Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank.

AIMS/HYPOTHESIS: The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers. METHODS: From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts. RESULTS: At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts. CONCLUSIONS/INTERPRETATION: Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.

Tumor-secreted exosomal miR-141 activates tumor-stroma interactions and controls premetastatic niche formation in ovarian cancer metastasis.

BACKGROUND: Metastatic colonization is one of the critical steps in tumor metastasis. A pre-metastatic niche is required for metastatic colonization and is determined by tumor-stroma interactions, yet the mechanistic underpinnings remain incompletely understood. METHODS: PCR-based miRNome profiling, qPCR, immunofluorescent analyses evaluated the expression of exosomal miR-141 and cell-to-cell communication. LC-MS/MS proteomic profiling and Dual-Luciferase analyses identified YAP1 as the direct target of miR-141. Human cytokine profiling, ChIP, luciferase reporter assays, and subcellular fractionation analyses confirmed YAP1 in modulating GROα production. A series of in vitro tumorigenic assays, an ex vivo model and Yap1 stromal conditional knockout (cKO) mouse model demonstrated the roles of miR-141/YAP1/GROα/CXCR1/2 signaling cascade. RNAi, CRISPR/Cas9 and CRISPRi systems were used for gene silencing. Blood sera, OvCa tumor tissue samples, and tissue array were included for clinical correlations. RESULTS: Hsa-miR-141-3p (miR-141), an exosomal miRNA, is highly secreted by ovarian cancer cells and reprograms stromal fibroblasts into proinflammatory cancer-associated fibroblasts (CAFs), facilitating metastatic colonization. A mechanistic study showed that miR-141 targeted YAP1, a critical effector of the Hippo pathway, reducing the nuclear YAP1/TAZ ratio and enhancing GROα production from stromal fibroblasts. Stromal-specific knockout (cKO) of Yap1 in murine models shaped the GROα-enriched microenvironment, facilitating in vivo tumor colonization, but this effect was reversed after Cxcr1/2 depletion in OvCa cells. The YAP1/GROα correlation was demonstrated in clinical samples, highlighting the clinical relevance of this research and providing a potential therapeutic intervention for impeding premetastatic niche formation and metastatic progression of ovarian cancers. CONCLUSIONS: This study uncovers miR-141 as an OvCa-derived exosomal microRNA mediating the tumor-stroma interactions and the formation of tumor-promoting stromal niche through activating YAP1/GROα/CXCRs signaling cascade, providing new insight into therapy for OvCa patients with peritoneal metastases.

High-density lipoprotein subclasses and cardiovascular disease and mortality in type 2 diabetes: analysis from the Hong Kong Diabetes Biobank.

OBJECTIVE: High-density lipoproteins (HDL) comprise particles of different size, density and composition and their vasoprotective functions may differ. Diabetes modifies the composition and function of HDL. We assessed associations of HDL size-based subclasses with incident cardiovascular disease (CVD) and mortality and their prognostic utility. RESEARCH DESIGN AND METHODS: HDL subclasses by nuclear magnetic resonance spectroscopy were determined in sera from 1991 fasted adults with type 2 diabetes (T2D) consecutively recruited from March 2014 to February 2015 in Hong Kong. HDL was divided into small, medium, large and very large subclasses. Associations (per SD increment) with outcomes were evaluated using multivariate Cox proportional hazards models. C-statistic, integrated discrimination index (IDI), and categorial and continuous net reclassification improvement (NRI) were used to assess predictive value. RESULTS: Over median (IQR) 5.2 (5.0-5.4) years, 125 participants developed incident CVD and 90 participants died. Small HDL particles (HDL-P) were inversely associated with incident CVD [hazard ratio (HR) 0.65 (95% CI 0.52, 0.81)] and all-cause mortality [0.47 (0.38, 0.59)] (false discovery rate < 0.05). Very large HDL-P were positively associated with all-cause mortality [1.75 (1.19, 2.58)]. Small HDL-P improved prediction of mortality [C-statistic 0.034 (0.013, 0.055), IDI 0.052 (0.014, 0.103), categorical NRI 0.156 (0.006, 0.252), and continuous NRI 0.571 (0.246, 0.851)] and CVD [IDI 0.017 (0.003, 0.038) and continuous NRI 0.282 (0.088, 0.486)] over the RECODe model. CONCLUSION: Small HDL-P were inversely associated with incident CVD and all-cause mortality and improved risk stratification for adverse outcomes in people with T2D. HDL-P may be used as markers for residual risk in people with T2D.

Genome-Wide Transcriptome Profiling of the Neoplastic Giant Cell Tumor of Bone Stromal Cells by RNA Sequencing.

Giant cell tumor of bone (GCTB) is the most common non-malignant primary bone tumor reported in Hong Kong. Failure of treatment in advanced GCTB with aggressive local recurrence remains a clinical challenge. In order to reveal the molecular mechanism underlying the pathogenesis of this tumor, we aimed to examine the transcriptome profiling of the neoplastic stromal cells of GCTB in this study. RNA-sequencing was performed on three GCTB stromal cell samples and one bone marrow-derived MSC sample and 174 differentially expressed genes (DEGs) were identified between these two cell types. The top five up-regulated genes are SPP1, F3, TSPAN12, MMP13, and LGALS3BP and further validated by qPCR and Western Blotting. Knockdown of SPP1 was found to induce RUNX2 and OPG expression in GCTB stromal cells but not the MSCs. Ingenuity pathway analysis (IPA) of the 174 DEGs revealed significant alternations in 23 pathways; variant calling analysis revealed 1915 somatic variants of 384 genes with high or moderate impacts. Interestingly, four canonical pathways were found overlapping in both analyses; from which VEGFA, CSF1, PLAUR, and F3 genes with somatic mutation were found up-regulated in GCTB stromal cells. The STRING diagram showed two main clusters of the DEGs; one cluster of histone genes that are down-regulated in GCTB samples and another related to osteoblast differentiation, angiogenesis, cell cycle progression, and tumor growth. The DEGs and somatic mutations found in our study warrant further investigation and validation, nevertheless, our study add new insights in the search for new therapeutic targets in treating GCTB. J. Cell. Biochem. 118: 1349-1360, 2017. © 2016 Wiley Periodicals, Inc.

Profiling of the silica-induced molecular events in lung epithelial cells using the RNA-Seq approach.

Silicosis is a prolonged, irreversible and incurable occupational disease, and there is a significant number of newly diagnosed cases every year in Hong Kong. Due to the long latency of the disease, the diagnosis can be missed until detailed clinical examination at a later stage. For a better control of this deadly disease, detailing the pro-inflammatory and fibrotic events in the macrophage would be instrumental in understanding the pathogenesis of the disease and essential for the significant biomarkers discovery. In this in vitro study, human cell line model A549 lung epithelial cells were used. The immediate molecular events underneath the activation of quartz silica polymorphs were followed in a time course of 0, 0.5, 2, 8, 16 and 24 h. The transcriptome library was prepared and subjected to RNA-Seq analysis. Data analysis was performed by pathway analysis tools and verified by real-time PCR. The results showed that triggered genes were mainly found in the immune response and inflammatory pathways. An interesting finding was the association of the DNA-binding protein inhibitor (ID) family in the silica exposure to lung cells. The linkage of ID1, ID2 and ID3 to cancer may rationalize themselves to be the markers indicating an early response of silicosis. However, further studies are required to consolidate the roles of these genes in silicosis. Copyright © 2017 John Wiley & Sons, Ltd.

Systematic Selection of Reference Genes for the Normalization of Circulating RNA Transcripts in Pregnant Women Based on RNA-Seq Data.

RNA transcripts circulating in peripheral blood represent an important source of non-invasive biomarkers. To accurately quantify the levels of circulating transcripts, one needs to normalize the data with internal control reference genes, which are detected at relatively constant levels across blood samples. A few reference gene candidates have to be selected from transcriptome data before the validation of their stable expression by reverse-transcription quantitative polymerase chain reaction. However, there is a lack of transcriptome, let alone whole-transcriptome, data from maternal blood. To overcome this shortfall, we performed RNA-sequencing on blood samples from women presenting with preterm labor. The coefficient of variation (CV) of expression levels was calculated. Of 11,215 exons detected in the maternal blood whole-transcriptome, a panel of 395 genes, including PPP1R15B, EXOC8, ACTB, and TPT1, were identified to comprise exons with considerably less variable expression level (CV, 7.75-17.7%) than any GAPDH exon (minimum CV, 27.3%). Upon validation, the selected genes from this panel remained more stably expressed than GAPDH in maternal blood. This panel is over-represented with genes involved with the actin cytoskeleton, macromolecular complex, and integrin signaling. This groundwork provides a starting point for systematically selecting reference gene candidates for normalizing the levels of circulating RNA transcripts in maternal blood.

Genetic and clinical variables identify predictors for chronic kidney disease in type 2 diabetes.

Type 2 diabetes and chronic kidney disease (CKD) may share common risk factors. Here we used a 3-stage procedure to discover novel predictors of CKD by repeatedly applying a stepwise selection based on the Akaike information criterion to subsamples of a prospective complete-case cohort of 2755 patients. This cohort encompassed 25 clinical variables and 36 genetic variants associated with type 2 diabetes, obesity, or fasting plasma glucose. We compared the performance of the clinical, genetic, and clinico-genomic models and used net reclassification improvement to evaluate the impact of top selected genetic variants to the clinico-genomic model. Associations of selected genetic variants with CKD were validated in 2 independent cohorts followed by meta-analyses. Among the top 6 single-nucleotide polymorphisms selected from clinico-genomic data, three (rs478333 of G6PC2, rs7754840 and rs7756992 of CDKAL1) contributed toward the improvement of prediction performance. The variant rs478333 was associated with rapid decline (over 4% per year) in estimated glomerular filtration rate. In a meta-analysis of 2 replication cohorts, the variants rs478333 and rs7754840 showed significant associations with CKD after adjustment for conventional risk factors. Thus, this novel 3-stage approach to a clinico-genomic data set identified 3 novel genetic predictors of CKD in type 2 diabetes. This method can be applied to similar data sets containing clinical and genetic variables to select predictors for clinical outcomes.

The Utilization of the Immune System in Lung Cancer Treatment: Beyond Chemotherapy.

Lung cancer is ranked first worldwide as one of the main cancers in terms of prevalence and mortality rate. The development of effective treatment strategies against lung cancer is therefore of paramount importance. Traditionally, chemotherapy was employed in the treatment of various cancers. However, the non-specific nature of the actions of chemotherapeutic drugs and the potential for tumors to develop resistance to these drugs may render chemotherapy a less favorable option for cancer treatment. Immunotherapy provides an alternative strategy for this purpose. It involves the utilization of the immune system and the immune effector cells to elicit an immune response to the tumors, thereby eliminating them. Strategies include the administration of pro-inflammatory cytokines for immune stimulation, the removal of immunological checkpoints using monoclonal antibodies, and the use of cancer vaccines to enhance immunity against tumors. This article summarizes the above strategies, highlights the reasons why immunotherapy is superior to chemotherapy for the purpose of tumor removal, and reviews the recent clinical studies comparing the clinical outcomes of patients undergoing immunotherapy and chemotherapy. The article also describes advances in immunotherapeutic strategies for the treatment of lung cancer.

Environmental Factors Associated with Altered Gut Microbiota in Children with Eczema: A Systematic Review.

Eczema is a common skin condition that impairs children's daily life activities and quality of life. Previous research shows that gut microbiome composition plays an important role in the development of eczema. The present review summarizes evidence on environmental factors related to altered gut microbiota in children with eczema. We searched Medline, PubMed, Embase, and the Cochrane database of Systematic Reviews through October 2015. The search strategy focused on articles published in peer-reviewed, English-language journals with no publication year limit. Only original studies and review articles that reported environmental factors on gut microbiome specific to eczema were included in this review. We selected six studies (total 1990 participants) for full review and identified that the composition of gut microbiota specific to eczema could be influenced by the following environmental factors: length of gestation, mode of delivery, type of feeding, method of treatment, number of older siblings, and other lifestyle factors. There has been inconsistent empirical evidence as to the modulatory effects of gut microbiota on immunological functions in children with eczema. Further research on the environmental-host-microbial interaction is needed to develop a strong base of knowledge for the development and implementation of prevention strategies and policies for eczema.

Evidence for Elizabethkingia anophelis transmission from mother to infant, Hong Kong.

Elizabethkingia anophelis, recently discovered from mosquito gut, is an emerging bacterium associated with neonatal meningitis and nosocomial outbreaks. However, its transmission route remains unknown. We use rapid genome sequencing to investigate 3 cases of E. anophelis sepsis involving 2 neonates who had meningitis and 1 neonate's mother who had chorioamnionitis. Comparative genomics revealed evidence for perinatal vertical transmission from a mother to her neonate; the 2 isolates from these patients, HKU37 and HKU38, shared essentially identical genome sequences. In contrast, the strain from another neonate (HKU36) was genetically divergent, showing only 78.6% genome sequence identity to HKU37 and HKU38, thus excluding a clonal outbreak. Comparison to genomes from mosquito strains revealed potential metabolic adaptations in E. anophelis under different environments. Maternal infection, not mosquitoes, is most likely the source of neonatal E. anophelis infections. Our findings highlight the power of genome sequencing in gaining rapid insights on transmission and pathogenesis of emerging pathogens.

A mouse model of luciferase-transfected stromal cells of giant cell tumor of bone.

A major barrier towards the study of the effects of drugs on Giant Cell Tumor of Bone (GCT) has been the lack of an animal model. In this study, we created an animal model in which GCT stromal cells survived and functioned as proliferating neoplastic cells. A proliferative cell line of GCT stromal cells was used to create a stable and luciferase-transduced cell line, Luc-G33. The cell line was characterized and was found that there were no significant differences on cell proliferation rate and recruitment of monocytes when compared with the wild type GCT stromal cells. We delivered the Luc-G33 cells either subcutaneously on the back or to the tibiae of the nude mice. The presence of viable Luc-G33 cells was assessed using real-time live imaging by the IVIS 200 bioluminescent imaging (BLI) system. The tumor cells initially propagated and remained viable on site for 7 weeks in the subcutaneous tumor model. We also tested in vivo antitumor effects of Zoledronate (ZOL) and Geranylgeranyl transferase-I inhibitor (GGTI-298) alone or their combinations in Luc-G33-transplanted nude mice. ZOL alone at 400 µg/kg and the co-treatment of ZOL at 400 µg/kg and GGTI-298 at 1.16 mg/kg reduced tumor cell viability in the model. Furthermore, the anti-tumor effects by ZOL, GGTI-298 and the co-treatment in subcutaneous tumor model were also confirmed by immunohistochemical (IHC) staining. In conclusion, we established a nude mice model of GCT stromal cells which allows non-invasive, real-time assessments of tumor development and testing the in vivo effects of different adjuvants for treating GCT.

A novel missense mutation in CCDC88C activates the JNK pathway and causes a dominant form of spinocerebellar ataxia.

BACKGROUND: Spinocerebellar ataxias (SCAs) are a group of clinically and genetically diverse and autosomal-dominant disorders characterised by neurological deficits in the cerebellum. At present, there is no cure for SCAs. Of the different distinct subtypes of autosomal-dominant SCAs identified to date, causative genes for only a fraction of them are currently known. In this study, we investigated the cause of an autosomal-dominant SCA phenotype in a family that exhibits cerebellar ataxia and pontocerebellar atrophy along with a global reduction in brain volume. METHODS AND RESULTS: Whole-exome analysis revealed a missense mutation c.G1391A (p.R464H) in the coding region of the coiled-coil domain containing 88C (CCDC88C) gene in all affected individuals. Functional studies showed that the mutant form of CCDC88C activates the c-Jun N-terminal kinase (JNK) pathway, induces caspase 3 cleavage and triggers apoptosis. CONCLUSIONS: This study expands our understanding of the cause of autosomal-dominant SCAs, a group of heterogeneous congenital neurological conditions in humans, and unveils a link between the JNK stress pathway and cerebellar atrophy.

The SARS-coronavirus membrane protein induces apoptosis via interfering with PDK1-PKB/Akt signalling.

A number of viral gene products are capable of inducing apoptosis by interfering with various cellular signalling cascades. We previously reported the pro-apoptotic property of the SARS-CoV (severe acute respiratory syndrome coronavirus) M (membrane)-protein and a down-regulation of the phosphorylation level of the cell-survival protein PKB (protein kinase B)/Akt in cells expressing M-protein. We also showed that overexpression of PDK1 (3-phosphoinositide-dependent protein kinase 1), the immediate upstream kinase of PKB/Akt, suppressed M-induced apoptosis. This illustrates that M-protein perturbs the PDK1 and PKB/Akt cell survival signalling pathway. In the present study, we demonstrated that the C-terminus of M-protein interacts with the PH (pleckstrin homology) domain of PDK1. This interaction disrupted the association between PDK1 and PKB/Akt, and led to down-regulation of PKB/Akt activity. This subsequently reduced the level of the phosphorylated forkhead transcription factor FKHRL1 and ASK (apoptosis signal-regulating kinase), and led to the activation of caspases 8 and 9. Altogether, our data demonstrate that the SARS-CoV M-protein induces apoptosis through disrupting the interaction of PDK1 with PKB/Akt, and this causes the activation of apoptosis. Our work highlights that the SARS-CoV M protein is highly pro-apoptotic and is capable of simultaneously inducing apoptosis via initiating caspases 8 and 9. Preventing the interaction between M-protein and PDK1 is a plausible therapeutic approach to target the pro-apoptotic property of SARS-CoV.

Differences in asthma genetics between Chinese and other populations.

Asthma is caused by complex gene-gene and gene-environment interactions. Most asthma genes are not replicable across populations, which is possibly because of differences in the epidemiology of these genes. Our case-control association and next-generation sequencing studies revealed substantial discrepancies in the frequencies of single nucleotide polymorphisms (SNPs) and haplotype blocks for asthma genes between Chinese and other populations. The minor allele frequencies for nearly half of our studied SNPs differed by 0.2 or greater between southern Chinese subjects in Hong Kong and European white populations, African populations, or both. Because genome-wide association studies for asthma have not been performed in Chinese subjects, we cannot tell whether the genomic findings of recent consortium-based genome-wide association studies are applicable to our population. In addition, our group performed Roche 454 pyrosequencing on a 100-kb area spanning each of 10 asthma loci in 24 healthy Hong Kong children. For the 17q21 locus, there was substantial variation in the haplotype structures that were constructed from 224 common SNPs among Hong Kong subjects and 6 ethnic groups under the 1000 Genomes Project. Sixteen mostly small haplotype blocks were formed in Hong Kong, whereas 6 haplotype blocks were identified in Han Chinese in Beijing and central European subjects and 11 and 19 blocks were identified in Puerto Rican and Yoruba African subjects. In conclusion, differences in allele frequencies of asthma genes and haplotype structures of asthma loci are found between Chinese subjects and other ethnic groups. These sequence variations must be considered during the selection of tagging SNPs for replicating genetic associations between populations.

Plus ça change - evolutionary sequence divergence predicts protein subcellular localization signals.

BACKGROUND: Protein subcellular localization is a central problem in understanding cell biology and has been the focus of intense research. In order to predict localization from amino acid sequence a myriad of features have been tried: including amino acid composition, sequence similarity, the presence of certain motifs or domains, and many others. Surprisingly, sequence conservation of sorting motifs has not yet been employed, despite its extensive use for tasks such as the prediction of transcription factor binding sites. RESULTS: Here, we flip the problem around, and present a proof of concept for the idea that the lack of sequence conservation can be a novel feature for localization prediction. We show that for yeast, mammal and plant datasets, evolutionary sequence divergence alone has significant power to identify sequences with N-terminal sorting sequences. Moreover sequence divergence is nearly as effective when computed on automatically defined ortholog sets as on hand curated ones. Unfortunately, sequence divergence did not necessarily increase classification performance when combined with some traditional sequence features such as amino acid composition. However a post-hoc analysis of the proteins in which sequence divergence changes the prediction yielded some proteins with atypical (i.e. not MPP-cleaved) matrix targeting signals as well as a few misannotations. CONCLUSION: We report the results of the first quantitative study of the effectiveness of evolutionary sequence divergence as a feature for protein subcellular localization prediction. We show that divergence is indeed useful for prediction, but it is not trivial to improve overall accuracy simply by adding this feature to classical sequence features. Nevertheless we argue that sequence divergence is a promising feature and show anecdotal examples in which it succeeds where other features fail.

[Oseltamivir resistance among pandemic A(H1N1)2009 viruses between 2009 and 2011 in Guangzhou].

OBJECTIVE: To study the prevalence of oseltamivir-resistance among pandemic A (H1N1)2009 viruses isolated from patients in Guangzhou between 2009 and 2011, and to provide more information for clinical usage of oseltamivir. METHODS: Totally 192 pandemic A (H1N1)2009 viruses isolated from patients in Guangzhou between July 2009 and April 2011 were studies. The HA and NA genes of all strains were sequenced to reveal the evolution of viruses, and the susceptibility of viruses to oseltamivir was tested in vitro. RESULTS: One strain with a S247N mutation of the NA gene, which would make the virus resistant to oseltamivir, was found. The susceptibility (IC)50 of this viral strain to oseltamivir was 0.45 nmol/L, 2.5 times lower as compared to the wild-type strains. Phylogenetic analysis showed that this virus was not prevalent in Guangzhou from 2009-2011, and was not located in the same branch with the strains being epidemic in Australia and Singapore during the early seasons of 2011. CONCLUSION: The resistance rate of pandemic A(H1N1)2009 viruses isolated from Guangzhou to oseltamivir was low, but surveillance on resistant strains needs to be strengthened to control resistant viruses imported from abroad.

Activity of the MEK inhibitor selumetinib (AZD6244; ARRY-142886) in nasopharyngeal cancer cell lines.

This study evaluated the preclinical activity of selumetinib (AZD6244, ARRY-142866), an inhibitor of the mitogen-activated protein kinase kinase (MAPKK or MEK1/2) in 6 nasopharyngeal cancer (NPC) cell lines. Selumetinib could achieve up to 90 % inhibition of cell growth with the respective IC(50) values in NPC cell lines as follow: HK1 = 0.04 µM, HK1-LMP1(B95.8) = 0.17 µM, HONE-1-EBV = 0.46 µM, HONE-1 = 1.79 µM, CNE-2 = 2.20 µM and C666-1 > 10 µM. The drug-sensitive cell lines HK1, HK1-LMP1(B95.8) and HONE-1-EBV have higher basal expression of phosphorylated (pi)-MAPK than the less sensitive cell lines. BRAF mutations were not detected in all 6 cell lines. Re-introduction of the EBV genome into HONE-1 cells, generating the HONE-1-EBV cell line, seemed to result in elevated expression of pi-MAPK and sensitivity to selumetinib when compared with the parental HONE-1 cells. At a concentration of 0.5 µM and 5 µM, selumetinib induced apoptosis (as indicated by cleaved PARP expression and caspase 3 induction), and G(0)/G(1) cycle arrest in HONE-1-EBV and HK1-LMP1(B95.8) cells. The combination of selumetinib (at IC(25) concentration) and the EGFR tyrosine kinase inhibitor, gefitinib (at concentrations of 0.1, 3 and 9 µM) resulted in synergistic growth inhibition in HK1-LMP1(B95.8) cells. The combination of selumetinib (at IC(25) concentration) and cisplatin (at concentrations of 0.1, 0.4, 0.8 and 2 µM) resulted in synergistic growth inhibition in HONE-1 and HONE-1-EBV cells. This result suggests that selumetinib alone or in combination with gefitinib or cisplatin maybe a promising strategy against NPC. Further studies are warranted.

Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis.

BACKGROUND: Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD). METHODS: In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset. RESULTS: Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models. CONCLUSIONS: Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD.

Identification of a Common Variant for Coronary Heart Disease at PDE1A Contributes to Individualized Treatment Goals and Risk Stratification of Cardiovascular Complications in Chinese Patients With Type 2 Diabetes.

OBJECTIVE: In this study we aim to unravel genetic determinants of coronary heart disease (CHD) in type 2 diabetes (T2D) and explore their applications. RESEARCH DESIGN AND METHODS: We performed a two-stage genome-wide association study for CHD in Chinese patients with T2D (3,596 case and 8,898 control subjects), followed by replications in European patients with T2D (764 case and 4,276 control subjects) and general populations (n = 51,442-547,261). Each identified variant was examined for its association with a wide range of phenotypes and its interactions with glycemic, blood pressure (BP), and lipid controls in incident cardiovascular diseases. RESULTS: We identified a novel variant (rs10171703) for CHD (odds ratio 1.21 [95% CI 1.13-1.30]; P = 2.4 × 10-8) and BP (ß ± SE 0.130 ± 0.017; P = 4.1 × 10-14) at PDE1A in Chinese T2D patients but found only a modest association with CHD in general populations. This variant modulated the effects of BP goal attainment (130/80 mmHg) on CHD (Pinteraction = 0.0155) and myocardial infarction (MI) (Pinteraction = 5.1 × 10-4). Patients with CC genotype of rs10171703 had >40% reduction in either cardiovascular events in response to BP control (2.9 × 10-8 < P < 3.6 × 10-5), those with CT genotype had no difference (0.0726 < P < 0.2614), and those with TT genotype had a threefold increase in MI risk (P = 6.7 × 10-3). CONCLUSIONS: We discovered a novel CHD- and BP-related variant at PDE1A that interacted with BP goal attainment with divergent effects on CHD risk in Chinese patients with T2D. Incorporating this information may facilitate individualized treatment strategies for precision care in diabetes, only when our findings are validated.

Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs.

BACKGROUND: Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed. RESULTS: In this paper, we constructed an integrated M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework. CONCLUSIONS: A protein interaction map of M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.