Variational graph auto-encoders for miRNA-disease association prediction

Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies.

Ensemble disease gene prediction by clinical sample-based networks

BACKGROUND

Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different.

RESULTS

To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer's disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes.

CONCLUSIONS

In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.

Enhancing the prediction of disease–gene associations with multimodal deep learning

Motivation

Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease–gene associations.

Results

In this study, we propose a method to predict disease–gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease–gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease–gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease–gene pairs also demonstrates the ability of dgMDL in predicting new disease–gene associations.

Availability and implementation

Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Two new phenolic glycosides from the seeds of Citrullus lanatus

Two new phenolic glycosides, named lanatusosides C (1) and D (2), together with four known compounds (3-6), were isolated from the seeds of Citrullus lanatus. Among them, compounds 3 and 4 were isolated from Cucurbitaceae for the first time, and compound 5 was reported from this plant for the first time. Their structures were elucidated by means of extensive spectral analysis, including HR-ESI-MS, ¹H and ¹³C NMR techniques. The isolated new compounds were evaluated for cytotoxic activity against HepG2 cell line, of which compound 1 demonstrated weak cytotoxicity against the tested cell line.

Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data

Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (positive samples) is much less than that of unknown genes which are typically considered to be negative samples. In addition, most methods have not utilized clinical data from patients with a specific disease to predict disease genes. In this study, we propose a disease gene prediction algorithm (called dgSeq) by combining protein-protein interaction (PPI) network, clinical RNA-Seq data, and Online Mendelian Inheritance in Man (OMIN) data. Our dgSeq constructs differential networks based on rewiring information calculated from clinical RNA-Seq data. To select balanced sets of non-disease genes (negative samples), a disease-gene network is also constructed from OMIM data. After features are extracted from the PPI networks and differential networks, the logistic regression classifiers are trained. Our dgSeq obtains AUC values of 0.88, 0.83, and 0.80 for identifying breast cancer genes, thyroid cancer genes, and Alzheimer's disease genes, respectively, which indicates its superiority to other three competing methods. Both gene set enrichment analysis and predicted results demonstrate that dgSeq can effectively predict new disease genes.

[Efficacy and safety analysis of the combination of cladribine, cytarabine, granulocyte colonystimulating factor (CLAG) regime in patients with refractory or relapsed acute myeloid leukemia]

目的

分析CLAG方案（克拉屈滨＋阿糖胞苷+ G-CSF）对难治复发急性髓系白血病（AML）的疗效及安全性。

方法

回顾性分析2014年4月1日至2015年12月9日采用1个疗程CLAG方案治疗的33例难治复发AML患者的临床资料。

结果

33例难治复发AML患者中，男16例，女17例，中位年龄49(14~68）岁。按照WHO分类（第4版）诊断：伴有重现性遗传学异常AML 7例（21.2%），伴有多系病态造血AML 5例（15.2%），治疗相关AML 2例（6.1%），其他类型19例（57.5%）。NCCN危险分层低危、中危、高危组分别为6、18、9例，其中FLT3-ITD基因突变5例。复发16例，难治17例，既往化疗疗程中位数为2(1~36）个。经CLAG方案1个疗程化疗后，26例（78.8%）获得完全缓解（CR），难治组CR率低于复发组[64.7%（11/17）对93.8%（15/16），P=0.041]。5例FLT3-ITD基因突变患者均获CR。所有患者均出现4级白细胞减少和血小板减少和不同部位感染，3例因感染而早期死亡。5例患者CR后接受异基因造血干细胞移植。中位随访142(9~525) d，复发10例，死亡13例，中位无事件生存期为230(9~525）d，中位总生存期为419(9~525) d。获CR患者（26例）中位总生存期长于未缓解患者（7例）[447(165~525) d对52(9~162) d，P <0.001]。

结论

CLAG方案对难治复发AML疗效肯定，复发患者CR率高于难治患者。控制感染是治疗成功的关键。

Protective effects of seed melon extract on CCl4-induced hepatic fibrosis in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao, was also known as watermelon belongs to family Cucurbitaceae, variously used as healthy food and in the treatment of liver and lungs problems. Currently, Citrullus lanatus has become a major economic crop of medicinal and edible effects with regional characteristics.

AIM

This study was designed to evaluate the hepatoprotective and antioxidant activity of the seed melon (Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao) extract (SME) against carbon tetrachloride (CCl₄) induced hepatic fibrosis in mice.

MATERIALS AND METHODS

In this study, mice were randomly divided into 7 groups, including normal control, model, silymarin tablets as the positive control, SME 100, 200, 400, and 800mg/kg. After 8 weeks, activities of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides (TG), hyaluronic acid (HA) and laminin (LN) were checked. The levels of antioxidant enzymes such as superoxide dismutase (SOD), glutataion (GSH) and glutathione peroxidase (GSH-Px) were determined after SME administration. The hydroxyproline (HYP) levels, malondialdehyde (MDA) levels and histopathologic examinations of hepatocyte fibrosis were also determined. Additionally, effects of SME on alpha-smooth muscle actin (α-SMA) and transforming growth factor beta-1(TGF-β1) protein expressions were determined.

RESULTS

We found that SME could significantly lower the serum levels of hepatic enzyme markers AST, ALT, HA and LN (P<0.01). Compared with the CCl₄-only treatment group, levels of hepatic SOD and GSH-Px were significantly increased, and the MDA levels were remarkably decreased in mice treated by SME at medium dose (400mg/kg) and high dose (800mg/kg) (P<0.01). A histological examination of the liver showed that lesions, including necrosis, lymphocyte infiltration and fatty degeneration, were partially healed by treatment with SME. The results of protein expressions studies displayed that SME could inhibit α-SMA and TGF-β1 protein expression (P<0.01).

CONCLUSION

The present results suggested that protective effect of SME against CCl₄-induced hepatic fibrosis may rely on its effect on reducing oxidative stress and improving drug metabolizing enzyme activity in liver.

Relationship between TLR4 and MCP2 expression levels and habitual abortion

Habitual abortion is associated with the altered expression of multiple genes. This study was carried out to investigate the relationship between expression of Toll-like receptor 4 (TLR4) and monocyte chemotactic protein 2 (MCP2 or CCL8) and habitual abortion. This was done by detecting and comparing their relative expression in peripheral blood and placental villi of patients and healthy fertile women. Based on our previous research, 85 subjects with habitual abortion (study group) and 40 healthy fertile women (control group), who were admitted to our hospital between June 2013 and December 2014, were enrolled in this study. After these subjects signed written informed consent, peripheral blood samples and villous tissues were collected, from which the total RNA was extracted. The expression of TLR4 and MCP2 was detected with quantitative reverse transcription-polymerase chain reaction, using GAPDH as a reference control. The expression of TLR4 and MCP2 in the peripheral blood and villous tissues of the study group was significantly higher than that of the control group (P < 0.05). A positive correlation was also observed between the changes in expression levels of TLR4 and MCP2. In conclusion, TLR4 and MCP2 expression correlated with the occurrence of habitual abortion. Detecting expression changes in TLR4 and MCP2 in the peripheral blood is a feasible method for predicting the occurrence of abortion in women of child-bearing age.

Influence of adriamycin on changes in Nanog, Oct-4, Sox2, ARID1 and Wnt5b expression in liver cancer stem cells

AIM: To determine the influence of Adriamycin (ADM) on the changes in Nanog, Oct4, Sox2, as well as, in ARID1 and Wnt5b expression in liver cancer stem cells.

METHODS: The MHCC97-L and HCCLM3 liver cancer cell lines were selected as the cell models in this study, and were routinely cultured. The 50% lethal dose (LD50) in the cell lines was detected by the MTT assay. Expression changes in liver cancer stem cell related genes (Nanog, Oct-4, Sox2, ARID1, and Wnt5b) were detected by western blot following treatment with ADM (LD50).

RESULTS: The LD50 of ADM in MHCC97-L cells was lower than that in HCCLM3 cells (0.4123 ± 0.0236 μmol/L vs 0.5259 ± 0.0125 μmol/L, P < 0.05). Wnt5b and Nanog were expressed in both MHCC97-L and HCCLM3 cells, while only Sox2 was expressed in HCCLM3 cells. However, neither ARID1A nor Oct4 was detected in these two cell lines. Genes, related to the stem cells, showed different expression in liver cancer cells with different metastatic potential following treatment with ADM (LD50). Wnt5b protein increased gradually within 4 h of ADM (LD50) treatment, while Nanog decreased (P < 0.05). After 12 h, Wnt5b decreased gradually, while Nanog increased steadily (P < 0.05). In addition, only Sox2 was expressed in HCCLM3 cells with high metastatic potential following ADM (LD50) treatment. The expression of Sox2 increased gradually with ADM (LD50) in HCCLM3 cells (P < 0.05).

CONCLUSION: ADM increased the death rate of MHCC97-L and HCCLM3 cells, while the growth suppressive effect of ADM was higher in MHCC97-L cells than in HCCLM3 cells.

[Prospective study on comparing two methods: for dynamic monitoring of minimal residual disease in acute promyelocytic leukemia]

This study was purposed to comparatively analyse the value of PCR and FCM for dynamic monitoring minimal residual disease (MRD) of acute promyelocytic leukemia. The patients with acute promyelocytic leukemia hospitalized in our hospital from January 2011 to December 2012 were observed and all achieved complete remission after remission induction therapy. Before the chemotherapy, the bone marrow cell morphology examination, polymerase-chain reaction (PCR) and multi-parameter flow cytometry (FCM) were performed for each patient. Then the detection results were statistically analyzed. The 477 specimens were achieved from 159 detections for 48 patients. The results showed that 3 specimens were found to be relapsed by bone marrow cell examination, and other specimens were complete remission;PCR detection confirmed 7 positive, and the FCM confirmed 19 positive. There wasn't significant difference between PCR and FCM by kappa test (P > 0.05). It is concluded that FCM is as sensitive as PCR in evaluating the treatment effect of acute promyelocytic leukemia.

Complexity analysis and parameter estimation of dynamic metabolic systems

A metabolic system consists of a number of reactions transforming molecules of one kind into another to provide the energy that living cells need. Based on the biochemical reaction principles, dynamic metabolic systems can be modeled by a group of coupled differential equations which consists of parameters, states (concentration of molecules involved), and reaction rates. Reaction rates are typically either polynomials or rational functions in states and constant parameters. As a result, dynamic metabolic systems are a group of differential equations nonlinear and coupled in both parameters and states. Therefore, it is challenging to estimate parameters in complex dynamic metabolic systems. In this paper, we propose a method to analyze the complexity of dynamic metabolic systems for parameter estimation. As a result, the estimation of parameters in dynamic metabolic systems is reduced to the estimation of parameters in a group of decoupled rational functions plus polynomials (which we call improper rational functions) or in polynomials. Furthermore, by taking its special structure of improper rational functions, we develop an efficient algorithm to estimate parameters in improper rational functions. The proposed method is applied to the estimation of parameters in a dynamic metabolic system. The simulation results show the superior performance of the proposed method.

Stability and bifurcation analysis of models for zebrafish somitogenesis

Notch-Delta signaling is indispensable for somitogenesis, which controls the vertebrate segmentation during embryonic development. Several theoretical models have been proposed to explain this interesting process. In zebrafish somitogenesis, genes her1, her7, delta, and their proteins plays the important roles. However, an auto-repression model with time delay involved only by her1/her7 is able to explain zebrafish somitogenesis. This paper will systematically study the dynamics of this model. Specifically we investigate its stability, bifurcation (oscillation), and stability of oscillation. First, the conditions for both stability and bifurcation are presented based on the linearized model. Then three indices for bifurcation of this nonlinear model are derived by using linear functional operator analysis. Finally, the numerical simulations are carried out to illustrate the theoretical results developed in this study.

Nonlinear model-based method for clustering periodically expressed genes

Clustering periodically expressed genes from their time-course expression data could help understand the molecular mechanism of those biological processes. In this paper, we propose a nonlinear model-based clustering method for periodically expressed gene profiles. As periodically expressed genes are associated with periodic biological processes, the proposed method naturally assumes that a periodically expressed gene dataset is generated by a number of periodical processes. Each periodical process is modelled by a linear combination of trigonometric sine and cosine functions in time plus a Gaussian noise term. A two stage method is proposed to estimate the model parameter, and a relocation-iteration algorithm is employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. One synthetic dataset and two biological datasets were employed to evaluate the performance of the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g., k-means) for periodically expressed gene data, and thus it is an effective cluster analysis method for periodically expressed gene data.

Parameter estimation method for improper fractional models and its application to molecular biological systems

Derived from biochemical principles, molecular biological systems can be described by a group of differential equations. Generally these differential equations contain fractional functions plus polynomials (which we call improper fractional model) as reaction rates. As a result, molecular biological systems are nonlinear in both parameters and states. It is well known that it is challenging to estimate parameters nonlinear in a model. However, in fractional functions both the denominator and numerator are linear in the parameters while polynomials are also linear in parameters. Based on this observation, we develop an iterative linear least squares method for estimating parameters in biological systems modeled by improper fractional functions. The basic idea is to transfer optimizing a nonlinear least squares objective function into iteratively solving a sequence of linear least squares problems. The developed method is applied to the estimation of parameters in a metabolism system. The simulation results show the superior performance of the proposed method for estimating parameters in such molecular biological systems.

Estimating parameters in genetic regulatory networks with SUM logic

Many methods for inferring genetic regulatory networks have been proposed. However inferred networks can hardly be used to analyze the dynamics of genetic regulatory networks. Recently nonlinear differential equations are proposed to model genetic regulatory networks. Based on this kind of model, the stability of genetic regulatory networks has been intensively investigated. Because of difficulty in estimating parameters in nonlinear model, inference of genetic regulatory networks with nonlinear model has been paid little attention. In this paper, we present a method for estimating parameters in genetic regulatory networks with SUM regulatory logic. In this kind of genetic regulatory networks, a regulatory function for each gene is a linear combination of Hill form functions, which are nonlinear in parameters and in system states. To investigate the proposed method, the gene toggle switch network is used as an illustrative example. The simulation results show that the proposed method can accurately estimates parameters in genetic regulatory networks with SUM logic.

[Clinical analysis of 14 patients with lymphoma associated hemophagocytic syndrome]

The study was aimed to investigate the clinical characteristics, diagnosis and therapy of patients with lymphoma associated hemophagocytic syndrome (LAHS) so as to provide the clinical basis for improving its recognition and giving effective therapy. The clinical data of 14 patients with LAHS in Beijing Friendship Hospital, Capital Medical University during the period from June 2005 to May 2008 were collected, the informations including primary diseases, clinical manifestations, laboratory findings, therapy and outcome were analyzed retrospectively, the coincidence of each diagnostic index was compared before and after diagnosis. All 14 patients were given therapeutic regimens containing fludarabine, methylprednisolone and gammaglobulin (FDIg) after final diagnosis. The results indicated that 100% patients had abnormal changes on NK cell activity and sCD25 level in serum, but hemophagocytosis in less than 40% patients at early stage was found in bone marrow. Even after confirmed diagnosis of the disease, the percentage of patients with hemophagocytosis was not up to 50%. 9 out of the 14 patients had a good prognosis after treatment, and the other 5 patients died. It is concluded that the detection of NK cell activity and sCD25 level in serum may be valuable for the early diagnosis of LAHS, the hemophagocytosis is not necessary for the diagnosis of LAHS. Fludarabine combined with methylprednisolone and gamma globulin may provide a new strategy for LAHS therapy.

[Significance of hemophagocytosis in diagnosis of hemophagocytic lymphohistiocytosis]

The study was aimed to explore the diagnostic significance of hemophagocytosis in the patients with hemophagocytic lymphohistiocytosis (HLH). 61 suspected HLH patients from June 2005 to October 2008 were enrolled in the study. The suspected HLH patients were divided into confirmed group (43 out of 61) and excluded group (18 out of 61) according to HLH-2004 diagnostic criteria. The incidences of hemophagocytosis in bone marrow, spleen or lymph nodes were compared in all groups. The results indicated that the hemophagocytosis in bone marrow, spleen or lymph nodes was found in 33 patients of confirmed group, while the hemophagocytosis was found in 4 patients of excluded group. The sensitivity of hemophagocytosis for the diagnosis of HLH was 76.7%, and its specificity was 77.8%. In conclusion, hemophagocytosis is a helpful marker for the diagnosis of most but not all HLH patients, however, the lack of hemophagocytosis does not mean to exclude the diagnosis of HLH.

[The early diagnosis and clinical analysis of 57 cases of acquired hemophagocytic lymphohistiocytosis]

OBJECTIVE

To explore the significance of NK cell activity, interleukin-2 receptors (sCD(25)) and glycosylated ferritin in the early diagnostic of acquired hemophagocytic lymphohistiocytosis (HLH).

METHODS

57 patients suspected of HLH from June 2005 to May 2008 and 25 healthy subjects were enrolled in the study. The patients suspected of HLH were divided into three groups i.e. (1) a group with diagnosis confirmed at first visit; (2) a group with diagnosis confirmed at subsequent visit and (3) a group with diagnosis unconfirmed according to HLH-2004 diagnostic criteria. Healthy subjects were enrolled as control. NK cell activity was determined with a released LDH assay. The percentage of glycosylated ferritin was determined with phytohemagglutinin adsorption assay. sCD(25) was examined with ELISA double antibody sandwich assay. We compared the coincidence of each diagnostic index before and after diagnosis.

RESULTS

The median percentage of NK cell activity was significantly lower in the first group (18.3 +/- 5.6)% and the second (16.7 +/- 6.7)% than that in the third group (33.4 +/- 6.8)% or in the controls (36.6 +/- 5.0)%. The median percentage of glycosylated ferritin was also significantly lower in the first group (15.4 +/- 2.0)% and the second group (16.9 +/- 3.4)% than that in the third group (40.4 +/- 3.0)% or in the controls (45.2 +/- 2.2)%. Meanwhile, the median level of sCD(25) was significantly higher in the first group (12 916 +/- 4328) ng/L and the second group (12 117 +/- 5465) ng/L than that in the third group (4728 +/- 1482) ng/L or in the controls (3841 +/- 993) ng/L. Furthermore, NK cell activity, sCD(25) and glycosylated ferritin were abnormal in all the patients in the early stage of HLH.

CONCLUSION

NK cell activity, sCD(25) and glycosylated ferritin may be helpful markers for the early diagnosis of HLH.

[Diagnostic significance of NK cell activity in the patients with hemophagocytic syndrome]

This study was aimed to explore the level of NK cell activity in the patients with secondary hemophagocytic syndrome (HPS) and its significance for early diagnosis of this disease. 16 suspected HPS patients and 25 healthy subjects were enrolled in this study. The activity of NK cells in peripheral blood was detected by a released LDH assay. The activity level of NK cells in peripheral blood from patients who were finally diagnosed as HPS was compared with healthy subjects. The results showed that 8 out of 16 suspected HPS patients were finally diagnosed as secondary HPS. The activity of NK cells in peripheral blood of these 8 patients was obviously lower than that in healthy subjects with statistical significance (p<0.001), and showed abnormal in the early stage of this disease. It is concluded that the detection of NK cell activity may play an important role in earlier diagnosis of secondary hemophagocytic syndrome.

[Diagnostic significance of glycosylated ferritin for patients with secondary hemophagocytic lymphohistiocytosis]

This study was aimed to explore the level of glycosylated ferritin in the patients with secondary hemophagocytic lymphohistiocytosis (HLH) and its diagnostic significance. 29 suspected HLH patients from October 2007 to October 2008 were enrolled in the study, and 25 healthy subjects were selected as control. The 29 suspected HLH patients were divided into confirmed group (22 out of 29) and unconfirmed group (7 out of 29) according to HLH-2004 diagnostic criteria. The percentage of glycosylated ferritin in peripheral blood was determined by phytohemagglutinin adsorption assay. The results showed that the median level of total serum ferritin in patients of confirmed group (2897.6+/-1837.2 microg/L) was significantly higher than that in patients of unconfirmed group (653.1+/-249.1 microg/L) (p<0.01), and was also higher than that in controls (414.6+/-212.6 microg/L) (p<0.01). The median percentage of glycosylated ferritin in patients of confirmed group was significantly lower (17.0+/-4.2%) than that in patients of unconfirmed group (40.7+/-4.5%) (p<0.01) and was lower than that in controls (53.6+/-13.3%) (p<0.01). The sensitivity (86.4% vs 77.3%) and specificity (71.4% vs 42.9%) of glycosylated ferritin for the diagnosis of HLH were higher than that of total serum ferritin. In conclusions, glycosylated ferritin may be a helpful marker for the diagnosis of HLH.

[Diagnostic significance of NK cell activity and soluble CD25 level in serum from patients with secondary hemophagocytic lymphohistiocytosis]

This study was aimed to explore the clinical significance of NK cell activity and serum soluble CD25 (sCD25) level in early diagnosis of the patients with secondary hemophagocytic lymphohistiocytosis (HLH). 38 suspected secondary HLH patients from June 2005 to June 2008 and 25 healthy subjects were enrolled in the study. The NK cell activity in peripheral blood was determined by a released LDH assay, The sCD25 level in serum was detected with ELISA double antibody sandwich assay. The 38 suspected secondary HLH patients were divided into diagnosed and excluded group according to HLH-2004 diagnostic criteria, The NK cell activity and sCD25 level were compared between the two groups. The results showed that 22 out of 38 suspected patients were diagnosed as secondary HLH, the NK cell activity in peripheral blood of these 22 patients was significantly lower than that of healthy control (p < 0.001), the sCD25 level in peripheral blood of these 22 diagnosed patients was higher than that of healthy control (p < 0.05). In conclusion, detection of NK cell activity and sCD25 level may be valuable in the early diagnosis of secondary HLH.

Fusion protein of interleukin 4 and diphtherial toxin with high cytotoxicity to cancer cells

Receptor of human interleukin 4 (hIL4R) has been found to be present on many types of cancer, so it may be a good target for cancer therapy. Here, fusion toxin gene DT4H has been constructed by fusing DNA sequence encoding the first 389 amino acids of diphtherial toxin (DT), which can not bind its own receptor, to human interleukin 4 (hIL4) gene. In order to improve the affinity of fusion toxin for hIL4R, a circularly permuted form of hIL4 (cpIL4) was used. The fusion gene was expressed in Escherichia coli where the fusion toxin DT4H was highly expressed. Purified DT4H was very cytotoxic to cancer cell line U251 cells, and moderate cytotoxic to HepG2 and MCF-7 cells. SGC-7901 cells were insensitive to it. The cytotoxic action of DT4H was specific because it was blocked by excess hIL4. These results suggest that DT4H may be a useful agent in the treatment of certain malignancies.

Expression of C-peptide multiple gene copies in Escherichia coli and stabilities of C-peptide in aqueous solution

A gene fragment encoding three copies of proinsulin C-peptide was synthesized and expressed in E. coli and the recombinant proinsulin C-peptide was produced through site-specific cleavage of the resulting gene products. The fusion protein was expressed at high level, about 80 mg/L, as a soluble product in the cytoplasm. Ni-NTA affinity chromatography efficiently separated the expressed fusion protein from the supernatant, to obtain about 37.5 mg/L of the fusion protein with 70% purity. Enzymatic digestion by trypsin and carboxypeptidase B of the fusion protein efficiently released native C-peptide, the overall yield of recombinant C-peptide at a purity over 95% was 1.5 mg/L. The good agreement of amino acids composition, together with shown similarities of the recombinant C-peptide to C-peptide standard in the comparative RP-HPLC analysis and IMMULITE C-Peptide quantitative assay, suggested that the recombinant C-peptide obtained in this report was the native human C-peptide. The investigation of the chemical stability of recombinant human C-peptide in aqueous solutions by RP-HPLC was also reported. The degradation of the recombinant C-peptide showed a marked dependence on pH and temperature. The degradation reaction of C-peptide occurred immediately in pH 3 or pH 9 buffered solution. The degradation reaction of C-peptide followed first-order kinetics in pH 3 buffered solution at 37 degrees C or 70 degrees C, only 40.3% of C-peptide was remained after 10 h at 70 degrees C. The maximum stability was achieved at pH 7.4, more than 90% of C-peptide were detected at pH 7.4 and 37 degrees C after 10 h and at pH 7.4 and 70 degrees C after 5 h. 99% and 96% of C-peptide was remained at pH 7.4 and 37 degrees C after 10 h with and without 10 g/L BSA respectively.

Red cell age and susceptibility to malaria during pregnancy

BACKGROUND

Increased susceptibility to malaria in pregnancy is well recognized, and has generally been assumed to be due to hormonal changes resulting in altered immunity. Based on previous work demonstrating enhanced parasite growth in young normal and thalassemic red blood cells, we hypothesized that in pregnancy increased malaria susceptibility may be due, in part, to the increase in the population of young red cells.

METHODS

FC27 strain of Plasmodium falciparum was cultured in the red cells and sera from healthy primigravida pregnant (n=9) and non-pregnant (n=9) women. Red cells from both pregnant and non-pregnant women were each placed in three cultures containing the sera from pregnant, non-pregnant and pooled control samples. Cultures were set up in triplicate and incubated for 144 hours. Parasite development and growth were assessed by slide microscopy.

RESULTS

At 96 hours the median parasite growth in cells from pregnant samples (5.7%) was significantly better than that in the non-pregnant cells (4.8%) (p=0.01). There was no significant difference in parasite growth in cultures with pregnant and non-pregnant sera. As expected, there were significant differences in parameters measuring red cell age between the cells from pregnant and nonpregnant samples: median red cell creatine (11.09 mg/dl) versus (6.90 mg/dl) (p=0.004) and median reticulocyte count (2.3%) versus (1.4%) (p=0.0006).

CONCLUSIONS

These preliminary results are consistent with the hypothesis that an increased population of young red cells may contribute to increased malaria susceptibility during pregnancy.

Invasion and growth of Plasmodium falciparum is inhibited in fractionated thalassaemic erythrocytes

Epidemiological and clinical studies have indicated that the thalassaemias may confer protection against malaria. The study reported here investigated this protective effect in vitro, using a new approach which controls for the potential effect of red cell size and age on the virulence of the parasite. A Percoll density gradient method was used to separate alpha- and beta-thalassaemic trait, haemoglobin H and normal red blood cells (RBC) into fractions of different density. Correlations between RBC density, age and size in fractions of all genotypes were established using red cell creatine as an index of cell age. The development of Plasmodium falciparum over 3 erythrocytic cycles (144 h) in whole blood as well as fractionated samples was monitored by slide microscopy and flow cytometry. A significantly reduced rate of parasite invasion and growth was demonstrated in RBC from all thalassaemic genotypes tested. Poor reinvasion rates were noted in the second and third cycles. Increased duration of culture and red cell age also had a greater negative impact on parasite growth in thalassaemic RBC. This poor growth rate was also associated with the arrest of parasite growth at the schizont stage (schizont maturation arrest) and the accumulation of abnormal, trophozoite/schizont stage parasites in the older thalassaemic RBC fractions. These findings suggest a defect in the number and viability of merozoites generated by parasites growing in thalassaemic RBC. Age related factors such as oxidant stress may play a key role in mediating this kind of protective mechanism and deserve further investigation.