Using Causal Bayesian Networks to Assess the Role of Patient-Centered Care and Psychosocial Factors on Durable HIV Viral Suppression.

We assessed the role of patient-centered care on durable viral suppression (i.e., all viral load test results < 200 copies per ml during 2019) by conducting a retrospective cohort study of clients medically case managed by the Miami-Dade County Ryan White Program (RWP). Summary measures of patient-centered care practices of RWP-affiliated providers were obtained from a survey of 1352 clients. Bayesian network models analyzed the complex relationship between psychosocial and patient-centered care factors. Of 5037 clients, 4135 (82.1%) had durable viral suppression. Household income was the factor most strongly associated with durable viral suppression. Further, mean healthcare relationship score and mean "provider knows patient as a person" score were both associated with durable viral suppression. Healthcare relationship score moderated the association between low household income and lack of durable viral suppression. Although patient-centered care supports patient HIV care success, wrap around support is also needed for people with unmet psychosocial needs.

RESUMEN: Evaluamos el rol de la atención centrada en el paciente en la supresión viral duradera (es decir, todos los resultados de las pruebas de carga viral < 200 copias por ml durante 2019) mediante la realización de un estudio de cohorte retrospectivo de clientes manejados médicamente por el Programa Ryan White del condado de Miami-Dade (RWP). Se obtuvieron medidas resumidas de las prácticas de atención centradas en el paciente de los proveedores afiliados a RWP usando una encuesta de 1352 clientes. Los modelos de redes bayesianos analizaron la relación compleja entre los factores psicosociales y de atención centrada en el paciente. De 5037 clientes, 4135 (82,1%) tenían una supresión viral duradera. Los ingresos del hogar fueron el factor asociado con la supresión viral duradera más fuerte. Además, la puntuación promedia de la relación con proveedores de atención médica y la puntuación promedia de "el proveedor conoce al paciente como persona" fueron asociados con una supresión viral duradera. La puntuación de la relación con proveedores de atención médica moderó la asociación entre los ingresos bajos del hogar y la falta de supresión viral duradera. Aunque la atención centrada en el paciente apoya el éxito de la atención médica del VIH, también se necesita un apoyo integral para las personas con necesidades psicosociales insatisfechas.

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 effects in microbiomes using interventional calculus.

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or "disease" variable, and then computing the causal network, referred to as a "disease network", with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

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.

Black, White, and Hispanic Children's Health and Function 2-13 Months After Sibling Intensive Care Unit Death.

OBJECTIVE: To describe children's anxiety, depression, behaviors, and school performance at 2-13 months after sibling neonatal/pediatric intensive care unit (NICU/PICU) or emergency department (ED) death and compare these outcomes by child age, sex, race/ethnicity, whether the child saw their sibling in the NICU/PICU/ED, and attended the sibling's funeral. STUDY DESIGN: Children in 71 families were recruited for this longitudinal study from 4 children's hospitals and 14 other Florida hospitals. Children rated anxiety (Spence Children's Anxiety Scale) and depression (Children's Depression Inventory); parents rated child behaviors (Child Behavior Checklist) and reported school performance (detentions, suspensions, requested parent-teacher meetings) at 2, 4, 6, and 13 months post-sibling death. Analyses included repeated measures-ANOVA, t-tests, and 1-way ANOVA. RESULTS: In total, 132 children and 96 parents participated. More children were female (58%), black (50%), and school-age (72%). Of the children, 43% had elevated anxiety and 6% had elevated depression over 13 months post-sibling death. Child-rated anxiety was higher for girls and black vs white children. Child-rated anxiety and depression were lower if they saw their sibling in the NICU/PICU/ED before and/or after the death, and/or attended the funeral. Teens were more withdrawn than school-age children at all time points. Children who did not see their deceased sibling in the NICU/PICU/ED after death had more requests for parent-teacher conferences. CONCLUSIONS: Children's anxiety was more common than depression, especially in girls and black children. Children who saw their siblings in the NICU/PICU/ED before/after death and/or attended funeral services had lower anxiety and depression over the first 13 months after sibling death.

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.

Human Relaxin Receptor Is Fully Functional in Humanized Mice and Is Activated by Small Molecule Agonist ML290.

Relaxin, a small peptide hormone of the insulin/relaxin family, demonstrated antifibrotic, organ protective, vasodilatory, and proangiogenic properties in clinical trials and several animal models of human diseases. Relaxin family peptide receptor 1 (RXFP1) is the relaxin cognate G protein-coupled receptor. We have identified a series of small molecule agonists of human RXFP1. The lead compound ML290 demonstrated preferred absorption, distribution, metabolism, and excretion profiles, is easy to synthesize, and has high stability in vivo. However, ML290 does not activate rodent RXFP1s and therefore cannot be tested in common preclinical animal models. Here we describe the production and analysis of a mouse transgenic model, a knock-out/knock-in of the human RXFP1 (hRXFP1) complementary DNA into the mouse Rxfp1 (mRxfp1) gene. Insertion of the vector into the mRxfp1 locus caused disruption of mRxfp1 and expression of hRXFP1. The transcriptional expression pattern of the hRXFP1 allele was similar to mRxfp1. Female mice homozygous for hRXFP1 showed relaxation of the pubic symphysis at parturition and normal development of mammary nipples and vaginal epithelium, indicating full complementation of mRxfp1 gene ablation. Intravenous injection of relaxin led to an increase in heart rate in humanized and wild-type females but not in Rxfp1-deficient mice, whereas ML290 increased heart rate in humanized but not wild-type animals, suggesting specific target engagement by ML290. Moreover, intraperitoneal injection of ML290 caused a decrease in blood osmolality. Taken together, our data show humanized RXFP1 mice can be used for testing relaxin receptor modulators in various preclinical studies.

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.

Parent Grief 1-13 Months After Death in Neonatal and Pediatric Intensive Care Units.

OBJECTIVE: Describe changes in mothers' and fathers' grief from 1 to 13 months after infant/child neonatal/pediatric intensive care unit death and identify factors related to their grief. METHODS: Mothers (n = 130) and fathers (n = 52) of 140 children (newborn-18 years) completed the Hogan Grief Reaction Checklist at 1, 3, 6, and 13 months post-death. RESULTS: Grief decreased from 3 to 13 months for mothers and from 3 to 6 months for fathers. Grief was more intense for: mothers of deceased adolescents and mothers whose child was declared brain dead. CONCLUSION: Mothers' and fathers' grief intensity may not coincide, resulting in different needs during the 13 months after infant/child death.