthesis and its control. eIF4E and its phosphorylation play important roles in cancer and tumorigenesis. As MNKs play important roles in several diseases, but are not essential to animal development, they may be good targets for cancer therapy. Our recent studies show that the MNKs contribute to the migration of cancer cells. The cytoplasmic FMRP-interacting protein 1 (CYFIP1) is reported as an eIF4E binding partner, and suppresses translation by binding with fragile X mental retardation protein (FMRP) and eIF4E. Our research demonstrates that inhibition of MNKs can also inhibit release of CYFIP1 from eIF4E, which may repress translation of certain mRNAs related to cell migration, including matrix metalloproteinases (MMP3, MMP9) and Vimentin (which serves as a marker of the epithelial-mesenchymal transition). Also, the MNKs were found to be involved in regulating the expression and phosphorylation of N-Myc Downstream Regulated 1 (NDRG1), a protein which is a well-known metastasis suppressor in breast cancers. In addition to functions in cell migration, our previous research shows that MNK2 affects fat mass of mice on a high fat diet. In addition, MNK2KO mice fed a high fat diet are protected from adipose tissue inflammation. Consequently, we focused on MNK functions in inflammation, and this thesis describes the effect by MNK inhibition on the morphology and other features of RAW macrophage cells.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 201

Relation between high density lipoprotein particles concentration and cardiovascular events: a meta-analysis

Abstract Background Trails aimed at raising high density lipoprotein(HDL) cholesterol concentration failed to make better cardiovascular outcomes. HDL particles may be better biomarkers reflecting properties of HDL. This meta-analysis was conducted to evaluate the relation between blood HDL particles level and cardiovascular events. Methods PubMed and other databases were searched for eligible studies and NewCastle-Ottawa Quality Assessment Scale(NOS) was used to assess the quality of included studies. A random or fixed-effect model was applied to calculate the pooled hazard ratio(HR). Results Twelve studies were finally included. The pooled HR(95%confidence interval) for per standard deviation(SD) increment and top quartile versus bottom quartile were 0.79(0.72,0.86) and 0.65(0.57,0.75), respectively. Subgroup analysis suggested that HR was significantly lower in subjects with a cardiovascular disease(CVD) history than that of people without established CVD. Subclass analysis indicated that HRs for per SD increment of small(0.85) and medium(0.84) HDL particles were significantly lower than that of large HDL particles(0.96). Conclusions HDL particle level in blood was inversely related to CVD events, indicating that HDL particles maybe a protective factor in patients with CVD, thus making HDL particles a potential biomarker and therapy target

DNA damage response activated by high calcium and phosphorus induces premature aging of human aortic smooth muscle cells

Objective To investigate the mechanism of DNA damage response(DDR) pathway regulating calcification in human aortic smooth muscle cells(HASMCs). Methods The HASMCs were divided into the control group, model group, ATM treatment group, and PARP treatment group, and they were cultured for 12 days. Cell calcification was measured by Alizarin red staining and σ-Cresolphthalein; phosphorylation levels of histone γH2AX, protein levels of p16 and p21, and phosphorylation levels of ATM on Ser1981 were tested by Western blot, premature cell senescence by β-galactosidase staining; and p16 and p21 mRNA by qPCR. The level of oxidative stress was measured by 8-hydroxy-2′-deoxyguanosine (8-OHDG), and the level of IL-6 and IL-8 was measured by ELISA kit. Results The calcification was evident in the model group as compared with that in control group. There were significant changes in 8-OHDG, histone γH2AX phosphorylation, β -galactosidase staining, mRNA and protein of p16, p21 mRNA, release of IL 6 and IL 8 and ATM phosphorylation(P＜0.05).The changes in the model group alleviated by ATM and PARP treatment. Conclusions High calcium and phosphorus environment stimulates HASMCs to produce sustained DNA damage, triggers ATM phosphorylation, activates p16 protein expression, and induces premature cell senescence causing cell death and resulted in calcification

Fusobacterium nucleatum bacteremia complicated with intracranial Porphyromonas gingivalis and HSV-1 infection: a case report and literature review

Abstract Background Fusobacterium nucleatum (F. nucleatum) belongs to the genus Fusobacterium, which is a gram-negative obligate anaerobic bacterium. Bacteremia associated with F. nucleatum is a serious complication, which is not common in clinic, especially when it is combined with other intracranial pathogenic microorganism infection. We reported for the first time a case of F. nucleatum bacteremia combined with intracranial Porphyromonas gingivalis (P. gingivalis) and herpes simplex virus type 1(HSV-1) infection. Case presentation A 60-year-old woman was admitted to our hospital with a headache for a week that worsened for 2 days. Combined with history, physical signs and examination, it was characterized as ischemic cerebrovascular disease (ICVD). F. nucleatum was detected in blood by matrix-assisted laser desorption/ionization time-offight mass spectrometry (MALDI-TOF-MS). Meanwhile, P. gingivalis and HSV-1 in cerebrospinal fluid (CSF) were identified by metagenome next generation sequencing (mNGS). After a quick diagnosis and a combination of antibiotics and antiviral treatment, the patient recovered and was discharged. Conclusion To our knowledge, this is the first report of intracranial P. gingivalis and HSV-1 infection combined with F. nucleatum bacteremia

DataSheet_1_A multi-subgroup predictive model based on clinical parameters and laboratory biomarkers to predict in-hospital outcomes of plasma exchange-centered artificial liver treatment in patients with hepatitis B virus-related acute-on-chronic liver failure.docx

BackgroundPostoperative risk stratification is challenging in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) who undergo artificial liver treatment. This study characterizes patients’ clinical parameters and laboratory biomarkers with different in-hospital outcomes. The purpose was to establish a multi-subgroup combined predictive model and analyze its predictive capability.MethodsWe enrolled HBV-ACLF patients who received plasma exchange (PE)-centered artificial liver support system (ALSS) therapy from May 6, 2017, to April 6, 2022. There were 110 patients who died (the death group) and 110 propensity score-matched patients who achieved satisfactory outcomes (the survivor group). We compared baseline, before ALSS, after ALSS, and change ratios of laboratory biomarkers. Outcome prediction models were established by generalized estimating equations (GEE). The discrimination was assessed using receiver operating characteristic analyses. Calibration plots compared the mean predicted probability and the mean observed outcome.ResultsWe built a multi-subgroup predictive model (at admission; before ALSS; after ALSS; change ratio) to predict in-hospital outcomes of HBV-ACLF patients who received PE-centered ALSS. There were 110 patients with 363 ALSS sessions who survived and 110 who did not, and 363 ALSS sessions were analyzed. The univariate GEE models revealed that several parameters were independent risk factors. Clinical parameters and laboratory biomarkers were entered into the multivariate GEE model. The discriminative power of the multivariate GEE models was excellent, and calibration showed better agreement between the predicted and observed probabilities than the univariate models.ConclusionsThe multi-subgroup combined predictive model generated accurate prognostic information for patients undergoing HBV-ACLF patients who received PE-centered ALSS.</p

<i>pelo</i> Is Required for High Efficiency Viral Replication

<div><p>Viruses hijack host factors for their high speed protein synthesis, but information about these factors is largely unknown. In searching for genes that are involved in viral replication, we carried out a forward genetic screen for Drosophila mutants that are more resistant or sensitive to <i>Drosophila C virus</i> (DCV) infection-caused death, and found a virus-resistant line in which the expression of <i>pelo</i> gene was deficient. Our mechanistic studies excluded the viral resistance of <i>pelo</i> deficient flies resulting from the known <i>Drosophila</i> anti-viral pathways, and revealed that <i>pelo</i> deficiency limits the high level synthesis of the DCV capsid proteins but has no or very little effect on the expression of some other viral proteins, bulk cellular proteins, and transfected exogenous genes. The restriction of replication of other types of viruses in <i>pelo</i> deficient flies was also observed, suggesting <i>pelo</i> is required for high level production of capsids of all kinds of viruses. We show that both <i>pelo</i> deficiency and high level DCV protein synthesis increase aberrant 80S ribosomes, and propose that the preferential requirement of <i>pelo</i> for high level synthesis of viral capsids is at least partly due to the role of <i>pelo</i> in dissociation of stalled 80S ribosomes and clearance of aberrant viral RNA and proteins. Our data demonstrated that <i>pelo</i> is a host factor that is required for high efficiency translation of viral capsids and targeting <i>pelo</i> could be a strategy for general inhibition of viral infection.</p></div

<i>pelo</i> deficiency inhibits viral replication.

<p>(<b>A–C</b>) Flies were challenged with DCV and then three pools of ten flies were collected at the indicated time points post-infection. The viral titer was determined by end-point dilution (<b>A</b>). The accumulation of DCV RNA was measured by qRT-PCR. Data represent the mean ± SD of triplicates (<b>B</b>). The accumulation of DCV capsid proteins was measured by immunoblotting with anti-DCV antibody (<b>C</b>). (<b>D</b> and <b>E</b>) S2 cells were untreated (Mock) or treated with dsRNAs against <i>GFP</i> (ds<i>GFP</i>) or <i>pelo</i> (ds<i>pelo</i>) for 6 days and then infected with DCV (MOI = 0.1). The accumulation of DCV RNA (<b>D</b>) or capsid proteins (<b>E</b>) was measured as described in <b>B</b> and <b>C</b>, respectively.</p