Inhibitor of DNA Binding Protein 3 (ID3) and Nuclear Respiratory Factor 1 (NRF1) Mediated Transcriptional Gene Signatures are Associated with the Severity of Cerebral Amyloid Angiopathy.

Cerebral amyloid angiopathy (CAA) is a degenerative vasculopathy. We have previously shown that transcription regulating proteins- inhibitor of DNA binding protein 3 (ID3) and the nuclear respiratory factor 1 (NRF1) contribute to vascular dysregulation. In this study, we have identified sex specific ID3 and NRF1-mediated gene networks in CAA patients diagnosed with Alzheimer's Disease (AD). High expression of ID3 mRNA coupled with low NRF1 mRNA levels was observed in the temporal cortex of men and women CAA patients. Low NRF1 mRNA expression in the temporal cortex was found in men with severe CAA. High ID3 expression was found in women with the genetic risk factor APOE4. Low NRF1 expression was also associated with APOE4 in women with CAA. Genome wide transcriptional activity of both ID3 and NRF1 paralleled their mRNA expression levels. Sex specific differences in transcriptional gene signatures of both ID3 and NRF1 were observed. These findings were further corroborated by Bayesian machine learning and the GeNIe simulation models. Dynamic machine learning using a Monte Carlo Markov Chain (MCMC) gene ordering approach revealed that ID3 was associated with disease severity in women. NRF1 was associated with CAA and severity of this disease in men. These findings suggest that aberrant ID3 and NRF1 activity presumably plays a major role in the pathogenesis and severity of CAA. Further analyses of ID3- and NRF1-regulated molecular drivers of CAA may provide new targets for personalized medicine and/or prevention strategies against CAA.

Probabilistic graphical modelling using Bayesian networks for predicting clinical outcome after posterior decompression in patients with degenerative cervical myelopathy.

BACKGROUND: Probabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model. OBJECTIVE: To develop PGM for the prediction of clinical outcome in patients with degenerative cervical myelopathy (DCM) after posterior decompression and to use PGM to identify causal predictors of the outcome. METHODS: We included data from 59 patients who had undergone cervical posterior decompression for DCM. The candidate predictive parameters were age, sex, body mass index, trauma history, symptom duration, preoperative and last Japanese Orthopaedic Association (JOA) scores, gait impairment, claudication, bladder dysfunction, Nurick grade, American Spinal Injury Association (ASIA) grade, smoking, diabetes mellitus, cardiopulmonary disorders, hypertension, stroke, Parkinson's disease, dementia, psychiatric disorders, arthritis, ossification of the posterior longitudinal ligament, cord signal change, postoperative kyphosis and the cord compression ratio. RESULTS: In regression analyses, preoperative JOA (PreJOA) score, presence of a psychiatric disorder, and ASIA grade were identified as significant factors associated with the last JOS score. Dementia, sex, PreJOA score and gait impairment were causal factors in the PGM. Sex, dementia and PreJOA score were direct causal factors related to the last follow-up JOA (LastJOA) score. Being female, having dementia, and having a low PreJOA score were significantly related to having a low LastJOA score. CONCLUSIONS: The causal predictors of surgical outcome for DCM were sex, dementia and PreJOA score. Therefore, PGM may be a useful personalized medicine tool for predicting the outcome of patients with DCM.

Sex, dementia and preoperative neurological status are causal factors contributing to the postoperative outcome of patients with degenerative cervical myelopathy.The Bayesian network (BN) structure may be useful for predicting the probability for clinical outcomes for each patient who undergoes posterior decompressive surgery.The BN structure may provide a useful model in the current era of personalized medicine.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer.

Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein-protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60-80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.

Polychlorinated Biphenyls and Pulmonary Hypertension.

Polychlorinated biphenyls (PCBs) are persistent environmental pollutants that were banned because of their potential carcinogenicity. Population studies have shown that PCBs are associated with lung toxicity and hypertension. The objective of this study was to evaluate whether higher exposure to PCB congeners is associated with the risk of pulmonary hypertension. Serum levels of PCBs in 284 subjects with combined risk factors for pulmonary arterial hypertension (PAH) were compared to 4210 subjects with no risk for PAH using the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004. The major findings from this study include significantly higher PCB levels in PAH subjects compared to non-PAH subjects; for example, the geometric mean (GM) of PCB74 was 15.91 (ng/g) (14.45-17.53) vs. 11.48 (ng/g) (10.84-12.16), respectively. Serum levels of PCB congeners showed an increasing trend in the age group 20-59 years as PCB180 GM was 19.45 (ng/g) in PAH vs. 12.75 (ng/g) in the control. A higher body burden of PCB153 followed by PCB138, PCB180, and PCB118 was observed. Estimated age, race, BMI, and gender-adjusted ORs for PCB congener levels in subjects with the combined risk factors for PAH compared to controls was significant; for example, PCB99 (OR: 1.5 (CI: 1.49-1.50). In summary, these findings indicate that exposure, as well as body burden estimated based on lipid adjustment of PCBs, were higher in people with risk factors for PAH, and PCB congeners accumulated with age. These findings should be interpreted with caution because of the use of cross-sectional self-reported data and a small sample size of subjects with combined risk factors for pulmonary arterial hypertension. Nonetheless, our finding emphasizes a need for a comprehensive environmental molecular epidemiologic study to determine the potential role of environmental exposures to PCBs in the development of pulmonary arterial hypertension.

Nuclear respiratory factor 1 transcriptomic signatures as prognostic indicators of recurring aggressive mesenchymal glioblastoma and resistance to therapy in White American females.

PURPOSE: The mechanisms contributing to recurrence of glioblastoma (GBM), an aggressive neuroepithelial brain tumor, remain unknown. We have recently shown that nuclear respiratory factor 1 (NRF1) is an oncogenic transcription factor and its transcriptional activity is associated with the progression and prognosis of GBM. Herein, we extend our efforts to (1) identify influential NRF1-driven gene and microRNA (miRNA) expression for the aggressiveness of mesenchymal GBM; and (2) understand the molecular basis for its poor response to therapy. METHODS: Clinical data and RNA-Seq from four independent GBM cohorts were analyzed by Bayesian Network Inference with Java Objects (BANJO) and Markov chain Monte Carlo (MCMC)-based gene order to identify molecular drivers of mesenchymal GBM as well as prognostic indicators of poor response to radiation and chemotherapy. RESULTS: We are the first to report sex-specific NRF1 motif enriched gene signatures showing increased susceptibility to GBM. Risk estimates for GBM were increased by greater than 100-fold with the joint effect of NRF1-driven gene signatures-CDK4, DUSP6, MSH2, NRF1, and PARK7 in female GBM patients and CDK4, CASP2, H6PD, and NRF1 in male GBM patients. NRF1-driven causal Bayesian network genes were predictive of poor survival and resistance to chemoradiation in IDH1 wild-type mesenchymal GBM patients. NRF1-regulatable miRNAs were also associated with poor response to chemoradiation therapy in female IDH1 wild-type mesenchymal GBM. Stable overexpression of NRF1 reprogramed human astrocytes into neural stem cell-like cells expressing SOX2 and nestin. These cells differentiated into neurons and form tumorospheroids. CONCLUSIONS: In summary, our novel discovery shows that NRF1-driven causal genes and miRNAs involved in cancer cell stemness and mesenchymal features contribute to cancer aggressiveness and recurrence of aggressive therapy-resistant glioblastoma.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer.

Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005−2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.

Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer.

Using a machine learning method, this study aimed to identify unique causal networks of genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network analysis identified differentially expressed genes in primary breast cancer tissues, in bone, brain, and lung breast cancer metastatic tissues, and the clinicopathological features of patients obtained from the Gene Expression Omnibus microarray datasets. We evaluated the causal Bayesian networks of breast metastasis to distant sites (bone, brain, or lung) by (i) measuring how well the structures of each specific type of breast cancer metastasis fit the data, (ii) comparing the structures with known experimental evidence, and (iii) reporting predictive capabilities of the structures. We report for the first time that the molecular gene signatures are specific to the different types of breast cancer metastasis. Several genes, including CHPF, ARC, ANGPTL4, NR2E1, SH2D1A, CTSW, POLR2J4, SPTLC1, ILK, ALDH3B1, PDE6A, SCTR, ADM, HEY1, KCNF1, and UVRAG, were found to be predictors of the risk for site-specific metastasis of breast cancer. Expression of POLR2JA, SPTLC1, ILK, ALDH3B1, and the estrogen receptor was significantly associated with breast cancer bone metastasis. Expression of PDE6A and NR2E1 was causally linked to breast cancer brain metastasis. Expression of HEY1, KCNF1, UVRAG, and the estrogen and progesterone receptors was strongly associated with breast cancer lung metastasis. The causal Bayesian network structures of these genes identify potential interactions among the genes in distant metastases of breast cancer, including to the bone, brain, and lung, and may serve as target candidates for treatment of breast cancer metastasis.

Sensitivity to differential NRF1 gene signatures contributes to breast cancer disparities.

PURPOSE: Nuclear respiratory factor 1 (NRF1) drives estrogen-dependent breast tumorigenesis. Herein we examined the impact of NRF1 activity on the aggressiveness and disparate molecular signature of breast cancer in Black, White, Asian, and Hispanic women. METHODS: NRF1 activity by transcription factor target enrichment analysis and causal NRF1-target gene signatures by Bayesian Network Inference with Java Objects (BANJO) and Markov Chain Monte Carlo (MCMC)-based gene order were examined in The Cancer Genome Atlas (TCGA) breast cancer cohorts. RESULTS: We are the first to report increased NRF1 activity based on its differential effects on genome-wide transcription associated with luminal A and B, HER2+ and triple-negative (TN) molecular subtypes of breast cancer in women of different race/ethnicity. We observed disparate NRF1 motif-containing causal gene signatures unique to Black, White, Asian, and Hispanic women for luminal A breast cancer. Further gene order searches showed molecular heterogeneity of each subtype of breast cancer. Six different gene order sequences involving CDK1, HMMR, CCNB2, CCNB1, E2F1, CREB3L4, GTSE1, and LMNB1 with almost equal weight predicted the probability of luminal A breast cancer in whites. Three different gene order sequences consisting of CCNB1 and GTSE1, and CCNB1, LMNB1, CDK1 or CASP3 predicted almost 100% probability of luminal B breast cancer in whites; CCNB1 and LMNB1 or GTSE predicted 100% HER2+ breast cancer in whites. GTSE1 and TUBA1C combined together predicted 100% probability of developing TNBC in whites; NRF1, TUBA1B and BAX with EFNA4, and NRF1 and BTRC predicated 100% TNBC in blacks. High expressor NRF1 TN breast tumors showed unfavorable prognosis with a high risk of breast cancer death in white women. CONCLUSION: Our findings showed how sensitivity to high NRF1 transcriptional activity coupled with its target gene signatures contribute to racial differences in luminal A and TN breast cancer subtypes. This knowledge may be useful in personalized intervention to prevent and treat this clinically challenging problem.

Nuclear Respiratory Factor 1 (NRF1) Transcriptional Activity-Driven Gene Signature Association with Severity of Astrocytoma and Poor Prognosis of Glioblastoma.

Despite tremendous progress in understanding the pathobiology of astrocytoma, major gaps remain in our knowledge of the molecular basis underlying the aggressiveness of high-grade astrocytoma (glioblastoma - GBM). Recently, we and others have shown nuclear respiratory factor 1 (NRF1) transcription factor being highly active in human cancers, but its role in astrocytoma remains unknown. Therefore, the purpose of this study was to uncover the role of NRF1 in the progression of GBM. NRF1 has higher mRNA expression and transcription factor activity in astrocytoma compared to non-tumor brain tissue. NRF1 activity also correlated with the aggressiveness of cancer. Increased NRF1 TF activity coupled with overexpression of RHOG was associated with poor survival of GBM patients. NRF1 activity was associated with transcriptomic signatures of neurogenesis, cell stemness, epithelial-mesenchymal transition and cell cycle progression. Overexpression of CDK4, AKT1, APAF1, HDAC1, NBN, TGFB1, & TNFRSF1A and downregulation of CASP3, IL7, STXBP1 and OPA1 predicted GBM malignancy in high expressors of NRF1 activity. Increased expression of the NRF1 motif containing genes, H6PD, NAT10, NBEAL2, and RNF19B predicted poor survival of IDH1 wild-type GBM patients. Poor survival outcomes and resistance to Temozolomide therapy were associated with higher NRF1 expression including its targets - LDHA, ZMAT3, NSUN2, ARMC5, NDEL1, CLPTM1L, ALKBH5, YIPF5, PPP2CA, and TFG. These findings suggest that aberrant NRF1 activity may contribute to the pathogenesis of GBM and severity of astrocytoma. Further analyses of NRF1 gene signatures will pave the way for next generation targeted therapies and drug combination strategies for GBM patients.

Erratum to: Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks.

[This corrects the article on p. 251 in vol. 25, PMID: 30574470.].

Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks.

BACKGROUND: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. METHODS: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. RESULTS: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. CONCLUSIONS: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

NRF1 motif sequence-enriched genes involved in ER/PR -ve HER2 +ve breast cancer signaling pathways.

Nuclear respiratory factor 1 (NRF1) transcription factor has recently been shown to control breast cancer progression. However, mechanistic aspects by which NRF1 may contribute to susceptibility to different breast tumor subtypes are still not fully understood. Since transcriptional control of NRF1 seems to be dependent on epidermal growth factor receptor signaling, herein, we investigated the role of NRF1 in estrogen receptor/progesterone receptor negative, but human epidermal growth factor receptor 2-positive (ER/PR -ve HER2 +ve) breast cancer. We found that both mRNA and protein levels of NRF1 and its transcriptional activity were significantly higher in ER/PR -ve HER2 +ve breast cancer samples compared to normal breast tissues. This was consistent with our observation of higher NRF1 protein expression in the experimental model of HER2+ breast cancer brain metastasis. To identify network-based pathways involved in the susceptibility to the ER/PR -ve HER2 +ve breast cancer subtype, the NRF1 transcriptional regulatory genome-wide landscape was analyzed using the approach consisting of a systematic integration of ChIP DNA-seq, RNA-Microarray, NRF1 protein-DNA motif binding, signal pathway analysis, and Bayesian machine learning. Our findings show that a high percentage of known HER2+ breast cancer susceptibility genes, including EGFR, IGFR, and E2F1, are under transcriptional control of NRF1. Promoters of several genes from the KEGG HER2+ breast cancer pathway and 11 signaling pathways linked to 6 hallmarks of cancer contain the NRF1 motif. By pathway analysis, key breast cancer hallmark genes of epithelial-mesenchymal transition, stemness, cell apoptosis, cell cycle regulation, chromosomal integrity, and DNA damage/repair were highly enriched with NRF1 motifs. In addition, we found using Bayesian network-based machine learning that 30 NRF1 motif-enriched genes including growth factor receptors-FGFR1, IGF1R; E2Fs transcription factor family-E2F1, E2F3; MAPK pathway-SHC2, GRB2, MAPK1; PI3K-AKT-mTOR signaling pathway-PIK3CD, PIK3R1, PIK3R3, RPS6KB2; WNT signaling pathway-WNT7B, DLV1, DLV2, GSK3B, NRF1, and DDB2, known for its role in DNA repair and involvement in early events associated with metastatic progression of breast cancer cells, were associated with HER2-amplified breast cancer. Machine learning search further revealed that the likelihood of HER2-positive breast cancer is almost 100% in a patient with the high NRF1 expression combined with expression patterns of high E2F3, GSK3B, and MAPK1, low or no change in E2F1 and FGFR1, and high or no change in PIK3R3. In summary, our findings suggest novel roles of NRF1 and its regulatory networks in susceptibility to the ER/PR -ve HER2 +ve aggressive breast cancer subtype. Clinical confirmation of our machine learned Bayesian networks will have significant impact on our understanding of the role of NRF1 as a valuable biomarker for breast cancer diagnosis and prognosis as well as provide strong rationale for future studies to develop NRF1 signaling-based therapeutics to target HER2+ breast cancer.

Effect of zinc intake on hepatic autophagy during acute alcohol intoxication.

Autophagy is a conserved mechanism that plays a housekeeping role by eliminating protein aggregates and damaged organelles. Recent studies have demonstrated that acute ethanol intoxication induces hepatic autophagy in mice. The effect of dietary zinc intake on hepatic autophagic flux during ethanol intoxication has not been evaluated using animal models. Herein, we investigated whether zinc deficiency and excess can affect autophagic flux in the liver in mice and in human hepatoma cells acutely exposed to ethanol. A mouse model of binge ethanol feeding was utilized to analyze the effect of low, adequate, and high zinc intake on hepatic autophagic flux during ethanol intoxication. Autophagic flux was inferred by analyzing LC3II/LC3I ratio, protein levels of p62/SQSTM1, Beclin1 and Atg7, and phosphorylation of 4EBP1. In addition, the degradation of the fusion protein LC3-GFP and the formation of autophagosomes and autolysosomes were evaluated in cells. Ethanol treatment stimulated autophagy in mice and cells. High zinc intake resulted in enhanced autophagy in mice exposed to ethanol. Conversely, zinc deficiency was consistently associated with impaired ethanol-induced autophagy in mice and cells. Zinc-deficient mice exhibited a high degree of ethanol-driven steatosis. Furthermore, zinc depletion increased apoptosis in cells exposed to ethanol. The results of this study suggest that adequate zinc intake is necessary for proper stimulation of autophagy by ethanol. Poor zinc status is commonly found among alcoholics and could likely contribute to faulty autophagy.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part I, Development of the Formula and its Internal Validation.

PURPOSE: As the elderly population increases, a growing number of patients have lower urinary tract symptom (LUTS)/benign prostatic hyperplasia (BPH). The aim of this study was to develop decision support formulas and nomograms for the prediction of bladder outlet obstruction (BOO) and for BOO-related surgical decision-making, and to validate them in patients with LUTS/BPH. METHODS: Patient with LUTS/BPH between October 2004 and May 2014 were enrolled as a development cohort. The available variables included age, International Prostate Symptom Score, free uroflowmetry, postvoid residual volume, total prostate volume, and the results of a pressure-flow study. A causal Bayesian network analysis was used to identify relevant parameters. Using multivariate logistic regression analysis, formulas were developed to calculate the probabilities of having BOO and requiring prostatic surgery. Patients between June 2014 and December 2015 were prospectively enrolled for internal validation. Receiver operating characteristic curve analysis, calibration plots, and decision curve analysis were performed. RESULTS: A total of 1,179 male patients with LUTS/BPH, with a mean age of 66.1 years, were included as a development cohort. Another 253 patients were enrolled as an internal validation cohort. Using multivariate logistic regression analysis, 2 and 4 formulas were established to estimate the probabilities of having BOO and requiring prostatic surgery, respectively. Our analysis of the predictive accuracy of the model revealed area under the curve values of 0.82 for BOO and 0.87 for prostatic surgery. The sensitivity and specificity were 53.6% and 87.0% for BOO, and 91.6% and 50.0% for prostatic surgery, respectively. The calibration plot indicated that these prediction models showed a good correspondence. In addition, the decision curve analysis showed a high net benefit across the entire spectrum of probability thresholds. CONCLUSIONS: We established nomograms for the prediction of BOO and BOO-related prostatic surgery in patients with LUTS/BPH. Internal validation of the nomograms demonstrated that they predicted both having BOO and requiring prostatic surgery very well.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part II, External Validation and Usability Testing of a Smartphone App.

PURPOSE: We aimed to externally validate the prediction model we developed for having bladder outlet obstruction (BOO) and requiring prostatic surgery using 2 independent data sets from tertiary referral centers, and also aimed to validate a mobile app for using this model through usability testing. METHODS: Formulas and nomograms predicting whether a subject has BOO and needs prostatic surgery were validated with an external validation cohort from Seoul National University Bundang Hospital and Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January 2004 and April 2015. A smartphone-based app was developed, and 8 young urologists were enrolled for usability testing to identify any human factor issues of the app. RESULTS: A total of 642 patients were included in the external validation cohort. No significant differences were found in the baseline characteristics of major parameters between the original (n=1,179) and the external validation cohort, except for the maximal flow rate. Predictions of requiring prostatic surgery in the validation cohort showed a sensitivity of 80.6%, a specificity of 73.2%, a positive predictive value of 49.7%, and a negative predictive value of 92.0%, and area under receiver operating curve of 0.84. The calibration plot indicated that the predictions have good correspondence. The decision curve showed also a high net benefit. Similar evaluation results using the external validation cohort were seen in the predictions of having BOO. Overall results of the usability test demonstrated that the app was user-friendly with no major human factor issues. CONCLUSIONS: External validation of these newly developed a prediction model demonstrated a moderate level of discrimination, adequate calibration, and high net benefit gains for predicting both having BOO and requiring prostatic surgery. Also a smartphone app implementing the prediction model was user-friendly with no major human factor issue.

Estrogenic Endocrine Disrupting Chemicals Influencing NRF1 Regulated Gene Networks in the Development of Complex Human Brain Diseases.

During the development of an individual from a single cell to prenatal stages to adolescence to adulthood and through the complete life span, humans are exposed to countless environmental and stochastic factors, including estrogenic endocrine disrupting chemicals. Brain cells and neural circuits are likely to be influenced by estrogenic endocrine disruptors (EEDs) because they strongly dependent on estrogens. In this review, we discuss both environmental, epidemiological, and experimental evidence on brain health with exposure to oral contraceptives, hormonal therapy, and EEDs such as bisphenol-A (BPA), polychlorinated biphenyls (PCBs), phthalates, and metalloestrogens, such as, arsenic, cadmium, and manganese. Also we discuss the brain health effects associated from exposure to EEDs including the promotion of neurodegeneration, protection against neurodegeneration, and involvement in various neurological deficits; changes in rearing behavior, locomotion, anxiety, learning difficulties, memory issues, and neuronal abnormalities. The effects of EEDs on the brain are varied during the entire life span and far-reaching with many different mechanisms. To understand endocrine disrupting chemicals mechanisms, we use bioinformatics, molecular, and epidemiologic approaches. Through those approaches, we learn how the effects of EEDs on the brain go beyond known mechanism to disrupt the circulatory and neural estrogen function and estrogen-mediated signaling. Effects on EEDs-modified estrogen and nuclear respiratory factor 1 (NRF1) signaling genes with exposure to natural estrogen, pharmacological estrogen-ethinyl estradiol, PCBs, phthalates, BPA, and metalloestrogens are presented here. Bioinformatics analysis of gene-EEDs interactions and brain disease associations identified hundreds of genes that were altered by exposure to estrogen, phthalate, PCBs, BPA or metalloestrogens. Many genes modified by EEDs are common targets of both 17 ß-estradiol (E2) and NRF1. Some of these genes are involved with brain diseases, such as Alzheimer's Disease (AD), Parkinson's Disease, Huntington's Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorder, and Brain Neoplasms. For example, the search of enriched pathways showed that top ten E2 interacting genes in AD-APOE, APP, ATP5A1, CALM1, CASP3, GSK3B, IL1B, MAPT, PSEN2 and TNF-underlie the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) AD pathway. With AD, the six E2-responsive genes are NRF1 target genes: APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1. These genes are also responsive to the following EEDs: ethinyl estradiol (APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1), BPA (APBB2, EIF2S1, ENO1, MAPT, and PAXIP1), dibutyl phthalate (DPYSL2, EIF2S1, and ENO1), diethylhexyl phthalate (DPYSL2 and MAPT). To validate findings from Comparative Toxicogenomics Database (CTD) curated data, we used Bayesian network (BN) analysis on microarray data of AD patients. We observed that both gender and NRF1 were associated with AD. The female NRF1 gene network is completely different from male human AD patients. AD-associated NRF1 target genes-APLP1, APP, GRIN1, GRIN2B, MAPT, PSEN2, PEN2, and IDE-are also regulated by E2. NRF1 regulates targets genes with diverse functions, including cell growth, apoptosis/autophagy, mitochondrial biogenesis, genomic instability, neurogenesis, neuroplasticity, synaptogenesis, and senescence. By activating or repressing the genes involved in cell proliferation, growth suppression, DNA damage/repair, apoptosis/autophagy, angiogenesis, estrogen signaling, neurogenesis, synaptogenesis, and senescence, and inducing a wide range of DNA damage, genomic instability and DNA methylation and transcriptional repression, NRF1 may act as a major regulator of EEDs-induced brain health deficits. In summary, estrogenic endocrine disrupting chemicals-modified genes in brain health deficits are part of both estrogen and NRF1 signaling pathways. Our findings suggest that in addition to estrogen signaling, EEDs influencing NRF1 regulated communities of genes across genomic and epigenomic multiple networks may contribute in the development of complex chronic human brain health disorders.

Integrated Bioinformatics, Environmental Epidemiologic and Genomic Approaches to Identify Environmental and Molecular Links between Endometriosis and Breast Cancer.

We present a combined environmental epidemiologic, genomic, and bioinformatics approach to identify: exposure of environmental chemicals with estrogenic activity; epidemiologic association between endocrine disrupting chemical (EDC) and health effects, such as, breast cancer or endometriosis; and gene-EDC interactions and disease associations. Human exposure measurement and modeling confirmed estrogenic activity of three selected class of environmental chemicals, polychlorinated biphenyls (PCBs), bisphenols (BPs), and phthalates. Meta-analysis showed that PCBs exposure, not Bisphenol A (BPA) and phthalates, increased the summary odds ratio for breast cancer and endometriosis. Bioinformatics analysis of gene-EDC interactions and disease associations identified several hundred genes that were altered by exposure to PCBs, phthalate or BPA. EDCs-modified genes in breast neoplasms and endometriosis are part of steroid hormone signaling and inflammation pathways. All three EDCs-PCB 153, phthalates, and BPA influenced five common genes-CYP19A1, EGFR, ESR2, FOS, and IGF1-in breast cancer as well as in endometriosis. These genes are environmentally and estrogen responsive, altered in human breast and uterine tumors and endometriosis lesions, and part of Mitogen Activated Protein Kinase (MAPK) signaling pathways in cancer. Our findings suggest that breast cancer and endometriosis share some common environmental and molecular risk factors.

The Analgesic Effect of Rolipram, a Phosphodiesterase 4 Inhibitor, on Chemotherapy-Induced Neuropathic Pain in Rats.

BACKGROUND: Chemotherapy-induced neuropathic pain is a significant side effect of chemotherapeutic agents. Currently, there are no effective analgesics for chemotherapy-induced neuropathic pain. Rolipram is a selective phosphodiesterase 4 inhibitor, which increases intracellular cyclic AMP in nerve and immune cells. The aim of our study was to determine the analgesic effects of rolipram on paclitaxel (PAC)-induced neuropathic pain in rats. METHODS: Chemotherapy-induced neuropathic pain was produced by intraperitoneal injection of PAC on 4 alternate days in male Sprague-Dawley rats. Mechanical allodynia was measured by using von Frey filaments. RESULTS: After the rats developed PAC-induced pain behavior (such as mechanical allodynia), a single intraperitoneal injection and continuous infusion of rolipram ameliorated PAC-induced pain behavior. In addition, systemic infusion of the drug during the early phase of developing pain behavior did not prevent the development of mechanical allodynia induced by PAC. CONCLUSIONS: These results suggest that rolipram alleviated mechanical allodynia induced by PAC in rats. Thus, phosphodiesterase 4 inhibitors may prove useful in the treatment of chemotherapy-induced neuropathic pain. However, further studies are needed to clarify their effects in clinical settings.

Health and Functioning in Grandparents After a Young Grandchild's Death.

This cross-sectional study examined the physical and mental health, grief and role functioning of 136 grandparents in the first year after death of their young grandchild (newborn through 6 years). Grandparents were 36-77 years old; 73% female; 24% Hispanic, 38% Black/African American, and 38% White. Mean age of the 115 deceased grandchildren was 12.8 months (SD = 20.71) with 37% <1 month old; 65% were male, 77% died in the hospital. Grandparents were recruited through state death records and interviewed by telephone. Grandparents experienced: clinical depression (31%), PTSD (35%); illnesses (28%), hospitalizations, new chronic health conditions (mental disorders, hypertension, angina, cancer), and medication changes. Grandparents who provided care for the deceased grandchild had more intense symptoms of grief, depression and PTSD and more trouble focusing at their jobs. Severity of depressive and/or PTSD symptoms were more likely to be at clinically important levels for grandparents who had provided childcare for the deceased grandchild than for non-caregiving grandparents. Black grandparents had more severe symptoms of PTSD and thought more about their deceased grandchild on the job than White grandparents. The interaction effect of race/ethnicity and provision of child care was significant for PTSD and Blame and Anger. Hispanic grandparents who provided some child care for their deceased grandchild had less severe PTSD symptoms than caregiving Black and White grandparents. Caregiving Hispanic grandparents also experienced less Blame and Anger than White caregiving grandparents.

Proteomic and Mitochondrial Genomic Analyses of Pediatric Brain Tumors.

The molecular mechanism unraveling why a particular type of pediatric brain tumor (pBT) behaves so differently from child to child or genetic/epigenetic changes in the mitochondrial genome vary from tumor to tumor is not clearly understood. Despite the identification of mitochondrial DNA (mtDNA) mutations in different types of pBT, the contribution of mitochondrial dysfunction-related genes or proteins that are selectively up- or down-regulated in pBT of different types has not been comprehensively examined. In the present study, we combined a 2D DIGE approach with protein identification using MALDI-TOF MS and LC-MS/MS, coupled with mtDNA genomics to screen brain samples for discovering changes in protein expression, and mtDNA sequence variation and mtDNA copy number in the disease states. Two-dimensional gel electrophoresis-based differential proteomic analysis of the brain tumors showed that 116 proteins were found to be up- or down-regulated in brain tumors. Some of the proteins up-regulated in tumors compared to controls were dihydropyrimidinase-like 2; glial fibrillary acidic protein isoform 2; phosphoserine aminotransferase isoform 1; Sirt2 histone deacetylase; and C10orf2 protein, mitochondrial DNA helicase. Proteins down-regulated in brain tumors compared to controls were heat shock protein 90 kDa beta, BiP; guanine nucleotide binding protein (G protein), beta polypeptide 2-like 1, isoform CRA_d; histone H2B.1; neurofilament, light polypeptide 68 kDa; Annexin I; and RAN. These differentially expressed proteins may provide useful information for developing molecular markers of diagnostic or prognostic value. To investigate further the role of mitochondrial dysfunction, we examined the effects of mtDNA copy number, oxidative damage, and mtDNA variants as independent or combined risk factors for the development of pBTs. Bayesian network and mechanistic hierarchical structure Markov Chain Monte Carlo (MCMC) modeling were used to analyze the relationship between these variables. The combined effects of G3196, 9952A, 10006G, 100398G, oxidative mtDNA damages, and mtDNA copy number increased the probability of developing brain tumors in female children by 51 times more when compared to normal incidence of pediatric brain tumors. Comparison of mechanistic structure models also supported the finding that female children who have the wild type allele G3196, variant allele 9952A, variant allele 10006G, variant allele10398A, and high mtDNA copy number had increased probability of developing pediatric brain tumors. Estimation of nuclear genes controlling mitochondrial biogenesis and development of brain, cortical dysplasia, and the effect of the environment using MCMC method showed that these latent variables had a very significant contribution in the development of pediatric brain tumors. Together, these results suggest that mitochondrial genome and tumor proteome are important contributors to brain tumor risk in children, and findings from this study may guide the prospects for targeting mitochondria for therapeutic treatment of childhood brain tumor.