Serum Exosomal Long Noncoding RNA Growth Arrest-Specific 5 Predicts 3-Month Mortality in Acute-on-Chronic Hepatitis B Liver Failure

Background

Acute-on-chronic hepatitis B liver failure (ACHBLF) is a clinical syndrome with an extremely high mortality. In this study, we aim to evaluate the potential role of serum exosomal long noncoding RNA (lncRNA) growth arrest-specific 5 (GAS5) in ACHBLF and its predictive value for 3-month mortality.

Methods

From December 2017 to June 2022, we enrolled 110 patients with ACHBLF and 42 healthy controls (HCs). Exosomes were isolated from the serum of the participants. Serum exosomal lncRNA GAS5 was detected using quantitative real-time polymerase chain reaction (qRT-PCR). The functional role of lncRNA GAS5 on hepatocyte phenotypes was investigated through loss-of-function and gain-of-function assays. Exosomal labeling and cell uptake assay were used to determine the exosomes-mediated transmission of lncRNA GAS5 in hepatocytes in vitro.

Results

The serum exosomal lncRNA GAS5 was identified to be an independent predictor for 3-month mortality of ACHBLF. It yielded an area under the receiver operating characteristic curve (AUC) of 0.88, which was significantly higher than MELD score (AUC 0.73; P < 0.01). Further study found that lncRNA GAS5 could inhibit hepatocytes proliferation and increase hepatocytes apoptosis. Exosomes-mediated lncRNA GAS5 transfer promoted hepatocytes injury. The knocked down of lncRNA GAS5 weakened H2O2-induced hepatocytes injury.

Conclusion

We revealed that serum exosomal lncRNA GAS5 might promote hepatocytes injury and showed high predictive value for 3-month mortality in ACHBLF.

Decreased GPX3 mRNA level in peripheral blood mononuclear cells is associated with HBV-related hepatocellular carcinoma

BACKGROUND

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignancies with increasing mortality. In this study, we aim to determine the alteration and diagnostic value of GXP3 expression for HBV-related HCC.

METHODS

We recruited 243 subjects, including 132 HBV-related HCC patients, 78 chronic hepatitis B (CHB) patients and 33 healthy controls (HCs). The mRNA level of GPX3 in peripheral blood mononuclear cells (PBMCs) was assessed by quantitative real-time PCR. The GPX3 plasma level was detected by ELISA.

RESULTS

The GPX3 mRNA level was significantly decreased in HBV-related HCC patients compared with in CHB patients and HCs (p<0.05). The plasma GPX3 level was significantly lower in patients with HBV-related HCC than in CHB patients and HCs (p<0.05). In the HCC subgroup, the GPX3 mRNA level was significantly lower in patients with positive HBeAg, ascites, advanced stage and poor differentiation compared with in the other groups (p<0.05). The receiver operating characteristic curve was constructed to estimate the diagnostic value of the GPX3 mRNA level for HBV-related HCC. The GPX3 mRNA level showed a significantly better diagnostic ability compared with alpha fetoprotein (AFP) (area under the curve 0.769 vs 0.658, p<0.001).

CONCLUSIONS

A decreased GPX3 mRNA level might be a potential non-invasive biomarker for HBV-related HCC. It showed better diagnostic ability than AFP.

Genetic analysis of longevity and their associations with fertility traits in Holstein cattle

The increase of longevity is intended to reduce involuntary culling rates, not extend the life span, and it reflects the ability of animals to successfully cope with the environment and disease during production. Sire model, animal model and repeatability animal models were used to estimate the (co) variance components of longevity and fertility traits. Six longevity and thirteen fertility traits were analysed, including herd life (HL), productive life (PL), number of days between first calving and the end of first lactation or culling (L1); number of days between first calving and the end of the second lactation or culling (L2); number of days between first calving and the end of the third lactation or culling (L3); number of days between first calving and the end of the fourth lactation or culling (L4); age at first service, age at first calving (AFC), the interval from first to last inseminations in heifer (IFLh), conception rate of first insemination in heifer, days open (DO), calving interval, gestation length, interval from calving to first insemination (ICF), interval from first to last inseminations in cow (IFLc), conception rate of first insemination in cow, calving ease (CE), birth weight, and calf survival. The estimated heritabilities (±SE) were 0.018 (±0.003), 0.015 (±0.003), 0.049 (±0.004), 0.025 (±0.003), 0.009 (±0.002) and 0.011 (±0.002) for HL, PL, L1, L2, L3 and L4, respectively. Strong correlations were appeared in HL and PL; the genetic and phenotypic correlation coefficients were 0.998 and 0.985, respectively. There were high genetic and phenotypic correlations which were observed in L1 and L2, L2 and L3, L3 and L4, respectively. All fertility traits of heifer showed medium to high heritability, while the cow showed low heritability. All heifer fertility traits had low genetic associations with longevity traits, ranging from -0.018 (L2 and IFLh) to 0.257 (L3 and AFC). Most of the fertility traits showed negative correlations with longevity traits in different parities, and we recommend DO, ICF, IFLc and CE as indirect indicators of longevity traits in dairy cows, but we also need to take into account the differences between parities.

[A meta-analysis of risk factors for multidrug-resistant tuberculosis in China]

Objective: To explore the main risk factors of multidrug-resistant tuberculosis (MDR-TB) in China and to provide evidence-based evidence for MDR-TB preventon and control. Methods: All relevant literatures were searched in thedatabases, such as Pubmed, Web of Science and CNKI, Wanfang, VIP and SinoMed from 2000 to 2021. Quality evaluation and data extraction were carried out, and then a meta-analysis was performed using Stata 16.0 software. Results: A total of 59 literatures (36 cross-sectional and 23 case-control) including 75 793 participants were included in this study, and meta-analysis results showed age (OR=1.27, 95%CI: 1.05-1.54), education level (OR=1.29, 95%CI: 1.02-1.65), positive sputum smear (OR=2.56, 95%CI: 1.09-6.04), pulmonary cavity (OR=1.99, 95%CI: 1.57-2.52), course of disease (OR=4.25, 95%CI: 1.95-9.30), history of tuberculosis treatment (OR=6.42,95%CI:5.40-7.63), treatment interruption (OR=2.81, 95%CI: 1.50-5.29), irregular medication (OR=5.02, 95%CI: 2.95-8.54), adverse drug reactions (OR=4.27, 95%CI: 2.22-8.19), combined chronic obstructive pulmonary disease (COPD) (OR=2.21, 95%CI: 1.45-3.37), tuberculosis exposure history (OR=1.99, 95%CI: 1.36-2.91), smoking history (OR=1.35, 95%CI: 1.09-1.66) and floating population (OR=1.60, 95%CI: 1.04-2.44) were associated with the occurrence of MDR-TB. Conclusions: The high risk groups were farmer, low education level, pulmonary cavity, long course of disease, history of tuberculosis treatment, treatment interruption, irregular medication, adverse drug reaction, co-COPD, contact history of tuberculosis, smoking history, rural residence, and floating population. We should pay attention to high-risk groups, strengthen management and take effective measures such as early screening, knowledge education on tuberculosis, standardized and personalized treatment and whole-course supervision.

Early prediction model for prognosis of patients with hepatitis-B-virus-related acute-on-chronic liver failure received glucocorticoid therapy

Background

Early prediction for short-term prognosis is essential for the management of hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF). In this study, we aim to establish a noninvasive model for predicting the 90-day mortality in patients with HBV–ACLF received glucocorticoid therapy.

Methods

Two hundred and eighty patients with HBV–ACLF were enrolled from July 2010 to June 2022. All patients received routine medicine treatment and 204 of them received additional glucocorticoid treatment. Then, the patients who received glucocorticoid treatment were randomly divided into a training cohort and a validation cohort. An early prediction model for 90-day mortality of HBV–ACLF was established in the training cohort and then validated in the validation cohort.

Results

HBV–ACLF patients received glucocorticoid treatment showed significantly better survival that those not (P < 0.01). In the training cohort, a noninvasive model was generated with hepatic encephalopathy grade, INR, total bilirubin, age and SIRS status, which was named HITAS score. It showed significantly better predictive value for 90-day mortality of HBV–ACLF than MELD score and Child–Turcotte–Pugh score in both the training cohort and validation cohort. Using the Kaplan–Meier analysis with cutoff points of 2.5 and 3.47, the HITAS score can classify HBV–ACLF patients into different groups with low, intermediate and high risk of death after glucocorticoid therapy.

Conclusions

We proposed a HITAS score, which was an early prediction model for the prognosis of HBV–ACLF. It might be used to identify HBV–ACLF patients with favorable responses to glucocorticoid treatment.

[Predictions of achievement of Sustainable Development Goal to reduce age-standardized mortality rate of four major non-communicable diseases by 2030 in China]

Objective: To predicate whether China can achieve the United Nations Sustainable Development Goals (SDGs) 3.4.1 to reduce the age-standardized mortality rate of four major non-communicable diseases (NCDs) in residents aged 30-70 years by 2030 based on the trend of the mortality from 1990 to 2019. Methods: We collected the mortality data on cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes by age, gender and year in China from the Global Disease Burden Study 2019 (GBD2019). The age-period-cohort (APC) Bayesian model was applied for modeling the age-standardized mortality rate of four major NCDs in China during 2020-2030 according to the trend of the mortality during 1990-2019, and comparing the predicted value in 2030 with the observed value in 2015 to evaluate the possibility of achieving SDGs 3.4.1. Results: The age-standardized mortality rate of the four major NCDs in China showed a downward trend during 1990-2019. It is predicted that the number of death of the four NCDs in Chinese residents aged 30-70 years would increase from 2.96 million in 2020 to 3.19 million in 2030, while the age-standardized mortality rate would decrease from 308.49/100 000 in 2020 to 277.80/100 000 in 2030. The age-standardized mortality rate in 2030 would only decrease by 15.94% (18.73% for males and 14.31% for females) compared with 330.46/100 000 in 2015, with a 25.09% decrease for cardiovascular diseases, 4.76% for cancers, 37.21% for chronic respiratory diseases, and unchanged for diabetes. Conclusion: Although the age-standardized mortality rate of four major NCDs declined from 1990 to 2019 in China, it is difficult to achieve the SDGs of a 1/3 mortality rate reduction by 2030 according to the current declining trend, suggesting more active and effective efforts for NCD prevention and control are needed.

[Clinicopathological characteristics and molecular alterations of primary cardiac leiomyosarcoma: report of five cases]

Objective: To investigate the clinical, pathologic and radiologic features and molecular alterations in patients with primary cardiac leiomyosarcoma (PCLMS). Methods: Five cases of PCLMS were collected in Beijing Anzhen Hospital from January 2016 to December 2020. The clinical, pathologic and radiologic data, and molecular alterations were analyzed, and the patients were followed up. Results: All five patients were female, and had no history of leiomyosarcoma in other parts of the body. The age of patients ranged from 37 to 62 years (median 47 years). The main clinical symptoms were chest pain and dyspnea, one also presented with palpitation and lower limb weakness and one with dizziness. Two tumors were located in the left atrium, two in the right atrium, and one in the right ventricle, and they maximal diameter ranged from 2.5 to 14.0 cm (mean 6.2 cm). The neoplasms presented as medium-echo masses with a broad base in the echocardiography, and as a low-density, solid mass when detected by contrast-enhanced CT. Histologically, two tumors were well-differentiated and three were moderately and poorly differentiated, and two included extensive, loose myxoid stroma. Immunohistochemical staining showed that PCLMS was positive for SMA, desmin, MDM2, and epidermal growth factor receptor. Fluorescence in situ hybridization showed ALK gene rearrangement in two cases, and COL1A1-PDGFB fusion in three cases. All cases received surgical excision and two cases received chemotherapy. Three patients died within 0-11 months (mean survival of 7.7 months) and two patients were alive. Conclusions: PCLMS is a malignant tumor with a high recurrence rate and poor prognosis. These cases may provide useful information to improve the diagnosis and management of PCLMS.

Serum exosomal long noncoding RNA nuclear-enriched abundant transcript 1 predicts 90-day mortality in acute-on-chronic hepatitis B liver failure

Objectives: Acute-on-chronic hepatitis B liver failure (ACHBLF) is characterized by high short-term mortality, calling for accurate prognostic biomarkers. This study aims to evaluate the predictive value of serum exosomal long noncoding RNA nuclear-enriched abundant transcript 1 (lncRNA NEAT1) for 90-day mortality of ACHBLF.Methods: This prospective study consisted of 113 ACHBLF patients from June 2013 to June 2017 as a training cohort and 72 ACHBLF patients from July 2017 to June 2020 as a validating cohort. LncRNA NEAT1 was detected using quantitative real-time polymerase chain reaction from serum exosomes.Results: LncRNA NEAT1 levels were higher in non-survivors than survivors (P< 0.01). In the training cohort, lncRNA NEAT1 (HR 1.049, 95%CI 1.023-1.075, P< 0.001) was an independent predictor for 90-day mortality of ACHBLF. Meanwhile, lncRNA NEAT1 showed significantly higher area under the curve of receiver operating characteristic (AUC) than MELD score in the training and validation cohort (P< 0.05, respectively). However, no significant difference was found in AUC between lncRNA NEAT1 and NEAT1 plus MELD score (P> 0.05). ACHBLF patients with lncRNA NEAT1 levels above 1.92 showed poorer survival condition than those below (P< 0.01).Conclusions: The serum exosomal lncRNA NEAT1 might be a better prognostic biomarker than MELD score for 90-day mortality of ACHBLF.

[Application of deep learning neural network in pathological image classification of non-inflammatory aortic membrane degeneration]

Objective: To investigate the value of deep learning in classifying non-inflammatory aortic membrane degeneration. Methods: Eighty-nine cases of non-inflammatory aortic media degeneration diagnosed from January to June 2018 were collected at Beijing Anzhen Hospital, Capital Medical University, China and scanned into digital sections. 1 627 hematoxylin and eosin stained photomicrographs were extracted. Combined with the ResNet18-based deep convolution neural network model, 4-category classification of pathological images were performed to diagnose the non-inflammatory aortic lesion. Results: The prediction model of artificial intelligence assisted diagnosis had the best accuracy, sensitivity and precision in identifying lesions with smooth muscle cell nuclei loss, which were 99.39%, 98.36% and 98.36%, respectively. The classification accuracy of elastic fiber fragmentation and/or loss lesions was 98.08%, while that of intralamellar mucoid extracellular matrix accumulation lesions was 96.93%. The overall accuracy of the classification model was 96.32%, and the area under the curve was 0.982. Conclusions: The accuracy of deep learning neural network model in the 4-category classification of non-inflammatory aortic lesionsis confirmed based on digital photomicrographs. This method can effectively improve the diagnostic efficiency of pathologists.

[Current status and projection of non-communicable diseases in 126 countries participating in the Belt and Road initiative]

Objective: To compare the indicators of non-communicable diseases (NCD) and predict the achieving time of United Nations (UN) Sustainable Development Goals (SDG) in 125 countries participating in the Belt and Road (B&R) initiative and China. Methods: Using the open access data of Global Burden of Disease study, we first got the premature mortality rates of four main chronic diseases (cardiovascular disease, cancer, diabetes and chronic respiratory diseases) and suicide mortality rate in the 126 countries from1990 to 2017. We transformed the value of each indicator into a scale of 0-100 in percentile for each country and applied geometric mean to calculate total NCD score for comparison among 126 countries. We then examined the association of NCD scores with socio-demographic index (SDI) values. Finally, we used annualized rates of change during 1990-2015 to predict achieving time of the UN goal by 2030 for each indicator of chronic diseases premature mortality rate and suicide mortality rates in each B&R country. Results: The integral median of total NCD score in the 126 countries in 2017 was 82.7. The score of China was 87.6, ranking 33(rd). The top three countries were Kuwait (98.1), Peru (97.5) and Italy (96.0). The last three countries were Papua New Guinea (28.9), Vanuatu (54.7) and Ukraine (58.0). The total NCD score showed positive correlation with SDI values (r=0.33) mainly due to chronic disease indicator (r=0.45). Fifteen countries will achieve the SDG goal of chronic disease premature mortality in or before 2030, but China will achieve it in 2038. Fifteen countries are expected to achieve the goal of suicide mortality, and China will acheive the goal ahead of schedule in 2024. Conclusions: The NCD rates varied widely among the countries along B&R. It is a challenge to achieve the SDG goal of chronic disease premature mortality rate by 2030 for China. In order to achieve the SDG goals by 2030, we should strengthen multilateral cooperation and complement each other's advantages, and reduce NCD mortality of people and improve people's health in countries along B&R.

[The therapeutic effect of carnosine combined with dexamethasone in the lung injury of seawater-drowning]

Objective: To explore the therapeutic effect of carnosine and dexamethasone in lung injury caused by seawater drowning. Methods: The in vitro experiments with A549 cells were divided into 5 groups: blank control group (C), seawater injury group (S), seawater injury+dexamethasone treatment group (S+D), seawater injury+carnosine treatment group (S+C), seawater injury dexamethasone and carnosine combined therapy(S+D+C) group. The optimal therapeutic dose of drugs for the treatment of seawater drowning lung injury was tested in vitro. Based on the optimal dose, the levels of TNF-α and IL-6 in each group at different time points were detected at the cell level by ELISA. The level of apoptosis was detected by flow cytometry. The in vivo experiments with SD rats were randomly divided into 5 groups (n=8 each): blank control group (RC),seawater drowning injury group (RS),seawater drowning injury+dexamethasone treatment group (RSD),seawater drowning injury+carnosine treatment group (RSC),seawater drowning injury+dexamethasone+carnosine combined treatment group (RSDC). The animal model with seawater inhalation acute lung injury was made by intratracheal infusion (4 ml/kg). The pathological changes of the lungs were observed. The expression of superoxide dismutase (SOD) in each group was detected by Western blot. Results: The results of in vitro experiments showed significant increase of apoptosis after seawater injury. The normal cell rate in group C was 98.3% while the apoptosis rate was 1.7%. The normal cell in group S was 18.8%, and the apoptosis rate was 81% (P<0.01). TNF-α and IL-6 levels in group S increased to 180.25 ng/L and 61.56 ng/L, respectively, which were statistically significant compared with group C (P<0.01). After drug protection, apoptosis was reduced in S+D group, S+C group and S+D+C group, with apoptosis rates of 65.4%, 70.9% and 42.6%, respectively. The contents of TNF-α and IL-6 also decreased in the S+D+C group (P<0.01). The results of in vivo experiments showed obvious lung injury and disordered lung tissue structures in the RS group at 4 h after modeling. There was hemorrhage in the pulmonary interstitium and a large number of inflammatory cells. Results of western blot showed that the expression of SOD increased in the RS group. Compared with RS group, the treatment alleviated acute lung injury and decreased the expression level of SOD in RSD, RSC and RSDC groups (P<0.01). Conclusion: Dexamethasone and carnosine reduced the influence of seawater inhalation on the lung in the rat model. The positive effect of combination of these two drugs on lung injury caused by seawater inhalation was stronger than a single drug.

[Emission Characteristics and Risk Assessment of Volatile Organic Compounds from Typical Factories in Zhengzhou]

To understand the characteristics and potential hazards of volatile organic compounds (VOCs) emitted from different industrial factories in Zhengzhou, several representative factories have been selected for sample collection using canisters; the samples were subsequently analyzed by GC-MS/FID system, from which the composition and risk of VOCs are discussed in this study. It was found that OVOCs, especially ethyl acetate and isopropanol, were the most important species originating from printing factories, which accounted for more than 93.1% of total VOCs. The major components related to manufacturing industries, including automobile, furniture, and coating, were aromatics, mainly m/p-xylene, o-xylene, and ethylbenzene, which contributed 33.5%-90.0% to VOCs. Halogenated hydrocarbons made the largest contribution (52.3%) to VOCs in the food processing industry. The main components of VOCs were halogenoalkanes (25.5%) and alkanes (28.8%) in rubber factories. As for graphite carbon factories, the main components of VOCs were aromatics (28.5%) and alkanes (24.1%). Compared with previous studies, the VOC emission characteristics of factories involving solvent usage in Zhengzhou are consistent with those in other cities, but the compositional information of VOCs varies across different factories, even within the same industry, due to the different production processes and raw materials used. Risk assessment showed that the concentration of VOCs emitted from solvent factories are positively correlated with ozone formation potential (OFP) and the hazard index (HI). Specifically, benzene, toluene, ethylbenzene, xylene, and other C₆-C₈ aromatic hydrocarbons contributed significantly to OFP and HI. The HI values were 1.18 and 2.74 in automobile manufacturing factory NO.3 and wooden furniture factory NO.5, respectively, which were higher than the limits stated by EPA regulations because of the different production processes and raw materials, and the VOCs of the factories were mainly composed of aromatics; in particular, C₆-C₉ benzene series contributed significantly to HI and OFP. Therefore, it is necessary to control VOCs originating from industries involving solvent usage.

Hypomethylation of the cyclin D1 promoter in hepatitis B virus-associated hepatocellular carcinoma

The hypomethylation of the Cyclin D1 (CCND1) promoter induced by excess oxidative stress likely promotes the development of hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC). We aimed to evaluate methylation status of the CCND1 promoter as a new plasma marker for the detection of HBV-HCC.We consecutively recruited 191 participants, including 105 patients with HBV-HCC, 54 patients with chronic hepatitis B (CHB), and 32 healthy controls (HCs). Using methylation-specific polymerase chain reaction, we identified the methylation status of the CCND1 promoter in plasma samples. We analyzed the expression levels of the CCND1 mRNA in peripheral blood mononuclear cells by using quantitative real-time PCR. We assessed the plasma levels of superoxide dismutase, 8-hydroxydeoxyguanosine and malondialdehyde by using enzyme-linked immunosorbent assays.Patients with HBV-HCC (23.81%) presented a reduced methylation frequency compared with patients with CHB (64.81%) or HCs (78.13%) (P < .001). When receiver operating characteristic curves were plotted for patients with HBV-HCC versus CHB, the methylation status of the CCND1 promoter yielded diagnostic parameter values for the area under the curve of 0.705, sensitivity of 76.19%, and specificity of 64.81%, thus outperforming serum alpha-fetoprotein (AFP), which had an area under the curve of 0.531, sensitivity of 36.19%, and specificity of 90.74%. Methylation of the CCND1 promoter represents a prospective diagnostic marker for patients with AFP-negative HBV-HCC and AFP-positive CHB. The expression levels of CCND1 mRNA was increased in patients with HBV-HCC compared with patients with CHB (Z = -4.946, P < .001) and HCs (Z = -6.819, P < .001). Both the extent of oxidative injury and antioxidant capacity indicated by the superoxide dismutase, 8-hydroxydeoxyguanosine and malondialdehyde levels were increased in patients with HBV-HCC. Clinical follow up of patients with HBV-HCC revealed a worse overall survival (P = .012, log-rank test) and a decreased progression-free survival (HR = 0.109, 95%CI: 0.031-0.384) for the unmethylated CCND1 group than methylated CCND1 group.Our study confirms that oxidative stress appears to correlate with plasma levels of CCND1 promoter methylation, and the methylation status of the CCND1 promoter represents a prospective biomarker with better diagnostic performance than serum AFP levels.

Hypomethylated Ubiquitin-Conjugating Enzyme2 Q1 (UBE2Q1) Gene Promoter in the Serum Is a Promising Biomarker for Hepatitis B Virus-Associated Hepatocellular Carcinoma

Aberrant DNA methylation, which can be detected in circulating cell-free DNA (cfDNA), is one of the major epigenetic alterations in hepatocellular carcinoma (HCC). UBE2Q1, a putative member of the ubiquitin-conjugating enzyme family, might play substantial roles in tumorigenesis. However, the methylation status of the UBE2Q1 gene in HCC remains unknown. We aimed to determine the methylation status of the UBE2Q1 gene promoter and to evaluate its potential clinical significance for HCC detection. The methylation-specific polymerase chain reaction (MSP) assay was used to detect the UBE2Q1 gene methylation status in serum samples from 80 patients with hepatitis B virus (HBV)-related HCC, 40 patients with liver cirrhosis (LC), 40 patients with chronic hepatitis B (CHB), and 20 healthy controls (HCs). Significantly lower methylation frequencies were detected in HCC patients (33.75%) compared with LC patients (55.00%, p = 0.026) and CHB patients (60.00%, p = 0.006) and HCs (65.00%, p = 0.011). Hypomethylation of the UBE2Q1 gene was negatively associated with the tumor node metastasis stage (r_s = -0.30, p = 0.008). The UBE2Q1 gene methylation status combined with alpha fetoprotein using cut-off points of 20, 200 and 400 ng/ml showed sensitivity and specificity values of 58.8% and 75.0%, 53.8% and 87.5%, and 37.5% and 88.7%, respectively, and yielded a significantly increased area under the ROC curve (0.720, 0.760 and 0.694, respectively) for discriminating HCC from LC and CHB. Our study results suggest that hypomethylation of the UBE2Q1 gene promoter is a potential biomarker for detecting HBV-associated HCC.

A noninvasive model to predict liver histology in HBeAg-positive chronic hepatitis B with alanine aminotransferase ≤ 2upper limit of normal

BACKGROUND AND AIM

Liver biopsy remains the gold standard to evaluate liver histology. However, it has several limitations. This study aims to construct a noninvasive model to predict liver histology for commencing antiviral therapy in HBeAg-positive chronic hepatitis B (CHB) with aminotransferase (ALT) ≤ 2 upper limit of normal (ULN).

METHODS

Two hundred and ninety-eight patients with HBeAg-positive CHB, ALT ≤ 2ULN and HBV-DNA ≥20 000 IU/ml were enrolled and randomly divided into a training group and a validation group. A noninvasive model was constructed in the training group to predict significant liver histological change [necroinflammatory activity grade (G) ≥ 2 or fibrosis stage (S) ≥ 2] and then validated in the validation group.

RESULTS

Aspartate aminotransferase, HBsAg, platelet, and albumin were identified as independent predictors. A model was constructed by them. It had an area under the receiver operating characteristic curve of 0.875 in the training group, 0.858 in the validation group and 0.868 in the entire cohort. Using a cut-off point of -0.96, it showed 93% sensitivity, 90% negative predictive value (NPV) in the training group and 95% sensitivity, 94% NPV in the validation group. Using a cut-off point of 0.96, it showed 95% specificity, 91% positive predictive value (PPV) in the training group and 89% specificity, 80% PPV in the validation group.

CONCLUSIONS

This study constructed a noninvasive model to predict liver histology in HBeAg-positive CHB with ALT ≤ 2ULN, which might reduce the clinical need for liver biopsy.

Overexpression of serum sST2 is associated with poor prognosis in acute-on-chronic hepatitis B liver failure

BACKGROUND AND OBJECTIVE

Interleukin-33 (IL-33) and soluble ST2 (sST2) have been demonstrated to be involved in liver injury. The present study aims to evaluate serum IL-33 and sST2 level in acute-on-chronic hepatitis B liver failure (ACHBLF) and determine their predictive value for prognosis.

METHODS

Serum IL-33 and sST2 level in patients with ACHBLF, chronic hepatitis B (CHB) and healthy controls (HCs) were determined by enzyme-linked immunosorbent assay (ELISA). Clinical and laboratory parameters were obtained.

RESULTS

Serum IL-33 was significantly higher in patients with ACHBLF (313.10±419.97pg/ml) than those with CHB (97.25±174.67pg/ml, P<0.01) and HCs (28.39±6.53pg/ml, P<0.01). Serum sST2 was significantly higher in patients with ACHBLF (1545.87±1135.70pg/ml) than those with CHB (152.55±93.28pg/ml, P<0.01) and HCs (149.27±104.90pg/ml, P<0.01). In all participants, serum IL-33 was significantly correlated with sST2 (r=0.43, P<0.01). In patients with ACHBLF, serum IL-33 was significantly correlated with alanine aminotransferase (ALT; r=0.26, P=0.04). Serum sST2 was significantly correlated with total bilirubin (TBIL; r=0.59, P<0.01), Log10 [HBV DNA] (r=-0.47, P<0.01) and model for end-stage liver diseases (MELD; r=0.28, P=0.03). Serum sST2 had an area under the receiver operating characteristic curve (AUC) of 0.81 in predicting 3-month mortality of ACHBLF. Patients with ACHBLF who had sST2 >1507pg/ml showed significantly poorer survival than those who had sST2 ≤1507pg/ml (P<0.01). Moreover, measurement of sST2 and MELD together significantly improved the diagnostic value of MELD alone (P<0.05).

CONCLUSIONS

Our study showed that serum IL-33 and sST2 were overexpressed in ACHBLF and sST2 might potentially serve as a prognostic marker for it.

Aberrant DNA methylation of G-protein-coupled bile acid receptor Gpbar1 (TGR5) is a potential biomarker for hepatitis B Virus associated hepatocellular carcinoma

BACKGROUND

G-protein-coupled bile acid receptor Gpbar1 (TGR5) is a newly identified liver tumor suppressor in carcinogenesis. This present study was therefore to determine the potential value of serum TGR5 promoter methylation in identifying hepatocellular carcinoma (HCC) from chronic hepatitis B (CHB) patients.

METHODS

The circulating cell-free DNA (cfDNA) was extracted from a retrospective dataset including 160 HCC, 88 CHB and 45 healthy controls (HCs). Methylation status of TGR5 promoter was examined by methylation-specific polymerase chain reaction (MSP).

RESULTS

Hypermethylation of the TGR5 promoter occurred significantly more frequent in HCC (77/160, 48.13%) than CHB (12/88, 13.64%; p<0.01) and HCs (2/45, 4.44%; p<0.01). The methylation rate of TGR5 in HCC patients ≥60 years old was significantly higher than those <60 years old (p<0.05). Alpha fetoprotein (AFP) had sensitivity of 58.13%, 30.63% and 24.38% at cut-off points of 20, 200 and 400ng/ml respectively; while TGR5 methylation combined AFP had sensitivity of 81.25%, 68.13% and 65%. AFP had specificity of 47.73%, 92.05% and 98.86% at cut-off points of 20, 200 and 400ng/ml respectively; while TGR5 methylation combined AFP had specificity of 38.64%, 78.41% and 85.23%. AFP had Youden index of 0.06, 0.23 and 0.23 at cut-off points of 20, 200 and 400ng/ml respectively; while TGR5 methylation combined AFP had Youden index of 0.20, 0.47 and 0.50.

CONCLUSIONS

Our findings strongly suggested the combination of serum TGR5 promoter methylation and AFP enhanced the diagnostic value of AFP alone in discriminating HCC from CHB patients.

[Hypomethylation of TNF-alpha gene promoter in the patients with acute-on-chronic hepatitis B liver failure]

OBJECTIVE

The present study was designed to investigate the possible epigenetic alteration in the promoter of TNF-alpha in the patients with acute-on-chronic hepatitis B liver failure (ACHBLF).

METHODS

The methylation of TNF-alpha promoter in peripheral blood mononuclear cells (PBMCs) was measured by methylation specific PCR (MSP). The level of serum TNF-alpha was determined by enzyme-linked immunosorbent assay (ELISA). Model for End-stage Liver Disease (MELD) was performed for the evaluation of liver failure.

RESULTS

The serum level of TNF-alpha in patients with ACHBLF(44.9260 +/- 26.48523) was higher than that in CHB (18.92505 +/- 9.04461) and healthy controls (11.9172 +/- 5.04612) (P < 0.05). Moreover, the serum TNF-alpha level was significantly decreased in methylation group as compared to unmethylaiton group in patients with ACHBLF (P < 0.05). MELD was not significantly different between methylated and unmethylated group of ACHBLF patients (P > 0.05). In addition, the serum level of TNF-alpha was found to be positively correlated with serum total bilirubin (r = 0.891, P < 0.01) and MELD score (r = 0.792, P < 0.01), but to be negatively correlated with prothrombin activity (r = - 0.511, P < 0.05) in patients with ACHBLF.

CONCLUSION

The TNF-alpha methylation patten is stable for the liver failure, suggesting the effect of environment on methylation.

Associations between single-nucleotide polymorphisms (+45T>G, +276G>T, -11377C>G, -11391G>A) of adiponectin gene and type 2 diabetes mellitus: a systematic review and meta-analysis

AIMS/HYPOTHESIS

The associations between adiponectin polymorphisms and type 2 diabetes have been studied widely; however, results are inconsistent.

METHODS

We searched electronic literature databases and reference lists of relevant articles. A fixed or random effects model was used on the basis of heterogeneity. Sub-group and meta-regression analyses were conducted to explore the sources of heterogeneity.

RESULTS

There were no statistically significant associations between +45T>G (rs2241766), +276G>T (rs1501299), -11391G>A (rs17300539) and type 2 diabetes risk. However, for -11377C>G (rs266729), the pooled OR (95% CI) for G vs C allele was 1.07 (1.03-1.11, p = 0.001). Subgroup analysis by study design revealed that -11377C>G (rs266729) dominant model (CG+GG vs CC, p = 0.0008) and G vs C allele (p = 0.0004) might be associated with type 2 diabetes risk in population-based case-control studies. After stratification by ethnicity, we found that -11377C>G (rs266729) dominant model (CG+GG vs CC, p = 0.004) and G vs C allele (p = 0.001) might be associated with type 2 diabetes risk in white individuals. In individuals with a family history of diabetes, the presence of -11391G>A (rs17300539) dominant model (GA+AA vs GG) and A vs G allele might be associated with increased risk of type 2 diabetes.

CONCLUSIONS/INTERPRETATION

The presence of +45T>G (rs2241766), +276G>T (rs1501299) and -11391G>A (rs17300539) do not appear to influence the development of type 2 diabetes. However, G vs C allele of -11377C>G (rs266729) might be a risk factor for type 2 diabetes.

Monodispersed NiO nanoflowers with anomalous magnetic behavior

Nickel oxide (NiO) nanoflowers, prepared by thermal decomposition, exhibit anomalous magnetic properties far below the blocking temperature, i.e., a cusp in both the zero-field-cooled and field-cooled curves at about 21 K. Detailed characterization discloses that the individual NiO nanoflower consists of porous crystals with holes (1.0-1.5 nm in size) inside. We believe that the low temperature magnetic feature observed here could be a new kind of spin transition for the uncompensated spins around the holes and will trigger more studies in other nanostructured antiferromagnetic materials.

[Study of oxidative stress in chronic hepatitis B patients with elevated serum total bilirubin]

OBJECTIVE

To investigate oxidative stress in chronic hepatitis B (CHB) patients with elevated serum total bilirubin (TBIL).

METHODS

75 CHB patients with elevated serum TBIL were enrolled in the present study. A, B, C, D and E group were defined. Serum Malondialdehyde (MDA), Xanthine Oxidase (XOD), Vitamin C (V(C)) and Vitamin E (V(E)) were determined. The control group contained 11 healthy donors and the carrier group contained 16 Hepatitis B surface antigen (HBsAg) carriers.

RESULTS

The concentrations of MDA and XOD were significantly higher in each group of patients than in the control (P < 0.05), while V(C) and V(E) were significantly lower (P < 0.05). The concentration of XOD was significantly higher in the carrier group than in the control (P < 0.05), while MDA, V(C) and V(E) were not significantly different (P > 0.05). The concentrations of MDA and XOD were significantly positively correlated with TBIL (r = 0.670, P < 0.01; r = 0.737, P < 0.01, respectively) in the patients, while V(C) and V(E) were significantly negatively correlated with TBIL (r = -0.463, P < 0.01; r = -0.247, P < 0.05, respectively). The concentration of MDA was significantly different among all the groups in the patients except the comparison between group A and group B. The concentration of XOD was significantly different between group A, B, C and group D, E (P < 0.05). The concentration of V(C) was significantly different between group A and group D, E and between group B, C, D and group E (P < 0.05). The concentration of V(E) was significantly different between group A, B and group E (P < 0.05).

CONCLUSION

There was a disturbance between oxidative stress and anti-oxidative ability in CHB patients with elevated serum TBIL. Oxidative stress became more serious along with the increasing of serum TBIL. In HBsAg carriers, oxidative stress level was low. The results suggest antioxidant treatment for CHB patients with elevated serum TBIL may help to improve the effect of therapy.

Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

Homology-free prediction of functional class of proteins and peptides by support vector machines

Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.

Trends in the exploration of therapeutic targets for the treatment of endocrine, metabolic and immune disorders

A number of therapeutic targets have been explored for developing drugs in the treatment of endocrine, metabolic and immune disorders. Continuous efforts and increasing interest have been directed at the search of new targets. Data from the therapeutic target database at http://bidd.nus.edu.sg/group/cjttd/ttd.asp, shows that there are 26, 24, and 22 targets of marketed drugs for the treatment of these three classes of diseases, respectively. The number of targets of investigational agents has reached 98, 124, and 72, respectively. An analysis of these targets, particularly those of recently approved drugs and patented investigational agents, provides useful hint about the general trends of target exploration, with current focus on drug discovery and the difficulties encountered in developing drugs against these targets. Multiple profiles of these targets have been analyzed to probe the sequence, structural, physicochemical and systems-related features contributing to the successful exploration of a target against these diseases.

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

MODEL—molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation

BACKGROUND AND PURPOSE

Traditional Chinese Medicine (TCM) is widely practised and is viewed as an attractive alternative to conventional medicine. Quantitative information about TCM prescriptions, constituent herbs and herbal ingredients is necessary for studying and exploring TCM.

EXPERIMENTAL APPROACH

We manually collected information on TCM in books and other printed sources in Medline. The Traditional Chinese Medicine Information Database TCM-ID, at http://tcm.cz3.nus.edu.sg/group/tcm-id/tcmid.asp, was introduced for providing comprehensive information about all aspects of TCM including prescriptions, constituent herbs, herbal ingredients, molecular structure and functional properties of active ingredients, therapeutic and side effects, clinical indication and application and related matters.

RESULTS

TCM-ID currently contains information for 1,588 prescriptions, 1,313 herbs, 5,669 herbal ingredients, and the 3D structure of 3,725 herbal ingredients. The value of the data in TCM-ID was illustrated by using some of the data for an in-silico study of molecular mechanism of the therapeutic effects of herbal ingredients and for developing a computer program to validate TCM multi-herb preparations.

CONCLUSIONS AND IMPLICATIONS

The development of systems biology has led to a new design principle for therapeutic intervention strategy, the concept of 'magic shrapnel' (rather than the 'magic bullet'), involving many drugs against multiple targets, administered in a single treatment. TCM offers an extensive source of examples of this concept in which several active ingredients in one prescription are aimed at numerous targets and work together to provide therapeutic benefit. The database and its mining applications described here represent early efforts toward exploring TCM for new theories in drug discovery.

[Phenotype of peripheral blood mononuclear cells derived dendritic cells from patients with chronic hepatitis B.]

BACKGROUND

The aim of this study was to access phenotype changes of dendritic cells (DC) cultured from peripheral blood mononuclear cells (PBMC) in patients with chronic hepatitis B and to reveal the relationship between phenotype of DC and ALT or HBV DNA.

METHODS

Indices of ALT and serum HBV DNA were measured in 37 patients with chronic hepatitis B and 21 healthy controls. Peripheral blood mononuclear cells were isolated from all patients and healthy controls, and cultured with granulocyte-macrophage colony-stumilating factor (GM-CSF), interleukin-4 (IL-4) and tumor necrosis factor- (TNF-)in RPMI 1640 medium that contained 10% fetal calf serum. After culturing for 7 days, the DC was counted and the phenotypes were detected by FACS. Then the data were statistically analysed.

RESULTS

The DC was significantly fewer (P less than 0.05) in patients with chronic hepatitis B than the controls. In particular, the expressive level of CD83 and CD86 on DC's surface from patients with chronic hepatitis B were also significantly lower (P less than 0.05) than that from the controls. In the patients with hepatitis B, the indices of DC had a significantly negative correlation with the level of serum HBV DNA (P less than 0.05), but no significant relationship was found between ALT and indices of DC (P greater than 0.05).

CONCLUSION

The DC cultured from patients with chronic hepatitis B were few and had immature phenotype. These changes had a significantly negative correlation with the level of serum HBV DNA, but had not correlation with the inflammatory reaction levels in the liver. DC was associated with the clearance of HBV in patients with hepatitis B.

Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties

Peptide binding to MHC is critical for antigen recognition by T-cells. To facilitate vaccine design, computational methods have been developed for predicting MHC-binding peptides, which achieve impressive prediction accuracies of 70-90% for binders and 40-80% for non-binders. These methods have been developed for peptides of fixed lengths, for a limited number of alleles, trained from small number of non-binders, and in some cases based straightforwardly on sequence. These limit prediction coverage and accuracy particularly for non-binders. It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders. This work explores support vector machines (SVM) as such a method for developing prediction systems of 18 MHC class I and 12 class II alleles by using 4208-3252 binders and 234,333-168,793 non-binders, and evaluated by an independent set of 545-476 binders and 110,564-84,430 non-binders. Binder accuracies are 86-99% for 25 and 70-80% for 5 alleles, non-binder accuracies are 96-99% for 30 alleles. Binder accuracies are comparable and non-binder accuracies substantially improved against other results. Our method correctly predicts 73.3% of the 15 newly-published epitopes in the last 4 months of 2005. Of the 251 recently-published HLA-A*0201 non-epitopes predicted as binders by other methods, 63 are predicted as binders by our method. Screening of HIV-1 genome shows that, compared to other methods, a comparable percentage (75-100%) of its known epitopes is correctly predicted, while a lower percentage (0.01-5% for 24 and 5-8% for 6 alleles) of its constituent peptides are predicted as binders. Our software can be accessed at .

Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods

Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity

Lipid binding proteins play important roles in signaling, regulation, membrane trafficking, immune response, lipid metabolism, and transport. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting lipid binding proteins irrespective of sequence similarity. This work explores the use of support vector machines (SVMs) as such a method. SVM prediction systems are developed using 14,776 lipid binding and 133,441 nonlipid binding proteins and are evaluated by an independent set of 6,768 lipid binding and 64,761 nonlipid binding proteins. The computed prediction accuracy is 78.9, 79.5, 82.2, 79.5, 84.4, 76.6, 90.6, 79.0, and 89.9% for lipid degradation, lipid metabolism, lipid synthesis, lipid transport, lipid binding, lipopolysaccharide biosynthesis, lipoprotein, lipoyl, and all lipid binding proteins, respectively. The accuracy for the nonmember proteins of each class is 99.9, 99.2, 99.6, 99.8, 99.9, 99.8, 98.5, 99.9, and 97.0%, respectively. Comparable accuracies are obtained when homologous proteins are considered as one, or by using a different SVM kernel function. Our method predicts 86.8% of the 76 lipid binding proteins nonhomologous to any protein in the Swiss-Prot database and 89.0% of the 73 known lipid binding domains as lipid binding. These findings suggest the usefulness of SVMs for facilitating the prediction of lipid binding proteins. Our software can be accessed at the SVMProt server (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi).

Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method

A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity (Nucleic Acids Res. 2003;31:3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins.

Prediction of functional class of the SARS coronavirus proteins by a statistical learning method

The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .

PEARLS: Program for Energetic Analysis of Receptor−Ligand System

Analysis of the energetics of small molecule ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions facilitates the quantitative understanding of molecular interactions that regulate the function and conformation of proteins. It has also been extensively used for ranking potential new ligands in virtual drug screening. We developed a Web-based software, PEARLS (Program for Energetic Analysis of Ligand-Receptor Systems), for computing interaction energies of ligand-protein, ligand-nucleic acid, protein-nucleic acid, and ligand-protein-nucleic acid complexes from their 3D structures. AMBER molecular force field, Morse potential, and empirical energy functions are used to compute the van der Waals, electrostatic, hydrogen bond, metal-ligand bonding, and water-mediated hydrogen bond energies between the binding molecules. The change in the solvation free energy of molecular binding is estimated by using an empirical solvation free energy model. Contribution from ligand conformational entropy change is also estimated by a simple model. The computed free energy for a number of PDB ligand-receptor complexes were studied and compared to experimental binding affinity. A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions. PEARLS can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/rune.pl.

Prediction of transporter family from protein sequence by support vector machine approach

Transporters play key roles in cellular transport and metabolic processes, and in facilitating drug delivery and excretion. These proteins are classified into families based on the transporter classification (TC) system. Determination of the TC family of transporters facilitates the study of their cellular and pharmacological functions. Methods for predicting TC family without sequence alignments or clustering are particularly useful for studying novel transporters whose function cannot be determined by sequence similarity. This work explores the use of a machine learning method, support vector machines (SVMs), for predicting the family of transporters from their sequence without the use of sequence similarity. A total of 10,636 transporters in 13 TC subclasses, 1914 transporters in eight TC families, and 168,341 nontransporter proteins are used to train and test the SVM prediction system. Testing results by using a separate set of 4351 transporters and 83,151 nontransporter proteins show that the overall accuracy for predicting members of these TC subclasses and families is 83.4% and 88.0%, respectively, and that of nonmembers is 99.3% and 96.6%, respectively. The accuracies for predicting members and nonmembers of individual TC subclasses are in the range of 70.7-96.1% and 97.6-99.9%, respectively, and those of individual TC families are in the range of 60.6-97.1% and 91.5-99.4%, respectively. A further test by using 26,139 transmembrane proteins outside each of the 13 TC subclasses shows that 90.4-99.6% of these are correctly predicted. Our study suggests that the SVM is potentially useful for facilitating functional study of transporters irrespective of sequence similarity.

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.

Trends in exploration of therapeutic targets

Lead discovery against a preselected therapeutic target is a key component in modern drug development. Continuous effort and increasing interest has been directed at the search for new targets, which has led to the identification of a growing number of them. Data from the therapeutic target database, at http://bidd.nus.edu.sg/group/cjttd/ttd.asp, show that, as of July 2004, the number of documented targets of marketed and investigational drugs has reached 1,174 distinct proteins (including subtypes) and 27 nucleic acids, 239 of which are targets of the marketed drugs. Analysis of these targets, particularly those of recently approved drugs and patented investigational agents, provide useful hints about general trends of target exploration and current focus in drug discovery for the treatment of high impact diseases needing effective or more treatment options.

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

A gelatin matrix-thrombin tissue sealant (FloSeal) application in the management of groin breakdown after inguinal lymphadenectomy for vulvar cancer

The rate of groin breakdown after radical wide vulvar excision and inguinal lymphadenectomy for vulvar cancer remains significant despite conservative surgical approaches. An 86-year-old Latin American woman underwent wide radical excision and bilateral inguinal lymphadenectomy for vulvar cancer. The postoperative course was complicated by bilateral groin wound separation and high output lymphorrhea. The patient responded to the application of a gelatin matrix-thrombin tissue sealant (FloSeal) to the bases of each groin with resolution in lymphorrhea and formation of granulation tissue. The application of a gelatin matrix-thrombin tissue sealant (FloSeal) may be a viable treatment in the management of groin breakdown in selected patients when conventional therapy produces suboptimal results.

MoViES: molecular vibrations evaluation server for analysis of fluctuational dynamics of proteins and nucleic acids

Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a self-consistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl.

Enzyme family classification by support vector machines

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.